Title of Invention	A METHOD AND A SYSTEM FOR EVALUATING THE HBA1C LEVEL
Abstract	A method, system, and computer program product related to the maintenance of optimal control of diabetes, and is directed to predicting the long-term exposure to hyperglycemia, and the long-term and short-term risks of severe or moderate hypoglycemia in diabetics, based on blood blucose readings collected by a self-monitoring blood glucose device. The method, system, and computer program product pertain directly to the enhancement of existing home blood glucose monitoring devices by introducing an intelligent data interpretation component capable of predicting both HbAlc and periods of increased risk of hypoglycemia, and to the enhancement of emerging continuous monitoring devices by the same features. With these predictions the diabetic can take steps to prevent the adverse consequences associated with hyperglycemia and hypoglycemia.

Title of Invention

A METHOD AND A SYSTEM FOR EVALUATING THE HBA1C LEVEL

Abstract

A method, system, and computer program product related to the maintenance of optimal control of diabetes, and is directed to predicting the long-term exposure to hyperglycemia, and the long-term and short-term risks of severe or moderate hypoglycemia in diabetics, based on blood blucose readings collected by a self-monitoring blood glucose device. The method, system, and computer program product pertain directly to the enhancement of existing home blood glucose monitoring devices by introducing an intelligent data interpretation component capable of predicting both HbAlc and periods of increased risk of hypoglycemia, and to the enhancement of emerging continuous monitoring devices by the same features. With these predictions the diabetic can take steps to prevent the adverse consequences associated with hyperglycemia and hypoglycemia.

Full Text	The present application is a national stage filing of International Application No. PCT/US2003/025053, filed August 8, 2003, which claims the benefit of priority under 35 U.S.C. Section 119(e) from U.S. Provisional Application Serial No. 60/402,976, filed August 13 2002 entitled( Method, System, and Computer Program Product for Processing of Self-monitoring Blood Glucose (SMBG) Data to Enhance Diabetic Self-management," and No. 60/478,377, filed June 13, 2003J entitled "Method, System, and Computer Program Product for Processing of Self-monitoring Blood Glucose (SMBG) Data to Enhance Diabetic Self-management," the entire disclosures of all three disclosures are hereby incorporated by reference herein. The present application is related to International Application No. PCT/US0:/09884, filed march 29, 2001 (Publication Nos. WO 01/72208 A2, WO 01/72208 A3), entitled "Method, System, and Computer Program Product for the Evaluation of Glycemic Control in Diabetes from Self-monitoring Data," and U.S. Patent Impaction Serial No.: 10/240,228 filed September 26, 2002, entitled "Method, System, and Computer Program Product for the Evaluation of Glycemic Control in Diabetes from Self-monitoring Data," the entire disclosures of which are hereby incorporated by reference herein. FIELD OF THE INVENTION The present system relates generally to Glycemic Control of individuals! with diabetes, and more particularly to a computer-based system and method for evaluation of predicting glycosylated hemoglobin (HbAic and HbA\|) and risk of incurring hypoglycemia. BACKGROUND OF THE INVENTION Extensive studies, including the Diabetes Control and Complications Trial (DCCT) (See DCCT Research Group: The Effect Of Intensive Treatment Of Diabetes On The Development And Progression Of Long-Term Complications Of Insulin-Dependent Diabetes Mellitus. New England Journal of Medicine, 329: 978-986,1993), the Stockholm Diabetes Intervention Study {See Reichard P, Phil M: Mortality and Treatment Side Effects During Long-term Intensified Conventionallnsului Treatment in the Stockholm Diabetes Intervention Study. Diabetes, 43: 313-317, 1994), and the United Kingdom Prospective Diabetes Study {See UK Prospective Diabetes Study Group: Effect of Intensive Blood Glucose Control With Metformin On Complications In Patients With Type 2 Diabetes (UKPDS 34). Lancet, 352; 837-853,1998), have repeatedly demonstrated that the most effective way to prevent the long term complications of diabetes is by strictly maintaining blood glucose (BG) levels within a normal range using intensive insulin therapy. However, the same studies have also documented some adverse effects of intensive insulin therapy, the most aconite of which is the increased risk of frequent severe hypoglycemia (SH), a condition defined as an episode of neuroglycopenia which precludes self-treatment and requires external help for recovery {See DCCT Research Group: Epidemiology of Severe Hypoglycemia In The Diabetes Control and Complications Trial. American Journal of Medicine, 90: 450-459,1991, and DCCT Research Croup: Hypoglycemia in the Diabetes Control and Complications Trial. Diabetes, 46: 271-286, 1997). Since SH can result in accidents, coma, and even death, patients and health care providers are discouraged from pursuing intensive therapy. Consequently, hypoglycemia has been identified as a major barrier to improved glycemic control (Cryer PE: Hypoglycemia is the Limiting Factor in the Management Of Diabetes. Diabetes Metah Res Rev, 15: 42-46,1999). Thus, patients with diabetes face a life-long optimization problem of maintaining strict glycemic control without increasing their risk of hypoglycemia. A major challenge related to this problem is the creation of simple and reliable methods that are capable of evaluating both patients' glycemic control and their risk of hypoglycemia, and that can be applied in their everyday environments. It has been well known for more than twenty years that glycosylated hemoglobin is a marker for the glycemic control of individuals with Diabetes Mellitus (Type I or Type II). Numerous researchers have investigated this relationship and have found that glycosylated hemoglobin generally reflects the average BG levels of a patient over the previous two months. Since in the majority of patients with diabetes the BG levels fluctuate considerably over time, it was suggested that the real connection between mtegrated glucose control and HbAic would be observed only in patients known to be in stable glucose control over a long period of time, Early studies of such patients produced an almost deterministic relationship between the average BG level in the preceding 5 weeks and HbAic, and this curvilinear association yielded a correlation coefficient of 0.98 {See Aaby Svendsen P, Lauritzen T, Soegard U, Nerup J (1982). Glycosylated Hemoglobin and Steady-State Mean Blood Glucose Concentration in Type 1 (Insulin-Dependent) Diabetes, DJabetologJa. 23,403-405). hi 1993 the DCCT concluded that HbAu was the "logical nommee" for a gold-standard glycosylated hemoglobin assay, and the DCCT established a linear relationship between the preceding mean BG and HbAic(5ee Santiago A'(1993). Lessons from the Diabetes Control and Complications Trial, Diabetes. 42,1549-1554). Guidelines were developed indicating that an HbAic of 7% corresponds to a mean BG of 8.3 mM (150 mg/dl), an HbAic of 9% corresponds to a mean BG of 11.7 mM (210 mg/dl), anda l%increaseinHbAicCorresponds to an increase in mean BG of 1.7 mM (30 mg/dl, 2). The DCCT also suggested that because measuring the mean BG directly is not practical, one could assess apatient's glycemic control with a single, simple test, namely HbAio. However, studies clearly demonstrate that HbAjc is not sensitive to hypoglycemia. Indeed, there is no reliable predictor of a patient's immediate risk of SH from any data. The DCCT concluded that only, about 8% of future SH could be predicted from known variables such as the history of SH, low HbAic, a^id hypoglycemia unawareness. One recent review details the current clinical status of this problem, and provides options for preventic^ SH, that are available to patients and their health care providers {See BoUi, GB: How To Ameliorate The Problem of Hypoglycemia In Intensive As Well As Nonintensive Treatment Of Type I Diabetes. Diabetes Care, 22, Supplement 2: B43-B52,1999). Contemporary home BG monitors provide the means for frequent BG measmements through Self-Monitoring of BG (SMBG). However, the problem with SMBG is that tliere is a missing link between the data collected by the BG monitors, and HbAic and hypoglycemia. In other words, there are currently no reliable methods for evaluating HbAic and recognizing imminent hypoglycemia based on SMBG readings {See Bremer T and Gough DA: Is blood glucose predictable from previous values? A solicitation for data. Diabetes 48:445-451,1999). Thus, an object of this invention is to provide this missing link by proposing three distinct, but compatible, algorithms for evaluating HbAic and the risk of hypoglycemia from SMBG data, to be used to predict the short-term and long-term risks of hypoglycemia, and the long-tenm risk of hyperglycemia. The inventors have previously reported that one reason for a missing link between the routinely available SMBG data and the evaluation of HbAio and the risk of hypoglycemia, is that the sophisticated methods of data collection and clinical assessment used in diabetes research, are infrequently supported by diabetes-specific and mathematically sophisticated statistical procedures. Responding to the need for statistical analyses that take into account the specific distribution of BG data, the inventors developed a symmetrizing fransformation of the blood glucose measurement scale (See Kovatchev BP, Cox DJ, Gonder-Frederick LA and WL Clarke (1997). Symmetization of the Blood Glucose Measurement Scale and Its Applications, Diabetes Care. 20,1655-1658) that works as the follows. The BG levels are measured in mg/dl in the United States, and in mmol/L (or mM) in most other countries. The two scales are directly related by 18 mg/dl = 1 mM. The entire BG range is given in most references as 1.1 to 33.3 mM, and this is considered to cover practically all observed values. According to the recommendations of the DCCT {See DCCT Research Group (1993) The Effect Of Intensive Treatment of Diabetes On the Development and Progression of Long-Term Complications of Insulin-Dependent Diabetes Mellitus, New England Journal of Medicine, 329, pp 978-986) the target BG range - also known as the euglycemic range- for a person with diabetes is 3.9 to 10 mM, hypoglycemia occurs when the BG falls below 3.9 mM, and hyperglycemia is when the BG rises above 10 mM. Unfortunately, this scale is numerically asymmetric -- the hyperglycemic range (10 to 33.3mM) is wider than the hypoglycemic range {1.1 to 3.9mM), and the euglycemic range(3.9to lOmM) is not centered withintiie scale. The inventors correct this asymmetry by introducing a transformation^fBGj, which is a continuous function defined on the BG range {1.1, 33.3], having the two-parameter analytical fomi: f(BG, a,fi)-[(ln(BG)r~/3J. a,p>0 and which satisfies the assumptions: A\:f(33.3. a,P) = -f(U.a,P) and A2:f(10.0,a,P)-.f(3.9.a,P). Next, f(.) is multiplied by a third scaling parameter to fix the minimum and maximum valuesoffhetransfonnedBGrangeat-VlO and->^ respectively. These values are convenient since a random variable with a standard normal distribufion has 99.8% ofits values within the interval [-Vio^, -JXQ]. IfBG is measured in mmol/I, when solved iiumerically with respect to the assimipfions Al and A2, the parameters of the fiinction/fBG, a,^ are « = 7,026,0=1.861, and the scalmg parameter is j' = /. 794. If BG is measured in mg/dl iflstead, the parameters are computed to be a=\ .084, p=5.381,aa. Thus, when BG is measured in mmol/1, the symmetrizing transformation is f(BG)=l. 794[(ln (BG)) ^'^ - IMl]. and when BG is measured in mg/dl the symmetrizing transformation is/(2G; = 1.509[(ln (BG)) ^■'^- 5.381]. On the basis of the symmetrizing transformation/f^J the inventors introduced the Low BG Index - a new measure for assessing the risk of hypoglycemia from SMBG readings {See Cox DJ, Kovatchev BP, Julian DM, Gonder-Frederick LA, Polonsky WH, Schlundt DG, Clarke WL: Frequency of Severe Hypoglycemia In IDDM Can Be Predicted From Sqlf-Monitoring Blood Glucose Data. Journal of Clinical Endocrinology and Metabolism, 79: 1659-1662,1994, and Kovatchev BP, Cox DJ, Gonder-Frederick LA Young-Hyman D, Schlimdt D, Clarke WL. Assessment of Risk for Severe Hypogiycemia Among Adults With EDDM: Validation of the Low Blood Glucose Index, Diabetes Care 21:1870-1875,1998). Given a series of SMBG data the LowBG Index is computed as the average of 10. f(BG/ taken for values off(BG) Using the Low BG Index in a regression model the inventors were able to account for 40% of the variance of SH episodes in the subsequent 6 months based on the SH history and SMBG data, and later to enhance this prediction to 46% (See Kovatchev BP, Straume M, Farhi LS, Cox DJ: Estimating the Speed of BloBd Glucose Transitions and its Relationship With Severe Hypoglycemia. Diabetes, 48: Supplement 1, A363,1999). In addition, the inventors developed some data regarding HbAio and SMBG (See Kovatchev BP, Cox DJ, Straume M, Farhy LS. Association of Self-monitoring Blood Glucose Profiles with Glycosylated Hemoglobm. In: Methods m Enzvmolosv, vol. 321: Numerical Computer Methods. Part C, Michael Johnson and Ludvig Brand, Eds., Academic Press, NY;'2O0O). These developments became a part of the theoretical background of this invention. In order to bring tiiis.theory into practice, several key theoretical components, among other things, as described in the following sections, were added. In particular, three methods were developed for employing the evaluation of HbAic, long-term and short-term risk for hypoglycemia. The development of these methods w£^, but not limited thereto, based on detailed analysis of data for 867 individuals with diabetes that included more than 300,000 SMBG readings, records of severe hypoglycemia and determinations ofHbAic. The mventors have therefore sought to improve upon the aforementioned limitations associated with the conventional methods, and thereby provide simple and reliable methods that are capable of evaluating both patients' glycemic control and their risk of hypoglycemia, and that can be applied in their everyday environments. SUMMARY OF THE EWENTIOW The invention includes a data analysis method and computer-based system for the simultaneous evaluation, from routinely collected SMBG data, of the two most important components of glycemic control in diabetes: HbAic and the risk of hypoglycemia. For the purposes of this document, self-monitoring of BG (SMBG) is defined as any method for determination of blood glucose at diabetic patients' natural environment and includes the methods used by contemporary SMBG devices customarily storing 200-250 BG readings, as well as methods used by emerging continuous monitoring technologies. Given this broad definition of SMBG, this mvention pertains directly to the enhancement of existing home blood glucose monitoring devices (but not limited thereto) by introducing an intelligent data interpretation component capable of predicting both HbAic and periods of increased risk of hypoglycemia, as well as to enhancement of future continuous monitoring devices by the same features. One aspect of the invention includes a method, system, and computer program product for evaluating HbAic from a predetermined period of collected SMBG data, for example about 4-6 weeks. lo one embodiment, the invention provides a computerized method and system for evaluating the HbAjg of a patient based on BG data collected over a predetermined duration. The method (or system or computer useable medium) includes evaluating the HbAu of a patient based on BG data collected over a first predetermined duration. The method comprising; preparing the data for estimating HbAic using a predetermined sequence of mathematical formulas defmed as: pre-processing of the data; estimating HbAic using at least one of four predetermined formulas; and validation of the estimate via sample selection criteria. Another aspect of the invention includes a method, system, and computer program product for estimating the long-term probability for severe hypoglycemia (SH). This method uses SMBG readmgs fiom a predetermined period, for example about 4-6 weeks, and predicts the risk of SH within the following approximate 6 months, hi one embodiment, the invention provides a computerized method and system for evaluating the long term probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration. The method (or system or computer useable medium) includes evaluating the long term probability for severe hypoglycemia (SH) or moderate hypoglycemia (MH) of a patient based on BG data collected over a predetemained duration. The method comprising: computing LBGI based on the collected BG data; and estimating the number of future SH episodes using a predetermined mathematical formula based on the computed LBGI. Still yet another aspect of the invention includes a method, system, and computer program product for identifying 24-hoiu" periods (or other select periods) of increased risk of hypoglycemia. This is accomplished through the computation of the short-term risk of hypoglycemia using SMBG readings collected over the previous 24 hours. In one embodiment, the invention provides a computerized method and system for evaluating the short term risk for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration. The method (or system or computer useable medium) includes evaluating the short term probabiUty for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration. The method comprising: computing scale values based on the collected BG data; and computing the low EG risk value (RLO) for each BG daia. An aspect of an embodiment of the present invention includes a method (or ahematively a computer program) for evaluating the HbAi^ of a patient based on BG data collected over a first predetermined duration. The method includes preparing the data for estimating HbAic using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-processing of the data; validation of a sample of the BG data via sample selection criteria; and estimating HbAic if the sample is valid, An aspect of an embodiment of the present invention includes a system for evaluating the HbAu of a patient based on BG data collected over a first predetermined duration. The system included a database component operative to maintain a database identifying said BG data and a processor, wherein the processor is programmed to prepare the data for estimating HbAic using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-process the data, validate a sample of the BG data via sample selection criteria, and estimate HbA^ if the sample is valid. An aspect of an embodiment of the present invention includes a system for evaluating the HbAic of a patient based on BG data collected over a first predetermined duration. The system comprising: a BG acquisition mechanism, which is configured to acquire BG data from the patient; a database component operative to maintain a database identifying said BG data; and a processor. The processor is programmed to prepare the data for estimating HbAjc using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-process the data; validate a sample of the BG data via sample selection criteria; and estimate HbAic if the sample is valid. An aspect of an embodiment of the present invention includes a method (or alternatively a computer program) for evaluating the HbAu of a patient without the need for prior HbAic information based on BG data collected over a first predetermined duration. The method includes preparing the data for estimating HbAic using a predetermined sequence of mathemadcal formulas. The mathematical formulas defined as: pre-processing of the data; validation of a sample of the BG data via sample selection criteria; and estimating HbAic if the sample is valid. An aspect of an embodiment of the present invention includes a system for evaluating the HbAu of a patient without the need for prior HbAie information based on BG data collected over a first predetermined duration. The system includes a database component operative to maintain a database identifying the BG data and a processor. The processor being programmed to prepare the data for estimating HbAj^ using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-process the data, validate a sample of the BG data via sample selection criteria, and estimate HbAie if the sample is valid. An aspect of an embodiment of the present invention includes a system for evaluating the HbAic of a patient without the need for prior HbAic information based on BG data collected over a first predetermined duration. The system comprising: a BG acquisition mechanism, which is configured to acquire BG data from the patient; a database component operative to maintain a database identifying said BG data; and a processor. The processor programmed to prepare the data for estimating HbAi^ using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-process the data; validate a sample of the BG data via sample selection criteria; and estimate HbAic if the sample is valid. These aspects of the invention, as well as other aspects discussed throughout this document, can be integrated together to provide continuous information about the glycemic control of an individual with diabetes, and enhanced monitoring of the risk of hypoglycemia. These and other objects, along with advantages and features of the invention disclosed herein, will be made more apparent from the description, drawings and claims that follow. BRIEF DESCRIPTION OF THE DRAWINGS The foregoing and other objects, features and advantages of the present invention, as well as the invention itself, well be more fiilly understood from the following description of preferred embodiments, when read together with the accompanying drawings in which: FIG. 1 graphically presents the empirical and theoretical probabilities for moderate (dashed line) and severe (black line) hypoglycemia within one month after the SMBG assessment for each of the 15 categories of risklevel defined by the Low BG Index of Example No. 1. FIG. 2 graphically presents the empirical and theoretical probabilities for moderate (dashed line) and severe (black Ime) hypoglycemia within three months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index of Example Np. 1, PIG. 3 graphically presents the empirical and theoretical probabilities for moderate (dashed line) and severe (black line) hypoglycemia within six months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index of Example No. 1. FIG. 4 graphically presents the empirical and theoretical probabilities for 2 or more moderate (dashed line) and sever (black line) hypoglycemic episodes within three months after the SMSG assessment for each of the 15 categories ofrisk level defmed by the Low BG Index of Example No. 1. FIG. 5 graphically presents the empirical and theoretical probabilities for 2 or more moderate (dashed line) and severe (black line) hypogf ycemic episodes within six months after the SMBG assessment for each of the 15 categories of risk level defmed by the Low BG Index of Example No. 1. FIG. 6 is a fimctional block diagram for a computer system for implementation of the present invention. FIGS. 7-9 are schematic block diagrams of altemative variations of the present invention related processors, communication links, and systems. FIG, 10 graphically presents the empirical and theoretical probabilities for 3 or more moderate (dashed line) and severe (black line) hypoglycemic episodes within six months after the SMBG assessment for each of the 15 categories ofrisk level defined by theLowBGIndexofExampleNo. 1. FIG. 11 graphically shows the analysis of the residuals of this model showed a close to normal distribution of the residuals for Training Data set lof Example No. 1. FtG. 12 graphically shows the analysis of the residuals of this model showed a close to norma] distribution of the residuals lof Example No. 1 FIG. 13 graphically shows a statistical evidence for that is given by the normal probability plot lofExarapleNo. 1. FIG. 14 graphically presents the smoothed dependence between the hit rate and the ratio Rud expressed m percentage in Example No. 1. FIG. 15 graphically presents the dependence between the prediction period and the corresponding hit rate in Example No. 1. FIGS. 16 (A)-(B) graphically present a one-month risk for significant hypoglycemia in TIDM predicted by the LBGI for ANOVA of number of severe hypoglycemic episodes by risk group (F=7.2, pO.OOl) and ANOVA of number of moderate hypoglycemic episodes by risk group (F=13.9, p FIGS. 17 (A)-{B) graphically present a 3-month risk for significant hypoglycemia in TIDM predicted by the LBGI for ANOVA of number of severe hypoglycemic episodes by risk group (F=9.2, p FIGS. 18 (A)-(B) graphically present a one-month risk for significant hypoglycemia in T2DM predicted by the LBGI for ANOVA of nmciber of severe hypoglycemic episodes by risk group (F=6.0, p FIGS. 19 (A)-(B) graphically present a 3-month risk for significant hypoglycemia in T2DM predicted by the LBGI for ANOVA of number of severe hypoglycerriic episodes by risk group (F=5.3, p DETAILED DESCRIPTION OF THE INVENTION The invention makes possible, but not limited thereto, the creation of precise methods for the evaluation of diabetics' glycemic control, and include, firmware and software code to be used in computing the key components of the method. The inventive methods for evaluating HhAu, the long-term probability of SH, and the short-term risk of hypoglycemia, are also validated based on the extensive data collected, as will be discussed later in this document. Finally, the aspects of these methods can be combined in structured display or matrix. I. Evaluating HbAir One aspect of the invention includes a method, system, and computer program product for evaluating HbAjc from a predetermined period of collected SMBG data, for example 4-6 weeks. In one embodiment, the invention provides a computerized (or other type) method and system for evaluating the HbAic of a patient based on BG data collected over a predetermined duration. The method includes evaluating the HbAic of a patient based on BG data collected over a first predetermined duration, the method comprismg: preparing the data for estimating HbAio using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-processing of the data; estimating HbAic using at least one of four predetermined formulas; and validation of the estimate via sample selection criteria. The first predetermined duration can be about 60 days, or alternatively the first predetermined duration ranges from about 45 days to about 75 days, or &om about 45 days to about 90 days, or as desired; The preprocessing of the data for each patient comprise: conversion of plasma to whole blood BG mg/dl; conversion of BG measured in mg/dl to units of mmol/1; and computing Low Blood Glucose Index (RLOl) and High Blood Glucose Index {RHIl). The preprocessing of the data for each patient uses a predetermined mathematical formulas defined as: conversion of plasma to whole blood BG mg/dl via BG=PLASBG (mg/dl) /1.12; conversion of BG measured in mg/dl to units of mmol/I) via BGMM=BG/18; and computing Low Blood Glucose hidex (RLOl) and High Blood Glucose Index (RHIl). The preprocessing of the data further uses a predetermined mathematical formula defined as: Scale=[In(BG)]^°^'^ - 5.381, wherein BG is measured m units of mg/dl; Riskl = 22.765 (Scale)^ wherein RiskLO=Riskl if (BG is less than about 112.5) and therefore risk of LBGI exists, otherwise RiskLO=0; RiskHI=Riskl if (BG is greater than about ] ]2.5) and therefore risk of HBGI exists, otherwise RisfcHI=0; BGMMI = average of BGMM per patient; RLOl = average of RiskLO per patient; RHIl = average of RiskHI per patient; L06 = average of RiskLO computed only for readings during the night, otherwise missing if there are no readings at night; N06, NI2, N24 are percentage of SMBG readings in time intervals; NCI = total number of SMBG readings in the first predetermined duration; and NDAYS = number of days with SMBG readings in the first predetermined duration. The N06, N12, N24 are percentage of SMBG readings in time intervals of about 0-6:59 hour time period; about 7-12:59 hour time peri6d, and about 18-23:59 hour time period, respectively, or other desired percentages and number of intervals. The method fijrther comprises assigning a group depending on the patient's computed High BG Index using a predetermined mathematical formula. This formula may be defined as: if (RHIl is about 16) then the assigned group= 0; if (RHIl is > about 5.25 and if RHIl is about 7.0 and if RHU is about8.5 and if RHIl is Next, the method may iiirther include providing estimates using a predetermined mathematical formula defined as: EC = 0.55555BGMMl+2.95; El = 0.50567BGMMl+0.074L06+2.69; E2 = 0.55555BGMMl-0.074L06+2.96; E3 = 0.44000BGMMl+0.035L06+3.65; and if (Group = 1) then EST2=E1, or if (Group = 2) then EST2=£2, or if (Group = 3) then EST2=E3, otherwise EST2=E0. The method comprise providing fiirther correction of the estimates using a predetermined mathematical formula defmed as; if (missing(L06)) EST2=E0, if (RLOl is about 26) then EST2=EO-1.5RL01; and if ((RLOl/RHIl) is about 1.3) then EST2=EST2-0.08. The estimation oftheHbAicofa patient based on BG data collected over the first predetermined duration can be accomplished by estimating HbAio Using at least one of four predetermined mathematical formulas defined as: a) HbAlc = the EST2 defmed above or as corrected above; b) HbAic = 0.809098BGMM1 + 0.064540RLOl - 0.15I673RHn + 1.S73325, wherein BGMMl.is the average BG (mmol/1), RLOl is the LowBG Index, RHIl is the High BG Index;.* c) HbAlt; = 0.682742HBAO + 0.054377RHIl +1.553277, wherein HBAO is a previous reference HbAl c reading taken about a second predetermined period prior to the estimate, wherein KHIl = is the High BG Index; or d) HbAlc = 0.41046BGMM + 4.0775 wherein BGMMl is the average BG (mmol/I). The second predetermined duration can be about three months; about 2.5 months to about 3.5 months; or ahout 2.5 months to six months, or as desired. The validation of the estimate using sample selection criteria of HbAlc estimate is achieved only if the first predetermined duration sample meets at least one of the following four criteria; a) a.test firequency criterion wherein if the first predetermined duration sample contains an average of at least about 1.5 to about 2.5 tests per day; b) an alternative test frequency criterion only if the predetermined duration sample contains at least a third predetermined sample period with readings with an average firequency of about 1.8 readings/day (or other desired average fl'equency); c) a randomness of data criterion-1 wherein the_HbAlc estimate is validated or displayed only if the ratio (RL01/RHII>= about 0.005), wherein: RLOl is the Low BG Index, RHIl is the High BG Index; or d) a randomness of data criterion wherein HbAlc estimate is validated or displayed only if the ratio {N06 >= about 3%), and wherem N06 is the percentage of readings during the night. The third predetermined duration can be at least 35 days, range firom about 35 days to about 40 days, or fiom about 35 days to about as long as the first predetermined duration, or as desired. n. Loae-term Probability for Severe Hypoglycemia fSH). Another aspect of the invention includes a method, system, and computer program product for estimating the long-term probability for severe hypoglycemia (SH). This method uses SMBG readings fiom a predetermined period, for example about 4-6 weeks, and predicts the risk of SH within the following approximate 6 months. In one embodiment, the invention provides a computerized method (or other type) and system for evaluating the long term probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration. The method for evaluating the long term probability for severe hypoglycemia (SH) or moderate hypoglycemia (MH) of a patient based on BG data collected over a predetermined duration comprises: computing LBGI based on the collected BG data; and estimating the number of fiiture SH episodes using a predetermined mathematical formula based on the computed LBGI. The computed LBGI is mathematically defined fi:om a series of BG readings ;i:;, X2,... Xn taken at time points fj, fa ...,t„ as: LBGI=^yibgi(x,;2).v!hsTe:lbgJ(BG;a)= lO.ffBG)' iif(BG)>OmAO othenvise, and a = about 2, representing a weighting parameter (or other weighting parameter as desire). A predetermined risk categories(RCAT) is defined, whereby each of the risk categories(RCAT) represent a range of values for LBGI; and the LBGI is assigned to at least one of said risk categories(RCAT). The risk categoriesCRCAT) are defined as follows: category 1, wherein said LBGI is less than about 0.25; category 2, wherein said LBGI is between about 0.25 and about 0.50; category 3, wherein said LBGI is between about 0.50 and about 0,75; category 4, wherein said LBGI is between about 0.75 and about 1.0; category 5, wherein said LBGI is between about 1.0 and about 1.25; category 6, wherein- said LBGI is between about 1.25 and about 1.50; category 7, wherein said LBGI is between about 1.5 and about 1.75; category 8, wherein said LBQI is between about 1.75 and about 2.0; category 9, wherein said LBGI is between about 2.0 and about 2.5; category 10, wherein said LBGI is between about 2.5 and about 3.0 category 11, wherein said LBGI is between about 3.0 and about 3.5; category 12, wherein said LBGI is between about 3.5 and about 4.25; category 13, wherein said LBGI is between about 4.25 and about 5.0; category 14, wherein said LBGI is between about 5.0 and about 6.5; and categoryl5, wherein said LBGI is above about 6.5. Next, the probability of incurring a select number of SH episodes is defined respectively for each'of said assigned risk categoriesfRCAT). Defining a probability of incurring a select number of SH episodes within a next first predetermined duration respectively for each of said assigned risk categories(RCAT), using the formula: F(x) = / -exp(-a.x') for any :t>(? and 0 otherwise, wherein: a = about-4.19 and b = about 1.75 (a and/or b may be other desired values). The first predetermined duration can be about one month; range from about 0,5 months to about 1.5 months, or ranges from about 0.5 months to about 3 months, or as desired. Also, the probability,of incurring a select number of SH episodes within a next second predetermined duration respectively for each of said assigned risk categories(RCAT) is defined, usmg the formula: F(x) = / - exp(-a.x^) for any x>0 and 0 otherwise, wherein: a = about -3.28 and b = about 1.50 (a and/or b may be other desired values). The second predetermined duration can be about three months, range from about 2 months to about 4 months, or about 3 months to about 6 months, or as desired. Further, a probability of incurring a select number of SH episodes within the next third predetermined duration is defined respectively for each of the assigned risk categories(RCAT), using the formula: F(x) = J - exp(-a.y^) for any x>0 and 0 otiiervi^se, wherein: a = about -3.06 and b = about 1.45 (a and/or b may be other desired values). rhe third predetermined duration can be about 6 months, range from about 5 months to about 7 months, or range from about 3 months to about 9 months, or as deshed. Alternatively, a probability of incurring a select number of MH episodes within ihe next first predetermined period (ranges of about 1 month, about 0.5-1.5 months, about 3.5-3 months, or as desired) is defined respectively for each of said assigned risk :ategories(RCATO, using the foiinula: F(x) = i - ey^(-a.x^) for snyx>0 and 0 otherwise, Alierein; a = about-V.58 and b = about 1.05 (aand/orbmay be other desired values). Alternatively, a probability of incurring a select number of MH episodes within he next second predetermined period (ranges of about 3 months, about 2-4 months, about 3-6 montiis, or as desired) is defined respectively for each of said assigned risk categories(RCAT), using tke formula: F(x) = i - exp(-a.x') for any x>0 and 0 otherwise, wherein: a = about-1.37andb-about 1.14 (a and/orb may be otlier desired values). Alternatively, a probability of incurring a select number of MH episodes within the next third predetermined period (ranges of about 6 months, about 5-7 months, about 3-9 months, or as desired) is defined respectively for each of said assigned risk categories(RCAT), using the formula; F(x) - 1 - expi-a.x) for any x>0 and 0 otherwise, wherein; a = about-1.37 andb = about 1.35 (a and/orb may be other desired values). Moreover, classifications of risk for future significant hypoglycemia of the patient are assigned. The classifications are defined as follows: minimal risk, wherein said LBGI is leas than, about 1.25; low risk, wherein said LBGI is between about 1.25 and about 2.50; moderate risk, wherein said LBGI is between about 2.5 and about 5; and high risk, wherein said LBGI is above about 5.0 (other classification ranges can be implemented as desired). in. Short-tenp ProbabHity for Severe Hypoglycemia fSH). Still yet another aspect of the invention includes a method, system, and computer program product for identifying 24-hour periods (or other select periods) of increased risk of hypoglycemia. This is accomplished through the computation of the short-term risk of hypoglycemia using SMBG readings collected over liie previous 24 hours. In one embodiment, the invention provides a computerized method and system for evaluating the short term risk for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration. The method for evaluating the short temi probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration comprises: computing scale values based on said collected BG data; and computing the low BG risk value (RLO) for each BG data. The computed RLO(BG) is mathematically defined as: Scale = [ln(BG)]' '^^'^ - 5.381, wherein BG is measured in units of mg/dl; Risk = 22.765(Scale)^; if (BG is less than about 112.5) then: RLO(BG)=Risk, otherwise RLO(BG)= 0. Alternatively, the computed RLO(BG) is mathematically defined as: Scale=[In(BG)]''^ -1.861, wherein BG is measured in units of mmolA; Risk= 32.184(Scale)^; if (BG isSibout 112.5) then: RLO(BG) = Risk, otherwise RLO(BG) = 0. LBGI can be computed based on the collected BG data. The computed LBGI is mathematically defined irom a series of BG readings ;ci, Xj,... a:„ taken at time points r^, t2, ..., t„ as: LBGI = -y]lbgiix,;2). where: lbgi(BG;a) = RLO(BG). Provisional LBGI can be computed based on the collected BG data. The computed provisional LBGI is mathematically defined from mathematically defined as: LBGI(l) - RLO(x,); RL02(1) = 0; LBGI(j)= ((j-l)/]) LBGI (j-l) + (1/j) * RLOC:^); and RLO2G)=(a-l)/j)RL02Ci-l) + (l/j)(RL0C:5)-LBGIG)f. SBGI can be computed using a mathematical formula defined as: SBGI(n)= Next, the invention provides a quahfication or warning of upcoming short term SH. The qualification or warning is provided if CLBGIC150) >2.5 and LBGI(50) > (1.5LBGI(150) and SBG1(50) > SBGI(150)) then said issue of warning is qualified or provided, or RLO >(LBGI(150)+1.5SBGI(150)) then said issue of warning is qualified or provided, otherwise, a warning is not necessarily qualified or provided. Altematively, Next, the invention provides a qualification or warning of upcoming short term SH. The qualification or warning i^ provided if: (LBGI(n) >a and SBGI(n.) ge (P)) then said issue of warning is qualified or provided, and/or (RLO(n) >(LBGI(n)+ y * SBGI(n))) then said issue of warning is qualified or provided; otherwise a warning is not necessarily qualified or provided, wherein a, p, and y are threshold parameters. The threshold parameters a, p, and y are defined as a = about 5, p = about 7.5, y = about 1,5. Other possible parameter combinations are presented in the table below. The values may be approximations of the values presented below as well as any intermediate combination of values in the table below. a P Y a P y 6.4 8.2 1.5 5.0 7.5 1.3 6.0 7.5 1.5 4.9 7,0 1.2 5.5 7.5 1.5 4.8 7.0 1.2 IV. Exemplary Systems The method of the invention may be implemented using hardware, software or a combination thereof and may be implemented in one or more computer systems or other processing systems, such as persona! digit assistants (PDAs), or directly in blood glucose self-monitoring devices (SMBG memory meters) equipped with adequate memory and processing capabilities. In an example embodiment, the invention was implemented in software runniag on a general purpose computer 900 as illustrated in FIG. 6. Computer system 600 includes one or more processors, such as processor 604 Processor 604 is connected to a commimication in&astructure 606 (e.g., a communications bus, cross-over bar, or network). Computer system 600 may include a display interface 602 that forwards graphics, text, and other data ftom the communication infrastructure 606 (or from a frame buffer not shown) for display on the display unit 630. Computer system 600 also includes a main memory 608, preferably random access memory (RAM), and may also mclude a secondary memory 610. The secondary memory 610 may include, for example, a hard disk drive 612 and/or a removable storage drive 614, representiiig a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, etc. The removable storage drive 614 reads from and/or writes to a removable storage unit 618 in a well known manner. Removable storage unit 618, represents a floppy disk, magnetic tape, optical disk, etc, which is read by and written to by removable storage drive 614. As will be appreciated, the removable storage unit 618 includes a computer usable storage medium having stored therein computer software and/or data. In alternative embodiments, secondary memory 610 may include other means for allowing computer programs or other instructions to be loaded into computer system 600. Such means may include, for example, a removable storage unit 622 and an interface 620. Examples of such removable- storage units/interfaces include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as a ROM, PROM, EPROM or EEPROM) and associated socket, and other removable storage units 622 and interfaces 620 which allow software and data to be transferred from the removable storage unit 622 to computer system 600. Computer system 600 may also include a communications interface 624, Communications interface 624 allows software and data to be transferred between computer system 600 and external devices. Examples of communications interface 624 may include a modem, a network interface (such as an Ethernet card), a communications port (e.g., serial or par^lcl, etc.), a PCMCIA slot and card, a modem, etc. Software and data, transferred via communications interface 624 are in the form of signals 628 which maybe electronic, electromagnetic, optical or other signals capable of being received by communications interfece 624. Signals 628 are provided to communications interface 624 via a communications path (i.e., channel) 626. Channel 626 carries signals 628 and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, an infrared link, and other communications channels. In this docLunent, the terms "computer program medium" and "computer usable medium" are used to generally refer to media such as removable storage drive 614, a hard disk installed in hard disk drive 612, and signals 628. These computer program products are means for providing software to computer system 600. The invention includes such computer program products. Computer programs (also called computer control logic) are stored in main memory 608 and/or secondary memory 610. Computer programs may also be received via communications interface 624. Such computer programs, when executed, enable computer system 600 to perform the features of the present invention as discxissed herein. In particular, the computer programs, when executed, enable processor 604 to perform the fimctions of the present invention. Accordingly, such computer programs represent controllers of computer system 600. In an emboi^ment where the Invention is implemented using software, tiie software may be stored in a computer program product and loaded into computer system 600 using removable storage drive 614, hard drive 612 or communications interface 624. The control logic (software), when executed by the processor 604, causes the processor 604 to perform the fimctions of the invention as described herein. In another embodiment, the invention is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine to perform the fimctions described herein will ,be apparent to persons skilled in the relevant art(s). In yet another embodiment, the mvention is implemented using a combination of both hardware and'software. In an example software embodiment of the mvention, the methods described above were implemented in SPSS control language, but could be implemented in other programs such as, but not limited to, C + + programming language or other programs available to those skilled in the art. FIGS. 7-9 show block diagrammatic representation of alternative embodiments of the invention. Referring FIG. 7, there is shown a block diagrammatic representation of the system 710 essentially cornprises tiie glucose meter 728 used by a patient 712 for recording, inter aha, insulin dosage readings and measured blood glucose ("BG") levels. Data obtained by the glucose meter 728 is preferably transferred through appropriate communication links 714 or data modem 732 to a processing station or chip, such as a personal computer 740, PDA, or cellular telephone, or via appropriate Internet portal. For instance, data stored may be stored within the glucose meter 728 and may be directly downloaded into the persona! computer 740 through an appropriate interface cable and then transmitted via the Internet to a processing location. An example is the ONE TOUCH monitoring system or meter by LifeScan, Inc. which is compatible with IN TOUCH software which includes an interface cable to down load the data to a personal computer. The glucose meter is common in the industry and includes essentially any device that can fimctions as a BG acqxiisition mechanism. The BG meter or acquisition mechanism, device, tool, or system incliides various conventional methods directed toward diawing a blood sample (e.g. by fmgea-prick) for each test, and a determination of the glucose level using an instrument that reads glucose concentrations by electromechanical or claorimetric methods. Recently, various m.ethods for determining the concentration of blood analytes without drawing blood have been developed. For example, U.S. Pat. No. 5,267,152 to Yang et al. Qiereby incorporated by reference) describes a noninvasive technique of measuring blood glucose concentration using near-IR radiation diffiise-reflection laser spectroscopy. Similar near-IR spectrometric devices are also described m U.S. Pat, No. 5,086,229 to Rosenthai et al. and U.S. Pat. No. 4,975,581 to Robinson et al. (of which are hereby incorporated by reference). . U.S.Pat. No. 5,139,023 to Stanley (hereby incorporated by reference) describes a transdermal blood glucose monitoring apparatus that relies on a permeability enhancer (e.g., a bile salt) to facilitate transdermal movement of glucose along a concentration gradient established between interstitial fluid and a receiving medium. U.S. Pat. No. 5,036,861 to Sembrowich (hereby incorporated by reference) describes a passive glucose monitor that collects perspiration through a skin patch, where a cholinergic agent is used to stimulate perspiration secretion &om the eccrine sweat gland. Similar perspiration collection devices are described in U.S. Pat, No. 5,076,273 to Schoendorfer and U.S. Pat. No. 5,140,985 to Schroeder (of which are hereby incorporated by reference). In addition, U.S. Pat. No. 5,279,543 to Glikfeld (hereby incorporated by reference) describes the use of iontophoresis to noninvasively sample a substance through skin into a receptacle on the skin surface. Glikfeld teaches that this sampling procedure can be coupled with a glucose-specific biosensor or glucose-specific electrodes in order to monitor blood glucose. Moreover, International Publication No. WO 96/00110 to Tamada (hereby incorporated by reference) describes an iontophoretic apparabis for transdermal monitoring of a target substance, wherein an iontophoretic electrode is used to move an analyte into a collection reservoir and a biosensor is used to detect the target analyte present in the reservoir. Finally, U.S, Pat, No. 6,144,869 to Bemer (hereby incorporated by reference) describes a sampling system for measuring the concentration of an analyte present. Further yet, the BG meter or acquisition meohanism may include indwelling catheters and subcutaneous tissue fluid sampling. The computer or PDA 740 includes the software and hardware necessary to process, analyze and mterpret the self-recorded diabetes patient data in accordance with predefined flow sequences (as described above in detail) and generate an appropriate data interpretation output. Preferably, the results of the data analysis and interpretation performed upon the stored patient data by the computer 740 are displayed in the form of a paper report generated through a printer associated with the personal computer 740. Alternatively, the results of the data interpretation procedure may be directly displayed on a video display unit associated with the computer 740. FIG. 8 shows a block diagrammatic representation of an alternative embodiment having a diabetes management system that is a patient-operated apparatus 810 having a bousing preferably sufficiently compact to enable apparatus 810 to be hand-held and carried by a patient. A strip guide for receiving a blood glucose test strip (not shown) is located on a surface of housing 816. Test strip is for receiving a blood sample from the patient 812. The apparatus includes a microprocessor 822 and a memory 824 connected to microprocessor 822. Microprocessor 822 is designed to execute a computer program stored in memory 824 to perform the various calculations and control fiinctions as discussed in great detail above. A keypad 816 is connected to microprocessor 822 through a standard keypad decoder 826. Display 814 is connected to microprocessor 822 through a display driver 830. Microprocessor 822 communicates with display driver 830 via an mterface, and display driver 830 updates and refreshes display 814 under the control of microprocessor 822. Speaker 854 and a clock 856 are also connected to raicioprocessoi 822. Speaker 854 operates under the control of microprocessor 822 to emit audible tones alerting the patient to possible future hypoglycemia. Clock 856 supplies the current date and time to microprocessor 822. Memory 824 also- stores blood glucose values of the patient 812, ihe insulin dose values, the insulin types, and the parameter values used by microprocessor 822 to calciUate future blood glucose values, supplemental insulin doses, and carbohydrate supplements. Each blood glucose value and insulin dose value is stored in memory 824 with a corresponding date and time. Memory 824 is preferably a non-volatile memory, such as an electrically erasable read only memory (EEPROM). Apparatus 810 also includes a blood glucose meter 828 comiected to microprocessor 822. Glucose meter 828 is designed to measure blood samples received on blood glucose test strips and to produce blood glucose values from measurements of the blood samples. As mentioned previously, such glucose meters are ■well known in the art. Glucose meter 828 is preferably of the type which produces digital values which are output directly to microprocessor 822. Alternatively, blood glucose meter 828 may be of the type which produces analog values. In this altemative embodiment, blood glucose meter 828 is connected to microprocessor 822 through an analog to digital converter (not shown). Apparatus 810 further includes an input/output port 834, preferably a serial port, which is connected to microprocessor 822. Port 834 is connected to a modem 832 by an interface, preferably a standard RS232 interface. Modem 832 is for estabhshing a communication link between apparatus 810 and a persona! computer 840 or a healthcare piovidei computer 838 through a communication network 836. Specific techniques for connecting electronic devices through cormection cords are well knovra in the art. Another altemative example is "bluetooth" technology communication. Alternatively, FIG. 9 shows a block diagrammatic representation of an altemative embodiment having a diabetes management system that is a patient-operated apparatus 910, similar as shown in FIG. 8, having a housing prefeiably suSiciently compact to enable the apparatus 910 to be hand-held and carried by a patient. For example, a separate or detachable glucose meter or BG acquisition mechanism/module 928. There are already self-monitoring devices that are capable of directly computing Algorithms 1, 1, 3 and displaying the results to the patient without transmitting the data to anything else. Examples of such devices are ULTRA SMART by Lifescan Inc., Milpitas, CA and FREESTYLE TRACKER by Therasense, Alameda, CA. Accordingly, the embodiments described herein are capable of being implemented over data communication networks such as the internet, making evaluations, estimates, and information accessible to any processor or computer at any remote location, as depicted in FIGS. 6-9 and/or U.S. Pat. No. 5,851,186 to Wood, of which is hereby incorporated by reference herein. Alternatively, patients located at remote locations may have the BG data transmitted to a central healthcare provider or residence, or a different remote location. In simmiary, the invention proposes a data analysis computerized (or noncomputerized) mefcod and system for the simultaneous evaluation of the two most important components of glycemic control in individuals with diabetes: HbAlc and the risk of hypoglycemia. The method, v^'hile using only routine SMBG data, provides, among other things, three sets of output. The potential implementations of the method, system, and computer program product of the invention is that it provides the following advantages, but are not limited thereto. First, the invention enhances existing home BG monitoring devices by producing and displaying; 1) estimated categories for HbAl c, 2) estimated probability for SH in the subsequent six months, and 3) estimated short-term risk of hypoglycemia (i.e. for the next 24 hours). The latter may include warnings, such as an alarm, that indicates imminent hypoglycemic episodes. These three components can also be integrated to provide continuous information, about the glycemic control of individuals with diabetes, and to enhance the monitoring of their risk of hypoglycemia. As an additional advantage, the invention enhances existing software or hardware that retrieves SMBG data. Such software or hardware is produced by virtually every manufacturer of home BG monitoring devices and is customarily used by patients and health care providers to interpret SMBG data. The methods and system of the invention can be directiy incorporated into existing home blood glucose monitors, or used for the enhancement of software that retrieves SMBG data, by introducing a data interpretation component capable of .predicting both HbAl c and periods of increased risk of hypoglycemia. Still yet another advantage, the invention evaluates the accuracy of home BG monitoring devices, both in the low and high BG ranges, and over the entire BG scale. Moreover, another advantage, tiie invention evaluates the effectiveness of various treatments for diabetes Further still, as patients with diabetes face a hfe-Iong optimization problem of maintaining strict glycemic control without increasing their risk of hypoglycemia, the present invention alienates this related problem by use of its simple and reliable methods, i.e., the invention is capable of evaluating both patients' glycemic control and their risk of hypoglycemia, and at the same time applying it in then- everyday environments. Additionally, the invention provides the missing link by proposing three distinct, but compatible, algorithms for evaluating HbAlc and the risk of hypoglycemia ftom SMBG data, to fae used to predict the short-term and long-term risks of hypoglycemia, and the long-term risk of hyperglycemia. Another advantage, the invention evaluates the effectiveness of new insulin or insulin delivery devices. Any manufacturer or researcher of kisulin or insulin delivery devices can utilize the embodiments of the invention to test the relative success of proposed or tested insulin types or device delivery designs. Finally, another advantage, the invention evaluates the effectiveness of drugs that are adjunct to insulin therapy. EXAMPLES OF INVENTION I. EXAMPLE NO. 1 This Example No. i consists of three algorithms for simultaneous evaluation, fiom routine SMBG data, ofthetwomost important components of glycemic control in diabetes, HbAjc and risk for hypoglycemia. This metliod pertains directly to enhancement of existing home BG-monitoring devices by introducing an intelligent data interpretation component capable ($f predicting both HbAu and periods of increased risk for hypoglycemia. The data analysis method has three components (algorithms); ■ Algorithm 1: Evaluation of HbAic! ■ Algorithm' 2: Evaluation of long-term risk for severe hypoglycemia (SH), and « Algorithm 3: Evaluation of short-term (within 24-48 hours) risk for hypoglycemia. Algorithm 1 and 2 provide uninterrupted monitoring and information about the overall glycemic control of an individual with Type I or Type 2 diabetes mellitus (TIDM, T2DM), covering both the high and the low end of the BG scale. Algorithm 3 is supposed to be activated when Algorithm 2 mdicates an increased long-term risk for hypoglycemia. Upon activation. Algorithm 3 requires more ftequent monitoring (4 times a day) and provides 24 to 48-hour forecast of the risk for moderate/severe hypoglycemia. Another important objective of Example 1 was to test with existing data a number of hypotheses and ideas that could potentially lead to alternative algorithms estimating HbAic and computing risk for hypoglycemia in a manner that is conceptually different from the one proposed in the invention disclosure. The goal vras to find potentially better solutions, or simply to verify that certain ideas do not lead to better results, which is essential for optimization and promptness of the analysis of the data that are currently being collected in Example No. 2 of the study. DATA SETS In order to ensure that the results of our optimization can be generalized to population level, Algorithms 1 and 2 were first optimized using traming data sets and then tested for accuracy using an vmrelated test data set. For Algorithm 3 we cunently have only one data set containing parallel SMBG and records of SH. A detailed description of the patient population follows: (1) Training Data set 1: Ninety-six patients with TIDM, who were diagnosed at least 2 years prior to the study. Forty-three of these patients reported at least two episodes of severe hypoglycemia in the past year and 53 patients reported no such episodes during the same period. There wepe 38 males and 58 females. The mean age was 35±8 yr., mean duration of disease I6±10 yr., mean msulin units/kg per day 0,58±0.19, and meanHbAic was 8.6±1.8yo. These subjects collected approximately 13,000 SMBG readings over 40-45-day period. The frequency of SMBG was approximately 3 readmg/day. This data collection was followed by 6 months of monthly diaries of modeiate and severe hypoglycemic episodes. This data set was used as a training data set for Algorithm 1 (no prior HbAic) and for Algorithm 2. (2) Traming Data set: Eighty-five patients with TIDM, diagnosed at least 2 years prior to the study, all of whom reported SH episodes ia the past year. There were 44 males and 41 females. The mean age was 44±I0 years, mean duration of disease 26±11 yr., meaninsulinumts/kgperday0.6±0.2, the mean baseline HbAio was 7,7±1.1%, and the mean 6-month HbAu was 7.4±1% (6-month HbAio available for 60 subjects). These subjects collected approximately 75,500 SMBG readmgs during the 6 months between the two HbAic assays. The frequency of SMBG in Data set 2 was higher - 4-5 readings per day. In addition, during the 6 months of SMBG the subjects kept diaries of moderate and severe hypoglycemic episodes by date and time of their occurrence, resulting in 399 SH episodes. This data set was used as a training data set for Algorithm 1 (with prior HfaAic) and for all analyses concerning Algorithm 3. (3) The Test Data Set that we used contains data for N=600 subjects, 277 with TIDM and 323 with T2DM, all of whom used insulin to manage their diabetes, These data were collected by Amylin Pharmaceuticals, San Diego, CA and included 6-8 months of SMBG data (approximately 300,000 readings), accompanied by baseline and 6-month HbAic determinations and some demographic data. These subjects were participating in a clinical trial investigating the effects of pramlintide (in doses of 60 to 120 micrograms) on metabolic control. The subjects' use pramlintide was randomized across the TIDM and T2DM groups (Table 1). Table 1: Demoeyaphjc characteristics of the subjects in die Test Data Set. Variable TIDM- Meaii(SD) T2DM-Mean(SD) p-level Age (years) 38.0(13.4) 58.1 (9.4) Gender; Male/Female 136/141 157/166 Ns Baseline HbAic 9.74(1.3) 9.85(1.3) Ns HfaAi(.atmonth6 8.77(1.1) 8.98 (1.3) 0,04 Duration of diabetes (years) 14,6 (9.8) 13.5 (7,6) Ns Age at onset (years) 23,4 (12.8) 44.6(10.4) # SMBG readings / subject / day 3.2(1.1) 2.9 (0.9) Table 1 presents demographic characteristics and comparison of TIDM vs. T2DM subjects. For the Srst 6 months of fee studyflie average HbAjc declined significantly in both TIDM and T2 DM groups, perhaps due to the use of medication, which is out of the scope of this presentation (Table 1). This relatively rapid change in HbAic allowed for a better estimation of the predictive ability of Algoritlim 1. hi all data sets SMBG was performed using Lifescan ONE TOUCH 11 or ONE TOUCH PROFILE meters. ALGORITHM 1: EVALUATION OF HBA.r Example No. 1 provides for, but not limited thereto, an optimization of the prediction of HbAu (Algorithm 1) through: (I) Weighting higher the more proximal SMBG; (2) Weighting higher more prolonged high BG events: (3) Calibrating the High BG Index with an earlier HhAu, and (4) Incorporating other patient variables, such as age, gender and duiition of disease. Algorithm I includes an optimal fimction of SMBG data that evaluates subsequent HbAic, as well as recommendations for the optimal duration of the data collection period and the optunal frequency of self-monitoring during that period. It is essential to note, however, that the broader goal of Algorithm 1 is to evaluate the status of patients' glycemic control. Although HbAic is the accepted "gold standard" for evaluation of glycemic control, cimently it is unclear whether anolher measure, such as average SMBG or ffigh BG Index, would not be a better predictor of long-term complications in diabetes than HbAic. Until this issue is clarified, the goal of Algorithm 1 will be to estimate HbAu- In order to approximate as closely as possible future real applications of Algorithm 1 we proceeded as follows; (1) First, several optimal ftmctions using different independent variables, optimal duration, and optimal frequency of SMBG were derived from two training data sets 1 and 2, collected in our previous studies involving patients with TIDM; (2) Then, all coefficients were fixed and Algorithm 1 was applied to the much larger test data set containing data for both TIDM and T2DM subjects collected imder very different conditions in a clinical trial conducted by Amylin Pharmaceuticals. (3) Detailed estimation ofthe preciseness of Algorithm 1 for various optimal fiinctions was made using the test data set only. This separation of training and test data sets allows us to clainithat the estimated preciseness of Algorithm 1 can be generalized to any other data of subjects with Tl DM or T2DM. Moreover, since the Amylin data (test data set) were collected from subjects who were undergoing treatment to lower their HbAic, and therefore exhibited unusually large variation of their HbAic over the 6rmonth period of observation, we can claim that Algorithm 1 is predictive not only of relatively constant HbAu, but also of large and unusually rapid changes in HbAu. Along these same lines. Algorithm 1 would be most useful for patients who have their goal to optimize their HbAio, which is presumably the patient group most likely to be interested in purchasing a meter with advanced features such as continuous evaluation of HbAio. Summary of the Results ■ The optunal SMBG data collection period is 45 days; ■ The optimal frequency of SMBG is 3 reading per day; ■ Two optimal HbAio estimating functions were developed: Fl - using only SMBG data, Eind F2 - -using SMBG data plus an HbAi^ reading taken approximately 6 months prior to the HbAic that is being predicted; ■ The evaluation of the accuracy of HbAic prediction in the test data set {N=573 subjects) was done by several criteria that are detailed in the following pages (Table 2). Here we will mention that m TIDM the overall accuracy (within 20% of measured HbAic) of FI was 96.5% and the overall accviracy of F2 was 95.7%. For T2DM the overall accuracy of Fi was 95.9%, the overall accuracy of F2 was 98.4%. Thus, the accuracy of both Fl and F2 is comparable to a direct measurement of HbAic; ■ Most importantly, for patients whose HbAio changed 2 or more units from their baseline reading CN=68), the accuracy of F/ in predicting this change was 100% ui both TIDM and T2DM, while the accuracy of F^ was 71% and 85% in TIDM and T2DM respectively; ■ Both Fl and F2 provided substantially more accurate estimation of HbAjc at 6 months than Qie original HbAio estimate at month 0. Using liie average BG as a direct estimate of HbAic is not accurate as well; ■ A number of alternative approaches were tested, such as selecting specific times of the day (postprandial reading) for evaluation of HbAic, different weightmg of SMBG readings according to the elapsed time between each SMBG reading and HbAic determination, separate evaluation of subjects with different average blood glucose -to HbAic ratio, etc. While some of these alternative approaches achieved certain better results than the two functions proposed above, none was better overall. We can conclude that the optimal functions Fl and F2 will be used in future applications of AJgoritiiml. Detailed Results - Test Data Set Themostimportantpartof the evaluation of Algorithm 1 is the evaluation of its performance on a data that are not related to the data used for its development and optimization. From the test data set, the data of 573 subjects, N=254 with TIDM and N=319 with T2DM, were complete enough to be used for the evaluation of Algorithm 1. Optimal Aleoritkm 1: For each subject, a 45-day subset of his/her SMBG reading was selected. This subset had a starting date of approximately 75 days before the subject's 6-month HbAic'assay and ending date approximately 30 days before that assay. Since in this data set the time of HhAjc assay is imown only approximately, the elapsed time between last SMBG reading taken mto analysis and HbAic is not exact. This time period was selected through sequential optimization of its duration and its ending point (how long before HbAio). The optunal duration was 45 days. The optimal ending time was 1 month prior to HbA]c. In other words, a 45-day SMBG would predict the values of HbAic approximately one month ahead. However, the prediction of any other HbAic value between days 45 and 75 is almost as good - the differences are numerical rather than of clinical significance. Similarly, the difference between a 45-day monitoring period and a 60-day monitoring period is not great. However, monitoring periods shorter than 45 days cause a rapid decline ia predictive power. The optimal estimation functions are linear and are given by the formulas: Estimate 1 - Without prior knowledge of HbAic: Fl = 0.80909SBGMM1 + 0.064540LBGn - 0.151673RHI1 + 1.873325 Estimate 2 - Knowing a prior HbAic (appioximately 6 months ago). F2 =0.682742'HBAO + 0.054377RHI1 + 1.553277 In these formulas BGMMl is the average blood glucose computed from the 45 days of SMBG readings; LBGU and RHIl are the Low and the High BG hidices computed from the same readings, and HBAO is the baseline HbAic reading that is used for Estimate 2 only. The values of the coefficients are optimized using the training data set and relevant statistics and plots are presented in section Detailed Resuhs - Training data set. The fimctions Fl and F2 produce point estimates of HbAic, i-e. each function produces an estimated value of HbAio. Interval estimates can be obtained by using the regression error estimates presented in section Detailed Results - Training data set. However, applied to the test data set, these mterval estimates will not be tme 90% or 95% confidence intervals for HbAic because they are originally derived from the training data set and are only applied to the test data (see also the statistical note in the next section). Evaluation oftlie accuracy of Algorithm 1: Tables 2A and 2B present results from the evaluation of the optimal Algorithm 1 with data from the test data set for subjects with TIDM and T2DM, respectively. Several criteria were used: (1) Absolute deviation (AERR) of Estimated from measured HbAio; (2) Absolute percent deviation (PERR) of Estimated from measured HbAic; (3) Percent Estunates within 20% of measured HbAic (HIT 20), (4) Percentreadings within 10 ofmeasured HbAic (HIT 10), and (5) Percent readings outside of a 25%i-zone around measured HbAio (MISS 25). Table 2A: Accuracy of Alaorit im 1 in TIDM fN=254 subiects). Fl F2 Average BG Prior HbAu P-value AERR 0.77 0.61 1.68 1.1 PERR (%) 8.3 7.1 19.4 12.8 HIT20(%) 96.5 95,7 61.0 81.0 HIT10(%) 65.4 75.5 29.9 48.2 MISS 25 (%) 2.4 1.6 28.4 9.9 Table 2B; Accuracvof Aleorifhm 1 inT2DMn^=319 subiects). Fl F2 Average BG Prior HbAu P-value AERR 0.72 0.57 . 1.92 0.87 PERR (%) 7.6 6,4 20.9 U.7 HIT20 (%) 95.9 98.4 56.4 82.8 HITIO (%) 70.2 79.3 29.5 53.3 MISS 25 (%) 1.2 0.6 36.7 8.2 The first two columns in Tables 2A and 2B present the results for the optimal functions F2 and F2 respectively. The third column presents the accuracy of the estimation if the average BG (in mmolA) was taken as an estimate of HbAio. The fourth column presents the same accuracy measures computed using the HbAic assay at time 0 as an estimate of HbAjc at 6 months. It is evident that for both TIDM and T2DM F2 is a little better overall estunate of HbAic than FJ. Most unportantly, both Fl and F2 are substantially better estimates of HbAuthan its earlier value, or than the average BG. This is especially true for the % estimates that fell outside of the 25% accuracy zoiie. The difference between the performance of FZ and F2 and the estimate from a prior HbAic assay is highly significant (column 4). Statistical Note: It is important to note that it is not appropriate to evaluate the accuracy of Algorithm 1 using traditional, regression-type criteria, such as R^ or F and p values from ANOVA table. This is because the parameter estimates were derived fi'ora another unrelated data set (the training data) and are only applied to this test data set. Thus, statistical assumptions for the underlying model are violated (for example in the test data set the sum of the residuals will not be zero) and therefore R^, F, and p lose their statistical meaning. Further evaluation of the accuracy of Algorithm 1 in the test data set was done by reviewing the TIDM and T2DM subjects who had a substantial change in their SMBG readmg from the baseline to 6-month follow-up. Tables 3A and 3B present list of the TIDM and T2DM subjects who had an absolute change in their HbAic equal to or greater than2units. In each subject group 34 subjects had such a change in HbAic- Algorithm 1, function Fl, predicted 100% of such changes m both TIDM and T2DM. The predictive power of F2 was diminished due to the inclusion of baseline HbAic in the equation (vAich partially pulls the estimates back to the baseline value of HbAic) and was 71% in TIDM and 85% in T2DM. The baselme HbAio was outside of the 20% zone from 6-month HbAic for all but 2 subjects: o » l-'l-'l-' l-'l-'l-" l-'WF-'l-'l-' l-'l-'F-'t-' M l-'l-'l-'l-'l-'l-'l-'l-'l-'l-'l-'i-'l-' OLJOlDOl-'OUDOI-'l-'rOl-'VC't-'Ol-'l-'lDI-'UJOI-JNJWI-'t-'MNlLOl-'MOrO opouiCDUiii>a)uii-'i-'(jii-'uitowi-'oi>^--iLJl>jaj-JCOi-'OiLii-JUi-j(r>ioa)0 MMMNJtOrOtOt-JMMMhOMlOtOrONJWMMNJMMiSJMtJWiAlLOLOOJLJLJLn ooooi-'i-i-'fOPoMOJU)Li).^LnOia^a^a^-J'JcDcoiDvoor\>rjuJLOcji(ji^o oooooooooooooooooooooooooooooooooo \ou>LohJ\i)MromajmwuikDa\-jy3-Jcoco-j-JvDj&.ocoijiio[s)criDPOit»oaj ^DO'ClJ-JO^-■^Jl£Jlt^cD^Jcn^J\|-■^J^i>ootJ>f^ou>u^UlM^J >-• h-'l-'l-'l-JhJ}-' ^^ Ml-l-'l-'V-'l-»l-'l-» \i3\|-'^CO\Ol^^DCO'£)OOI-OtDO^DaU3COOCD^£>OOI-'OU^OI-'l-'kDOtOl^ ou)roLfiMcDi-'i^o'^-'^i-'-JOiON)C3LnijJiji-JLJOCOJ^inu3iomoiJi-Jcjjio M\|--'l--'l--'MI--'MMM>--'i--»Ml--'M\|--'l--'l--'l--'l--'l-->l--'MI--'l--'l--'l--'l--'l--'l--'F--'l--'f-'l--'1--' oooooooooooooooooooooooooooooooooo oooooooooooooooooooooooooooooooooo OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO oooooooooooooooooooooooooooooooooo a: en o B S' a CD o f» S rt 3. n s n re B. s- iq IS M D tq A B- rc •^ M Sf 1-3 »6 ■^ "» 1-1 !-■ M M !-■ l-'l-'l-'l-'F-'l-'l-'l-'l-'MI-'t-' o o o o o o o o o o o o I O O oooooooooooooooooo oooooooooooooooo 0O0C3CD000CDC30O0OOOO00O0OC5O000000C3000 oocsooooooooooaoooooooooooooooooooo o o oooooooooooooooocuooooooooooooooooo OOOOOOOOOOOODOOOOOOOOOOOOOOOOOOOOO H V i-a il "] »^ M e H h-« S" cc M ■^ Table 3B: T2DM subjects who experienced change in their HbAi, >= 1 units. ID HBAO HBA6 DHBA Fl F2 HIT Fl HIT F2 HI' HBAO 6754 10.8 7.0 3.80 ■ 6.90 9.03 100.00 .00 .00 6361 11.3 7.6 3.70 8.51 10.20 100.00 .00 .00 6270 12.0 8.6 3.40 7.85 10.03 100.00 100.00 .00 6264 11.1 7.8 3.30 8.31 9.70 100.00 .00 .00 6355 11.8 8.6 3.20 ." 7.99 9.90 100.00 100.00 .00 3961 10.8 B.O 2.80 9.13 9.73 100.00 ,00 .00 6555 11.1 8.3 2.80 B.ll 9.55 100.00 100.00 .00 8052 11.7 8.9 2.80 7.68 9.80 100.00 100.00 .00 5356 5.7 7.0 2.70 5.75 8.20 100.00 100.00 .00 3966 10.3 7.7 2.60 8.08 9.07 100.00 100.00 .00 908 9.5 6.9 2.60 ■7.47 8.23 100.00 100.00 .00 6554 10.7 8.1 2.60 8.16 9.42 100.00 100.00 .00 2353 11.1 8.7 2.40 8.99 9.90 100.00 100.00 .00 4064 11.3 8.9 2.40 7.89 9.88 100.00 100.00 .00 6351 10.1 7.7 2.40 7.92 B.63 100.00 100.00 .00 7551 12.2 9.8 2.40 9.17 11.02 100.00 100.00 .00 6358 8.4 6.1 2.30 7.00 7.32 100.00 .00 .00 3965 10.1 7.8 2.30 7.83 8.64 100.00 100.00 .00 914 11.1 S.8 2.30 9.57 10.33 100.00 100.00 .00 1603 10.2 7.9 2.30 8.02 8.88 100.00 100.00 .00 1708 10.8 8.6 2.20 7.62 9.24 100.00 100.00 .00 3761 12.4 10. 2 2.20 9.13 10.B6 100.00 100.00 ,00 37 68 11.2 9.0 2.20 8.29 9.74 100.00 100.00 .00 326 10.3 8.2 2.10 7.45 8.78 100.00 100.00 .00 109 9.3 7.2 2.10 7.70 8.18 100.00 100.00 .00 1501 11.9 9.8 2.10 8.52 10.18 100.00 100.00. .00 3964 13.7 11.6 2.10 10.08 12.65 100.00 100.00 100.00 4352 12.2 10.1 2.10 9.51 11.14 100.00 100.00 .00 7858 12.1 10.0 2.10 9.53 11.01 100.00 100.00 .00 4256 10.6 8.6 2.00 8.76 9.69 100.00 100.00 .00 47S2 10.1 8.1 2.00 8.51 a. 87 100.00 100.00 .00 6556 11.1. 9.1 2.00 8.72 9. 66 100.00 100.00 ■.00 65 62 7.9 5.9 2.00 7.07 7.04 100.00 100.00 .00 8255 10.9 8.9 2.00 8.90 9.87 100.00 100.00 .00 In Tables 3A aad 3B: ID - sul^ject's ID niOTiber; HBAO - baseline HbAic; HBA6 - measured 6-month HbAic; DHBA - absolute difference between baseline and 6-monl3i HBAic; Fl - Estimated HbAic by Function Fl, SMBG data only; F2 - Estunated HbAjc by Function F2 using prior HbAic assay; Hit Fl = 100 if Fi is within 20% of 6-montb Hbaic reading, 0 otherwise; Hit F2 = 100 itF2 is within 20% of 6-month Hbau reading, 0 otherwise, and Hit HbAO = 100 if baseline HhAic is within 20% of 6-month Hba:c reading, 0 otherwise, Detailed Results - Trainine Data Set This section describes the steps to optimization of Algorithm 1. This optimization included two parts; (1) Assuming that no previous HbAic reading is available, and (2) Assuming that a prior HbAic could be used for prediction of HbAio. Several different functions were considered for description of the relationship between SMBG data and HbAu. Optimal, in terms of accuracy and simplicity of computation, appeared to be a linear function of the average of SMBG readings. Low and High BG Indices, if no prior HbAlc reading is used and another linear function of a prior HbAlc and the High BG Index. Nonlinear relationships did not enhance the goodness-of-fit of the models and therefore are not coBsidered for practical application. Trainine Data set 1 -No prior HbAir A linear regression model was used to optimize the coefficient of function F2. The optimal coefEcients were presented in the previous sectioru Here we give data about the goodness-of-fit of the model: Multiple R .71461 R Square .51067 Analysis ofVariance DF Sum of Squares Mean Square Regression 3 154.57097 51.52366 Residual 90 148.10903 1.64566 F= 31.3Q8S9 SignifF= .0000 Analysis of the residuals of this model showed a close to normal distribution of the residuals (see FIG. 11). The SD of the residuals was 1.2 (the mean is 0 by definition). Therefore we can accept that this model described the data well. Training Data set 2 - Prior HbA !„■ Again, a linear regression model was used to optimize the coefficient of function F2. The optimal coefficients were presented in the previous isection. Here we give data about the goodness-of-fit of the model; Multiple R .86907 R Square .75528 Analysis of Variance DF Sum of Squares Mean Square Regression 4 38.70237 ' 9.67559 Residual 54 12.54000 .23222 F= 41.66522 SignifF= .0000 Analysis of the residuals of this model showed a close to normal distribution of the residuals (see FIG. 12). The SD of the residuals was 0.47. Therefore we can accept ■ that this model described the data well. In addition, comparing the models without and with a prior HbAl c, we can conclude that if a prior HbAlc is available for inclusion in.the computations, the resulting model is substantially better both in terms of R^ and in terms of residual error. However, as we saw in the previous section, a prior HbAic does not contribute to the overall accuracy of prediction in an unrelated dataset and, in certain cases when HbAlc changed substantially, is even obstructing the ability of the algorithm to account for rapid changes. Thus, we can conclude that, even if a prior HbAic maybe better from a statistical point of view, it may not have a sufficient practical utility to j ustify an inclusion of input of a reading in fiiture meters. We also don't know what could be the elapsed time between an HbAlc assay and SMBG profile that would still render the HBAlc input isefiil. Perhaps this depends on the change of HBAlc during that time period- as we aw in the previous section, a change of 2 HbAlc units makes a prior HbAl c reading ompletely useless. :he Ratio of SMBG to HbAu We will now present an alternative way to improve the statistical accuracy of the lodel fit and to keep a reasonable cimical ^plicability. It turns out that the ratio between tiie average of 45 days of SMBG readings and HbAk is a measure that has an almost perfect norma! distribution (as evidenced by Kolmogorov-Smimov test) and, most importantly, identifies three groups of subjects for whom this ratio is below 1.0, between 1.0 and 1.2,andabove 1.2. Each ofthe first two groups accounts for approximately 40% of the subjects, the third group accounts for approximately 20%) ofthe subjects. This is valid for both TIDM and T2DM and is observed in the traming as well as in the test data sets. In addition, this jatio seems to be pretty stable over time and is perhaps a measure that reflects patients SMBG habits (for example, if SMBG is performed mostly at times when BG is low, the resulting average will underestimate HbAic and the corresponding ratio will bebelow 1.0). Keeping in roind that this is just a hypothesis that cannot be validated with the available data, we make some analyses that seem to demonstrate certain utility in knowing each person's ratio at some point of time. This may seem equivalent to knomng a prior HbAic, and it is perhaps equivalent in terms of data input, however the me of th6 ratio is very different than the use of a prior HbAio.' Instead of being included directly into the prediction formuja, the ratio is used to classify the person into usii^ one of three different prediction formulas. These new formulas do not include HbAio directly and therefore do not suffer by the inertia of prediction that such inclusion may cause, hi addition the average HbAu is not substantially different between the three groups defined by the ratio and is not correlated to the ratio, so the reason for different ratios in different persons must be imrelated to HbAio. If we first classify the subjects in three groups according to thek ratio and perform separate regression in the training data set, the goodness-of fit ofthe regression models increases substantially: (1) In group 1 (Ratio 1.2) the fit is worst R=0.69, R^=0.47. Since all three re^ssion models do not include a prior HbAio, we can conclude that the goodnessnsf-fit mcreases dramatically for about 80%i ofthe subjects, remains the same for the rest 20% of the subjects, and these subjects for whom the fit will be worse can be identified in advance. Further, separating the test data set in three groups according to the subject's ratio, we get prediction accuracy similar to the accuracy we have achieved before (Tables 4A and 4B): Table 4A! Accuracy of Aleorilhm t in TIDM fN=254 subjects'). Ratio 1.2 AERR 0.70 0.63 ,0.74 ■ PERR (%) 7.8 7.4 7.9 H1T20 (%) 93.8 93.0 95.5 -— HITIO (%) 68.8 73.4 72.7 MiSS25(%) 3.1 2.6 0.0 Table 4B: Accuracy of Akorithm I inT2DM n^=319 subiectsl. Ratio 1.2 AERR 0.63 0.68 0.89 PERR(%) 7.6 7.8 8.8 HIT20{%) 97.4 95.0 95.3 HITIO (%) 67.2 65.3 57.7 MISS 25 (%) 0.0, 1.7 0.0 , In short, knowing the SMBG-to HBAlc ratio for each subject and using separate estimates accordingly, seem to improve the statistical performance of the models without losing clinical accuracy. Other hypotliesea and ideas that were tested We have tested a number of other hypotheses and ideas which may prove useful at least for prompt and more focused analysis of the data that are collected by Example No. 2, A brief account of the results follows: (1) HbAic is most associated (correlated) with SMBG readings taken in the afternoon hours - from 12 noon to 6 p.m. and least associated with fasting SMBG readings (4 a.m. - 8a.m.). However, it does not follow that taking only postprandial SMBG readmgs would improve the prediction of HbAic. on the contrary, the prediction would become worse if the [relatively small but important] contribution of al! hours throughout the day is ignored. It is possible to unprove a the prediction of HbAic a little if different hours throughout the day get different weighting, however the improvement is not sufEcient to justify this additional complication of the model; (2) The relationship between HbAie and average SMBG is substantially stronger ,m T2DM compared to TIDM, even if the two groups are matched by HBAie. In terms of direct correlation, in TIDM the coefficient is about 0.6 while in T2DM the coefficient is about 0.75 throughout the studies; (3) Experiments with different weighting of SMBG reading dependent on the elapsed time between SMBG and PBAic assay (such as weightmg higher more projcimal results) did not yield better prediction of HB Aic; (4) Inclusion of demographic variables, such as age, duration of diabetes, gender, etc., does not improve the prediction of HBAlc; (5) The simplest possible linear relationship between HbAic and average SMBG (measured in mmol/1) is given by the formula: HbAic = 0.41046BGMM + 4.0775. Although statistically inferior to the formulas Fl and F2, this formula provides HbAic estimates that are about 95% accurate in both TIDM and T2DM (in terms of deviation less than 20% from HbAic assay) and maybe usefiil if the computation of the Low and High BG Indices presents an issue for incorporation in a meter (however, the prediction of hypoglycemia cannot be done without computing the Low BG Index and therefore this formula might be usefiil only for meters that include Algorithm 1 but not include Algorithms 2 and 3). ALGORITHM 2; EVALUATION of LONG-TERM RISK for SH. Example No. I provides for, but not limited thereto, an expansion of Algorithm 2 to include estimating individual probabilities for biochemical significant hypoglycemia (BSH, defined as BG reading Algorithm 2 is a classification algorithm. That is, based on SMBG data for a subject, it classifies the subject in a certain risk category for future BSH or MSH. In order to approxunate as closely as possible future real applications of Algorithm 2 we proceeded as follows: (4) First, several optimal classification variables and optmial classification categories optimal duration, and optimal fi^quency of SMBG were derived^fi:om training data set 1; (5) Then, the test data set was split into two sections: fmst 45 days, and the rest of the data. The optimal parameters of Algorithm 2 were applied to the first 45-day portion of the data and the so estimated probabilities for fiiture BSH or MSH were used to predict BSH and MSH in the second portion of the data; (6) Detailed estimation of the preciseness of Algorithm 2, was made using test data only. This separation of training and test data sets allows us to claim that the estimated preciseness of Algorithm 2 can be generalized to any other data of subjects vwth TIDM or T2DM. Moreover, since the Amylm data were collected from subjects who were undergoing intensive treatment, we can speculate that Algorithm 2 is tested and proven useful in subjects with changing and increasing risk for hypoglycemia. Summary of the Results ■ The optunal SMBG data collection period needed for estimation of the probabiHty for fimire BSH or BMH is 40 to 45 days. The optimal frequency of SMBG is 3 to 4 readings per day. Larger number of readings does not lead to a substantial increase of the predictive power of Algorithm 2. With less than 3 reading per day the predictive power declines. However, this requirement refers to average number of readings per day for the 45-day observation period, it does not necessarily mean that 3-4 readings need to be performed every day; ■ The relationship between predictor variables and future SH and MH is strictly nonlinear. Consequently, linear methods are not applicable for optimal prediction, although an R^=50% can be achieved by a direct linear model (in comparison, the best result in the DCCT was 8% prediction of future SH); • A separate prediction of nocturnal SH is generally weaker than prediction of daytime SH; ■ Fifteen risk categories for future BSH and BMH were identified. The best separation of categories was achieved on the basis of the Low BG Index alone, although combinations between the low BG Mdex and other variables worked similarly well; ■ Although the frequencies of BSH and BMH were different between TIDM and T2DM (see Table 5), the conditional frequencies, given a risk category, were not different between TIDM and T2DM. This allowed for xmified approach to the risk of SH and MH; ■ Various empirical probabilities for future were computed and compared for the 15 risk categories. All comparisons were highly significant, p's ■ These empirical probabilities were approximated by a two-parameter WeibuU distributions yielding theoretical probabilities for future BSH and BMH in each risk category. • The goodness-of-f!t of these approximation was very good - all coefficients of determination were above 85%, some as high as 98% (see FIGS. 1-5 and 9-10). Detailed Results - TestDgtaSet Identifvine personal risk cateeories for SH/MH: The data for aU 600 subjects were used for these analyses. The Low BG Index (LBGI was computed for each subject from his/her first 45 days of SMBG data collection. Then, the LBGI for was classified in one of the 15 optimal risk categories (variable RCAT rangmg from 0 to 14) as derived in training data set 1. These rislc categories are defined by the inequalities: if(LBGIle0.25)RCAT=0. if (LBGI gt 0.25 and LBGI le if (LBGI gt 0.50 and LBGI le if (LBGI gt 0.75 and LBGIle if(LBGIgt 1.00 andLBGIle if (LBGI gt 1.25 and LBGIle if(LBGIgt 1.50 and LBGIle if (LBGI gtl.75 and LBGIle if(LBGIgt 2.00 and LBGIle if(LBGIgt 3.00 and LBGIle if(LBGlgt 3.50and LBGIle if (LBGI gt 4.00 and LBGI le if (LBGI gt 4.50 and LBGI le if (LBGI gt 5.25 and LBGI le if (LBGI gt 6.50) RCAT=14. 0.5)RCAT=1. 0.75) RCAT=2. 1.00)RCAT=3. 1.25)RCAT=4.- 1.50)RCAT=5. 1.75)RCAT=6. 2.00) RCAT=7. 2.50)RCAT=8. 3.50)RCAT=9. 4.00)RCAT=10. 4.50)RCAT=11. 5.25)RCAT=12. 6.50)RCAT=13. Observed frequency of BSH and BMH: For each subject, any occurrences of BSH and BMH registered by SMBG were counted for 1-month, 3-month, and 6-month periods following the initial 45-day data collection, Table 5A presents the observed frequencies of 0, >= 1, >=2. and >= 3 B SH and BMH for T1 DM, Table SB presents the same data for T2DM: Table 5A: Observed freauencv of BSH and BMH in TIDM BSH(BG 1 month 3 months 6 months 1 month 3 months 6 months Average #/Subject 0.82 1.77 2.74 3.64 8.33 12.93 % Ss with 0 episodes 62.8 50.8 46.6 25.2 18.0 17.7 % Ss with > 2 episodes 18.8 33.1 38 64.3 75.6 77.1 % Ss with > 3 episodes 9.8 23.3 28.2 50.8 68.0 71.1 Table 5B: Observed freauencv of BSH and BMH in T2DM BSH(BG I month 3 months 6 months I month 3 months 6 months Average # / Subject 0.18 0.53 0.76 1.11 2.93 4.59 % Ss with 0 episodes 91.4 84.9 81.3 73.0 61,1 55.8 % Ss with > 2 episodes 3.6 8.6 10,1 18.1 26,7 30.3 % Ss with > 3 episodes 1.5 5.9 7.4 13.9 21.4 25.8 Nocturnal BSH and BMH represented approximately 15% of all episodes registered by SMBG. As in the training data set the correlation between nocturnal episodes and all predictor variables was weaker. We conclude Ihat a targeted prediction of nocturnal episodes would be ineffective. Enmirical Pr^babUUies for future BSH and BMH: Certain empirical probabilities for fiiture BSH and BMH were computed in each of the 15 risk categories. These probabilities include: (1) Probabilities for at least one BSH or BMH within the next 1 month, 3 months, and 6 months; (2) Probabilities for at least two BSH or BMH within the next 3 months and 6 months, and (3) Probabilities for at least three BSH or BMH within the next 6 months. Of course, it is possible to compute any other combinations probabilities upon request. A most important conclusion &om this analysis was that, given a risk category, the probabilities for future BSH and BMH did not differ significantly between TIDM and T2DM. This allows for an unified approach to empirical and theoretical estimation of these probabilities in both TIDM and T2DM. Consequently, flie data for TIDM and T2DM patients were combined for the following analyses. Fl presented by black triangles, Virhile the empirical probabilities for BMH are presented by red squares. All sets of empirical probabilities were compared across the 15 risk categories using univariate ANOVAs, and all p-levels were below 0.0005. Therefore, we observe highly significant differences between the fi-equencies of BSH and BMH episodes in the different risk categories. Theoretical Probabilities for future BSH and BMH: Jn order to be able to use direct-formula estimation of the probabilities for future BSH and BMH, we approximated the empirical probabilities using two-parameter Weibull probability distribution. The Weibull distribution fimction is given by the formula: F(x) — 1 - expf-a.x') for any x>0 and 0 otherwise Statistical Note: The parameters a and b are greater than 0 and are called scale and shape parameter, respectively. ID the special case b=l, Weibull's distribution becomes exponential. This distribution is fi^quently used in engineering problems as the distribution of randomly occurring technical failures tiiat are not completely unrelated to each other (If the failures are completely unrelated, then they would form a Poisson process that would be described by an exponential distribution, e.g. b=l). The situation here is remotely similar - we need to describe the distribution of events (failures) that are not completely independent and tend to occur in clusters as evidenced by our previous research. Each set of empirical probabilities was approximated by the theoretical formula given above. The parameters were estimated using nonlinear least squares (with initial parameter estimates given by a linear double-logarithmic model). The goodness-of-fit of each model was evaluated by its coefficient of determination (D^). This statistics has a meaning similar to that of R^ in linear regression, however R^ is not applicable to nonlinear models. . The model fits are presented in FIGS. 1-6 as black Iraes for the probabilities of BSH and as dashed lines for the probabilities of BMH. Above each figure we present the parameter estimates for the corresponding models, thus we give direct formulas for computing probabilities of 0, >=1, >=2, >=3 BSH or BMS episodes jn periods of 1 month, 3 months, and 6 months following initial SMBG. Some of these formulas, or their versions, can be included in monitoring devices or software as indicators of risk for SH andMH. The values ofD^ (and its square root D) are given below each figure as indicators ofthepreciseness of approximation. All values are above 85% and some reach 98%, which demonstrates that the approximation is very good and confirms that theoretical, instead of empirical probabilities could be used in future studies/appUcation. The theoretical probabilities for one or more moderate or severe hypoglycemic episodes are given by the formulas as shown in FIG. 1: P (MH >= 1) = 1 - exp (-exp (-1.5839) Risk** 1.0483) P (SH >= 1) = 1 - exp (-exp (-4.1947) * Risk** 1.7472) FIG. 1 presents the empirical and theoretical probabilities for moderate (dashed line) and severe (black line) hypoglycemia within one month after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index. Since the models are nonlinear, the goodness-of-fit is evaluated by their coefficient of determination D^, an analog of R^ in linear models. The coefficients of determination and their square roots are as follows; SH Model: D^ = 96%, D=98%. MHModel: D'= 87%, D = 93%. The theoretical probabilities for one or more moderate or severe hypoglycemic episodes are given by the formulas as shown in FIG. 2: P(MH>=l) = l~exp(-exp(-I.373I)* Risk** 1.1351) P (SH >= 1) = 1 - exp (-exp (-3.2802) * Risk** 1.5050) FIG. 2 presents the empirical and theoretical probabilities for moderate (dashed line) and severe (black line) hypoglycemia within three months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index. The coefScients of determination and their square roots are as follows: SH Model: D^ = 93%, D=97%. MH Model: D^ ^ 87%, D = 93%. The theoretical probabilities for one or more moderate or severe hypoglycemic episodes are given by the formulas as shown in FIG. 3: P (MH >= 1) = 1 - exp (-exp (-1.3721) * Risk** 1.3511) P (SH >= 1) = 1 - exp (-exp (-3.0591) * Risk** 1.4549) FIG. 3 presents the empirical and theoretical probabilities for moderate (dashed hne) and severe (black line) hypoglycemia within six months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index. The coeiBcients of determination and their square roots are as follows: SH Model: D^ = 86%, D=93%. MH Model: D^ = 89%, D = 95%. The theoretical probabilities for two or more moderate or severe hypoglycemic episodes are given by the formulas as shown in FIG. 4: P (MH>= 2) = 1 - exp (-exp (-1.6209) * Risk** 1.0515) P (SH >= 2) = 1 - exp C-exp (-4.6862) * Risk** 1.8580) no. 4 presents the empirical and tiieoretical probabilities for 2 or more moderate (dashed line) and severe (black line) hypoglycemic episodes within three months after the SMBG assessment for each of the' 15 categories of risk level defined hy the Low BG hidex. The coefficients of determination and their square roots are as follows: SH Model: D* = 98%, D=99%. MH Model: D* = 90%, D - 95%. The theoretical probabilities for two or more moderate or severe hypoglycemic episodes are given by the formulas as shown in FIG. 5: P (MH>=2) = ] -exp (-exp (-1.7081) * Risk** 1.1955) P (SH >= 2) = 1 - exp (-exp (-4.5241) * Risk** 1.9402) FIG. 5 presents the empirical and theoretical probabilities for 2 or more moderate (dashed line) and severe (black line) hypoglycemic episodes within six months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index, The coefScients of determination and their square roots are as follows: SH Model: D^ = 98%, D=99%. MH Model: D^ = 89%, D = 95%. The theoretical probabilities for three or more moderate or severe hypoglycemic episodes are given by the formulas as ahovm in FIG. 9: P (MH >= 3) = 1 - exp (-exp (-2.0222) * Risk** 1.2091) P (SH >= 3) = 1 - exp (-exp (-5.5777) * Risk 2.2467) FIG. 10 presents the empirical and theoretical probabilities for 3 or more moderate (dashed hne) and severe (black line) hypoglycemic episodes withm six months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index. The coefBcients of determination and their square roots are as follows: SH Model: D^ = 97%, D=99%. MH Model: D^ = 90%, D = 95%. Detailed Results - Trainine Data Set The training data set contained SMBG data followed by monthly diaries of severe hypoglycemia. As opposed to the test data set where BSH and BMH were identified by cutoff BG values, the monthly diaries contained report of symptomatic severe episodes defined as unconsciousness, stupor, inability for self-treatment, or significant cognitive impairment due to hypoglycemia. Within 6 months following SMBG the subjects reported on average 2.24 such episodes per person with 67% of the subjects reporting no such episodes. From a statistical point of view, ihis alone makes fee distribution of SH episodes substantially skewed and unsuitable for application of linear methods. Nevertheless linear regression could be used to evaluate the relative contribution of various variables to the prediction of SH, but not for building the final model. We performed the following three analyses; (1) No knowledge of SH history: Ignoring any knowledge of history of SH, we used regression to predict future SH from baseline HbAic and SMBG characteristics such as average BG, Low BG Index, and estimated BG risk rate of change (all variables are described in the original invention disclosure). As repeatedly found before, HbAic and average BG did not have any contribution to the forecast of SH. The final regression model included the Low BG Index and the BG risk rate of change and had the following goodness-of-fLt; Multiple R .61548 R Square .37882 Rnalysis of Variance F - 27.74772 Signif F.= .0000 —. Variables in the Equation Variable B SE B Beta T Sig T LBGI 4.173259' .649189 2.104085 6.428 .0000 RATE -5.749637 '1.091007 -1.724931 -5.270 .0000 (Constant) -2.032859 .790491 -2.572 .0117 (2) Knowledge of prior SH: When we included the number of SH episodes in the previous year as reported in a screening questionnaire, this variable accounted for an additional 11% of the variance of ftiture SH: Multiple R . .70328 R Square .49461 Analysis of Variance F = 29.35999 Signif F = .0000 Variable B SE B Beta T Sig T SH .337323 .074286 .375299 4 .541 .0000 LDR -4.350779 1.036380 -1.305264 -4 .198 .0001 RLO 3.134519 .631684 1.560371 4 ,962 .0000 (Constant) -2.136619 .717334 -2. ,979 .0037 (3) Without knowledge of the number of prior SH, just knowing whether a person had or did not have prior SH, we were able to account for 45% of ftie variance of future SH using only SMBG variables; (4) Finally, two separate linear models accounted for 55% of the variance in daytime SH vs. 25% of the variance in nocturnal SH. The direct correlations of all predictor variables with nocturnal SH were also weaker. Nocturnal episodes represented 30% of all SH. We conclude that a linear predictive model could directly account for about 40 to 50% of the variance of future SH. However, such a model is not well balanced in terms of its residual errors (which is due to the highly skewed distribution of die number of SH episodes across tiie diabetic population). A statistical evidence for that is given by the normal probability plot of FIG. 13, which shovfs a substantial deviation of the standardized residuals from their expected values: Thus, we adopt anodier approach to predicting SH based on classification of subjects mto risk categories using their SMBG data and estimation of the probabilities for subsequent SH in these categories. We attempted various classification models maximizing the difference between the risk categories and trying to achieve a maximum resolution of risk evaluation (in terms of maximal number of categories). The best results were achieved by the classification based on the Low BG Index alone that had 15 risk categories (presented in the beginning of the previous section). In addition to its best separation between categories, this result has other advantages as well: (1) No prior knowledge of history of SH is required; (2) The calculation is relatively simple and does not require tracking of temporal variables such as BG rate of change, and (3) The classification appeared to be equally applicable to both TIDM and T2DM patients (which is coherent with no requirements for knowledge of prior SH). ALGORITHMS: EVALVATION of SHORT-TERM RISK FOR HYPOGLrCEMU Example No. 1 provides for, but not limited thereto, an optimization of Algorithm 3 in terms of: (1) Utilization of baseline long-term risk (^m Algorithm 2) and HbAio (fcom Algorithm 1); (2) Risk criterion/threshold for hypoglycemia alert; (3) Frequency of SMBG; (4) Whether a hypoglycemia alert should be issued if an mcreased risk for hypoglycemia is detected and there is no SMBG for certain period of time, and (5) Contribution of demographic variables such as history of severe hypoglycemia. Introduction As opposed to Algorithms 1 and 2, which have a longer history of development, Algorittun 3 deals wth a proposition ftrnt was, until recently, considered impossible, hi fact, there is still a general perception that prediction of any future BG value (hypoglycemia in particular) is not possible on the basis of previously known values (Bremer T and Gough DA. Is blood glucose predictable from previous values? A solicitation for data. Diabetes, 1999,48: 445-451.). Our previous work, reported in one manuscript and presented in detail in the invention disclosure available to Ltfescan, Inc. disputes this general perception. In order to explain the basis for this dispute and to clarify the reasoning behind Algoiitkn 3, we inclucle the following paragraph. Our "philosophy" in auantifyitte characteristics of diabetes: Hormonal interactions are governed by dynamic-control biochemical networks that have a more or less complex structure of principal nodes and conduits, depending on the studied endocrine system. Diabetes disrupts the network control of insulin-glucose dynamics at various levels. For example, in TIDM the natural production of insulin is completely eliminated, while in T2DM the utilization of insulin in the cell is obstructed by a greater insulin resistance. In TIDM (and frequently m T2DM) some form of external insulin replacement is required, which makes the control system vulnerable to imperfect external factors, including the timing and amount of pill or insulin injection, food eaten, physical activity, etc. This frequently leads to extreme BG excursions into hypoglycemia and hyperglycemia. In many, but not in all cases, hypoglycemia triggers an endocrine response, known as counterregulation. Thus, in mathematical terms, BG fluctuations over time are the measurable result of the action of a complex dynamic system, influenced by a number of internal and external factors. However, it is well known from the theory of dynamical systems that when the complexity of control increases, a purely deterministic system evolves to display random macro-behavior. Consequently, within short periods of time (minutes) the BG fluctuations observed at a human level would be nearly-deterministic, while over longer periods of time the fluctuations woidd be nearly-random, including extreme transitions, such as SH episodes. Thus, stochastic modeling and statistical inference are most appropriate for analysis of the system over longer periods of time - a paradigm adopted by Algorithms 1 and 2 that use our originally developed measures, such as the LBGI and HBGI, to predict, after a certain observation period, a range of values, or a probability of an event. Over short periods of time BG fluctuations can be modeled and predicted using deterministic networks, which would be the case with future intelligent insulin-delivery devices linked to continuous monitoring. Algorithm 3 operates in an mtermediate time scale of a few hours to a few days and therefore requires a combination of statistical inference and deterministic modeling. The former will be used to assess the baseline risk for SH for an individual, while the latter will be used for a dynamical tracking of individual parameters and forecast of SH spisodes prior to their occurfence. When implemented in a device Algorithm 3 would ivork as follows: ;1) The device collects certain baselme information for the subject and establishes individual baseline pararneters; [2) Then, the device begins tracking a certam set of properties of the SMBG data; '3) The device is equipped with a decision-making rule ttiat decides when to raise a flag for upcoming SH and when to lower this flag if the data indicate that the threat is reduced; 4} When the flag is raised, we assume that the subject is warned for SH in the following 24 hours (prediction time). [his dynamical prediction creates theoretical problems at both the level of model larameter optimization and at the level of evaluation of the preciseness of the optimal solution. We will begin with clarifying the second problem, as it is most important for understanding the action of Algorithm 3. Evaluatins the preciieness {fAlEorithm 3: While Algorithms 1 and 2 employ a static forecast and the criterion for evaluation of these algorithms is theoreticaily apparent - a better predictive value, with Algorithm 3 the optimization criterion is no longer straightforward. This is because by increasing the percentage of predicted SH episodes, we unavoidably increases the number of "raised flags," which in turn increases the number of potential "false alarms." The matter is additionally complicated by the fact that a "felse alarm" is not clearly defined. In its pure form, a false alarm would be a raised flag that is not followed by and SH episode. However, SH could be avoided if the person perceives symptoms and takes an appropriate action. Thus, even if the biochemical potential for SH may present, an event may not occur. In order to deal with this problem we adopt the following optimization criterion: (1) Maximize the prediction of upcommg SH within 24 hours; (2) Minimize the ratio Rnd of duration periods of "flag up" to "flag down". While the first of these two pomts is clear, the second may need an additional explanation. Looking fixim the perspective of an implementation of Algorithm 3 in a meter, at every SMBG determination the meter decides whether to r^se a flag or not to raise a flag for upcoming SH. When the flag is raised, it may stay up for some time (along several subsequent SMBG readings) imtil a decision is made to take the flag down. Thus, we will have an altematmg process of "flag up" and "flag down" with the changes happening at points of SMBG. The ratio Rud referred to in point (2) above, is the average tune for a person, counted while the flag is up, divided to the average time counted while the flag is down. Our previous best result presented in the Invention disclosure was a prediction of 44% of SH episodes within 24 hours, and Rud=l:7, e.g. one day of high-risk alert was alternating with 7 days of no alert. Since at that time we assumed that the warning period was at least 24 hours, the algorithm was optimized to raise a flag no more frequently than once a week. Given that this analysis was done using data for subjects who were experiencing high rate of SH episodes, this ratio was considered acceptable. During Example No, 1 of this study we had to use the same data set for refinement of Algorithm 3 since there is no other data available that include simultaneous SMBG records and records of SH. We also used a similar criterion to evaluate the preciseness of Algorithms. However, v/e changed substantially everything else. The tracking of the data, the parameter estimation, all threshold values and the decision-making rule are no longer the same. These changes were caused by a new idea that SH is preceded by cert^ "depletion" of the body's reserves to counterregulate and that tins depletion can be tracked by using SMBG data. ■ The exact implementation of this idea is described in the section "Decision-making Rule." Since the decision-making rule involves a continuous criterion and a somewiiat artificial cutoff, several solutions are presented and one is selected as optimal for fiirther investigatidn. However, upon presentation of these results, we may decide to select anottier solution to be implemented in future applications of Algorithm 3. Summary of the Results First, it is important to note that all results presented helow go well beyond statistical significance. As we will see in a few examples in the next section, the observed differences are always highly significant (with p-values below any imaginable significance level). The point of Algorithm 3 is to predict occurrence of SH episodes on an individual basis. The results are; (1) The minimum baseline observation period is 50 SMBG readings taken over approximately two weeks with a frequency of 3-4 readings a day. After this time each subject is classified in one of two risk groups that later use different decision-making rules; (2) From the 6 months of data that we have we find that it is sufficient to make this group assignment once in the beginning of observation. Thus, we can assume that about every 6 months the meter would use 50 reading to reevaluate its owner's group assignment; (3) The optimallag of SMBG tracking is 100 to 150 readings taken with a frequency of 3-4 readings per day. In other words, the optimal decision-making criterion would be based on a computation using all 150 readings in a meter's memory. This was done to simulate the memory capacity of ONE TOUCH ULTRA. In general, good results are achieved using a lag of only 20 readings taken over a week, but a longer lag yields better prediction; (4) The decision-making rules is based on a new computational procedure that tracks subjects Low BG Index and other related parameters using "provisional mean" computation. Special software was designed to implement this procedure and to process the data that we had available. From a programming point of view, the code needed for implementation of this procedure is only about 20 lines, which includes the computation of the LBGI; (5) Several decision-maldng rules (using various parameters) were investigated. Regardless of the frequency of SMBG, thfese rules achieved prediction of SH within 24 hours anywhere fi-om 43.4% with Rud=I :25 to 53.4% with Rud=l :7. Thus, compared to our previous result, the prediction of SH within 24 hours increased by 10%; (6) As an optimal solution for further investigation we choose the decision-making rule that predicted 50% of SH within 24 hours and had R„d=l: 10. The followmg results refer to this optimal solution under different conditions: (7) The optimal frequency of SMBG is 4 readings per day. If this frequency is achieved, the prediction of SH within 24 hours mcreases to 57.2% with the same Rud=l:10. Other frequencies of SMBG are investigated and reported as well; (8) Ifweextendthepredicitionperiodto36, or 48 hours, the prediction of SH increases to 57% and 63% respectively, with the same Rud=l:10; (9) Utilizing baseline information increases substantially the prediction of SH. In fact, the 10% increase over our previous version of Algorithms is entb:ely due to the use of baseline tracking. However, this baseline tracking is now modeled as a two-week period of self-calibration of the meter that does not use any additional input from the subject; (10) Personal/demographic information, such as history of SH or prior HbAic, does not contribute to a better short-term prediction of SH; (11) Raising a flag whenever there is a prolonged period of no SMBG activity is not justified. The only times when the meter would issue warning for upcoming SH would be the times of usage. This is because a major part of the prediction of SH is based on the recurrence (clustering) of very low BGs. An assessment of this recurrence is presented in an abstract (Kovatchev et al. Recurrent Hypoglycemia and Severe Hypoglycemia (SH) in TIDM Patients With History of Multiple SH) prepared for the June 2002 ADA meeting (See Appendix). Detailed Pescription of the Data Processing The meter stores SMBG readings together with the date and exact time (hour, minute, second) of each reading. Thus, in Training Data set 2 we have for each subject a certain temporal sequence of SMBG records. During the study, a total of 75,495 SMBG readings (on average 4.0±1.5 per subject per day) were downloaded &om the participants' memory meters. From subjects' monthly diaries, we had the date and time of SH episodes that had occurred. Subjects reported 399 (4.7±6.0 per subject) SH episodes. Sixty-eight (80%) of the participants experienced one or more episodes of SH. These subjects did not differ from those who did not experience SH (the remaining 20% of the subjects) in terms of any of their demographic characteristics. Pre-ProcessinB ofihehata: Special software was developed iorpre processing of the data. This included: (1) Assembling of the memory meter data for each subject into a continuous 6-8-month sequence of BG readii^s, and (2) Matching of each subject's records of SH with this sequence by date and time, The latter was performed as follows: for each SMBG reading the time (hours/mmutes) until the nearest SH episode, and the time elapsed &om the latest SH episode, were computed. Thus, it was possible to: (1) time 24-hour, 48-hour, etc. periods backward and forward from each SH episode, and (2) time periods between SMBG readings. Due to the nature of SH (stupor, unconsciousness), no SMBG was performed exactly at the time of SH, thus SH episodes for flie purposed of Algorithm 3 do not include biochemical significant hypoglycemia that was used for Algorithm 2. The average per SH episode minimum elapsed lime between SH and the nearest preceding SMBG reading was 5.2±4.1 hours; 29 SH episodes (7%) were preceded by a SMBG readmg within 15 minutes. For each SH episode, we counted how many SMBG readings were performed within 24h, 36h, 48h, and 72h prior to that episode. Computing of Baseline Risk Values and Self-Calibration: The Low BG Index for each subject is computed on his/her first SNBG readings. It was determined that the minimum numbei of reading required to compute a baseline LBGI is SO taken over approximately 2 weeks. Therefore for each new meter we need to anticipate an initial two-week self-calibration period during which the meter would be scanning the overall risk for SH of its owner. After the initial period, the person is classified into one of two risk groups: Low-moderate risk (LBGI 3.5, MH Group), Our test data show that a more precise classification would not fae necessary. This classification allows for different decision-making rules to be used in the LM and MH groups and raises the hit rate of the algorithm by approximately 10% as compared to its original hit rate presented in the invention disclosure. With the test data re-calibration of the b^eline risk was not necessary. Thus, we can assume that if the person does not undergo changes in treatment, re-calibration would be performed approximately every 6 months. This is consistent with the results of Algorithm 2 showing that the long-term prediction of SH is quite valid for 6 months after the initial observation period. However, if the person experiences rapid changes in his/her glycemic control, re-calibration maybe required more frequently. The decision for re-calibration can probably be automated and based on observed increasing differences between the running risk value (see the next paragraph) and the baseline LBGI. However, the available data do not allow us to clarify this issue suice the subjects that we observed did not have substantial changes in their risk for hypoglycemia. Computitts SMBG Parameters: After the pre-processing step, another piece of software was designed to compute SMBG parameters that ■would be used for prediction of imminent SH. This software included; (1) Computing of a Low BG Risk value (RLO) for each BG readii^ that is done by the following code (here BG is measured in mg/dl, if the units are mmol/l the coefficients are different): scale=(ln(bg))!.08405 - 5.381 ri3k=22.7653calescalG if [bg_l le 112.5) then RLO=risk else RLO=0 endif (2) For each SMBG reading with a sequential number n, BG(n), computing of a running value of the LBGI(n), and another statistics, SBGI(n) that is the standard deviation of the low BG risk values. These two parameters were computed with a certain lag (k) backwards from each SMBG readmg, e.g. included that readmg, BG(n), and (k-1) readings taken prior to BG(n). (3) The computation of LBGI(n) and SBGI(n) used a new provisional means procedure that is based on the following recursive code: Initial values at n-k (or at the max (l,n-k) to be exact in order to account for meter readings with a sequential number less than k): LBGI (n-k) = rlo (n-k) rio2(n-k)=0 Values for any consecutive iteration/' between n-k and «: LBGI (i) = ((i-iyj)LBGI G-l) + (l/j)RLO Q) rlo2 0) = {Q-mrrhl Q-l) + (l/j)(RLO (j)-LBGI Q)) 2 After this cycle is completed we have the value of LBGI (n) and we compute SBGI (n) = sqrt (rlo2 (n)) Since the maximum of n is 150 for ONE TOUCH ULTRA meters, the search for an optimal lag ^ was performed within the range of k=10 to k=150. Although the difference in performance was not sipificant, the optimal lag was determined to be k-150 (see the next section for examples). Decision-Makine Rule: At each SMBG reading the procedure decides whether to raise a flag vraming for upcoming SH, or not. If the flag is raised, the procedure decides whether to bring it down. These decisions depend on three threshold parameters, a, p, y that work as follows: For subject at low-to-moderate risk (LM group): FLAG=0 if (LBGI(n) > a and SBGI(n) > P) FLAG=1 if (RLOm) > (LBGI(n) + 7SBGI{n))} FLAG=1 For subjects in the moderate-to-high risk group only the second if-statement is active. In other words, the flag is raised (e.g. becomes equal to 1) if both the running value of LBGI(n) and its standard deviation SBGI(n) exceed certain threshold values, and is also raised if the current value of the low BG risk RLO(n) exceeds the value of LBGI(n) plus y standard deviations. An heuristic explanation: The values of LBGI(n) and SBGI(n) reflect slower changes in risk for hypoglycemia - it takes a few days of SMBG to substantially change these values. Since elevated LBGICn) means more ftequent and extreme recent hypoglycemia, we can conclude that LBGI(n) and SBGI(n) reflect a persistent depletion (or lack of replenishment) of counterregulatory reserves over the course of several days. In addition, SBGI(n) is a marker of the stability of the system - a larger SBGI(n) indicates that a subjects' BG fluctuations increase and therefore the control system becomes unstable and vulnerable to extreme aberrations. Thus, the first logical expression reflects the notion that SH occurs whenever the couterregulatory defenses are exhausted and the controls (external or internal) become unstable. The second logical expression accounts for acute changes in the low BG risk, triggering a flag whenever the current Low BG risk value suddenly becomes greater than its running average. The fact that for subjecis in the moderate-to-high risk group only the second logical expression is relevant goes along with these subjects' eventual 'permanent depletion" and "permanent instability" status. Since these subjects continuously run low BG values, and thek BG is unstable, any acute hypoglycemic episode would be capable of triggering SH. In general, a flag for severe hypoglycemia is raised either after aperiod of low unstable BG, or after an acute hypoglycemic event tha;t deviates substantially (in a risk space) from the latest running risk average (that maybe ah^ady high). It follows that SH episodes that are not preceded by any of these warning signs will remain unaccounted for by this algorithm. Below in Table 5C we present a sample output that illustrates the action of Algorithm 3 for several subjects: Table 5C; A Sample Output that Illuatrates the Action of Algorithm 3 for Several Subjects: ID SG SH FLAG TIME Herea=5, p=7.5,Y=I.5 3^35 -jQ _QQ _gg 53 75 For subject # 135 the first flag is raised I2s 77 .00 .00 4l!o9 about 30 hours prior to SH and Stays up for 135 124 .00 .00 35.02 ^^ next reading whish is taken 16 hours 135 51 .00 1.00 30.44 later and 14 hour prior to SH. This latter 135 50 .00 1,00 H . 72 reading and the tivo readings that follow 135 66 .00 135 1.00 135 49 ^.00 1.00 8.30 considerthis episode to be predicted. Nevertheless, the flag is brought up again about 8 hours and 20 minutes prior to SH. 135 97 .00 .00 140.05 135 130 .00 .00 25.17 135 59 .00 .00 20.20 135 76 .00 .00 5,23 135 41 .00 1.00 .62 135 1.00 219 200 .00 .00 40.72 219 64 .00 .00 37.8 8 219 43 .00 1.00 28.73 219 225 ,00 ' ,00 16.22 219 3a .00 1.00 11.18 219 43 .00 219 75 .00 . 219 1.00 222 156 .00 .00 19.08 222 176 .00 .00 13.23 222 83 .00 ,00 9.72 222 86 .00 .00 T.83 222 42 .00 1.00 4.75 222 1.00 223 228 .00 .00 IS.80 223 149 .00 .00 14.15 223 41 .00 l.OO 5.85 223 1.00 223 110 .00 223 1.00 A second SH episode for this subject is flagged 36 minutes in advance. This subject gets two warnings -approximately 28,7 and 11.2 hours prior to this SH episode. This subject gets a warnings approximately 4 hours and 45 minutes prior to this SH episode. This subject experienced two recurrent SH episodes within 12 hours. The flag is raised approximately 6 hours before the first episode and tiierefore we consider both episodes to be in the predicted risk hi^-risk 24-hour time period. Each line of this output presents an SMBG reading, or an SH episode (without a reading). ID is subject's ID number, BG is BG level in mg/dl, SH=1 whenever SH episode occurs. FLAG=1 if Algorithm 3 decides to raise the flag; TIME is the time to the nearest SH episode in hours. Optimizing the of Las of the Provisional Means Procedure: In a prior publication we have reported that in the period 48 to 24 hours before SH the average BG level decreased and the BG variance increased. In the 24-houi period immediately preceding SH average BG level dropped further, the variance of BG continued to increase, and there was a sharp increase in the LBGI. In the 24-houT period following SH, average BG level normalized, however the BG variance remained greatly increased. Both the average BG and its variance returned to baseline levels v^thin 48 hours after SH (see Kovatchev et al. Episodes of Severe Hypoglycemia in Type 1 Diabetes are Preceded, and Followed, within 48 Hours by Measurable Disturbances in Blood Glucose. J of Clinical Endocrinolo^ and Metabolism. 85: 4287-4292,2000). We now use these observations to optimize the lag of the provisional means procedure, k, employed by Algorithm 3 on the basis of the deviations in the average values of LBGI(n) and SBGI(n) observed within 24 hour prior to SH. In short, the lag for computing LBGI(n) and SBGI(n) was chosen to maximize the difference that these measures display within 24 hours prior to SH compared to the rest of the study, excluding periods immediately after SH when the system is out of balance. The optimal lag was found to be ^150. Tables 6A and 6B present the means of LBGI(n) and SBGI(n) for several values of the parameter k. and for both subject groups, low-moderate risk and moderate-high risk. It is evident that the difference between variousvaluesof (fe is not great, thus in a practical application any value of i> 10 would be appropriate. However, based on the current data we would recommend lc=l 50, and all fiirther computations use this lag. "Hiis recommendation is also based on the reduced variance in LBGI(n) and SBGI(n) at larger lag values, that is reflected by larger t-values below. Table 6A : LBGWn^ within 1)4 hour prior to SH vs the rest of the time for different laps: LBGI Low-moderate risk (LM Group) Moderate-hiqh risk (MH Group) 24h prior SH Rest of the time t P . 24h prior SH Rest of the time t p k=in Am St^-3 n? i(=?n 4 7.^ a:?4 fl4 k=.in 4fi4 ;i:ifi fli k=fin dR!^ .■^•u OR k=inn 4,4fi 3 59 11 7 n _a5a_ R4n 17,3 k='15d, 4.46 : a 7R 12.1^^ Table 6S : SBGirn) withm 24 hour orior to SH vs the rest of the time for different laes: SBGI Low-moderate risk (LM Group) Moderate-hiqh risk (MH Group) 24h prior SH Rest of .the time t P 24h prior SH Rest of the time t P k=in 7fiO ■ fi?ifi fin k=?n fl.li B7fl in 1 k=rin a fin fiRR Qfi k=fin R7R 704 inn k=1fVl flifl 7Sa 11 7 k=i'in 043 '7 4? 1:4V Optimal solution As seen in Tables 6A and 6B both LBGI and SBGI become hi^y significandy elevated in the 24-hour periods preceding SH. Thus, one is tempted to run a direct discriminant or logistic model to predict upcoming SH. Unfortunately such standard statistics don't woric very well, although both models are highly statistically significant. The discriminant model (that worked better than logistic regression) predicted correctly 52.6% of upcoming SH episodes. However, its flag-up to flag-down ratio was quite poor - Rud=I:4. Therefore this model was biased towards the larger amount of data points, a bias that is to be expected in any statistical procedure. Consequently, we had to employ the decision-making rule presented above. Accuracy of Prediction of Severe Hypoglycemia Optimization of the Threshold Parameters a, B. and y: Below we present a detailed account of the predictive power of Algorithm 3 using various combinations of its threshold parameters a, p, and y. Since the relationship between these parameters and the desired outcome (high prediction of SH and minimal ratio Rud) is quite complex, the optimization procedure that we used did not reach a single solution. Also, it seems that there is no need for a single solution either. It is probably a business, rather than mathematical decision, what would be an acceptable % of prediction of SH, given a "flag up" to "flag down" ratio. Thus, we do not claun that any of the presented below solution is optunal. However, m order to explore this subject further, we accept that a 50% prediction of future SH with Rud = 1:10 is a base for investigating other than 24 hours . prediction periods as well as various reqiurements for the number of SMBG readings per day required for a better risk profile. Table 7 presents the performance of Algorithm 3 at several combinations of the values of a, P, and y that are representative for the relationship between the percentage of predicted SH (hit rate) and the ratio Rud which we could call "annoyance index." Table 7 also includes the average total time (in days) per subject spent in alert vs. no-alert status during the study, i.e. the summary result from tiie alternating process of warning - no-waming periods that a subject would experience using this algorithm which illustrates the meaning of the ratio Rud. Table 7: Prediction of SH: Hits. Aimovance Index, and Averaee Times: Total for study (days) a P Y %Hit Rud Flag up Flag Down 6.4 8.2 1.5 43.4 1:25 7.8 198.9 6.0 7.5 1.5 45.2 1:20 96 197.3 5.5 7.5 1.5 47.2 1 : 15 , 12.9 194.1 .5,0 T.S - 1.5. 49.9 ■ 1:10 , .".,-19.0 ,' 190..1 5.0 7.5 1.3 51.3 1:9.5 19.5 185.7 4.9 7.0 1.2 ■ 53.1 1:8.4 21.6 182.0 4.8 7.0 .-1.2 53.4 1 :7 25.5 178.2 The highlighted solution is used for all fiirther analyses. Given that the participants in this study experienced 4.7 SH episodes on average, 19 days of high-alert periods seem to be acceptable, if these alerts would prevent 50% of SH. In addition, high-alert periods tend to come in clusters. Therefore we can assume that in practice, long and relatively calm periods will alternate with a few days of high-risk warnings. The last line in Table 7 presents a solution with a Rud - 1:7, which is equivalent to the solution presented in the invention disclosure. However, the current solution has almost 10% higher hit rate, 53.4% compared to 44% in our previous algorithm. When the hit rate is comparable to our previous algorithm, the annoyance ratio is below 1:20, i.e. three times better. FIG. 14 presents fiie smoothed dependence between tiie hit rate and the ratio Rud expressed in percentage. It is evident that the ratio between "flag up" and "flag down" increases rapidly vrfien the hit rate of Algorithm 3 increases. Thus, given these data it maybe unjustified to pursue parameter combinations resulting in a higher than 50% hit rate: Alternative Prediction Periods: In the beginning of the description of Algorithm 3 we made the basic assumption that an SH episode would be considered predicted if the flag is raised within the 24-hour period of time preceding this episode. This assumption resulted in the hit rates reported in the previous section, We will now present computations of the hit rate based on other prediction periods ranging from 12 to 72 hours. Throughout this experiment the parameters a, p, and y remain fixed at 5.0, 7.5, and 1.5 respectively, i.e. at their values in the solution highlighted of Table 7. Therefore the flag-i:^ rate remains the same as in this solution with Rnii=l: 10, and only the hit rate changes since we change the definition of a hit. FIG, 15 presente the dependence between the prediction period and the corresponding hit rate. It is evident that the hit rate increases rapidly with the increase of the prediction period to about 24 hours and then the increase of the hit rate gradually slows down. Therefore, we can conclude that 24 hours ahead is an optimal and a reasonable forecast period. Optimal Number ofSMBG Readings Per Day. Finally we experiment with the requirement of how many readings per day are needed in order to produce an optimal forecast of SH. As we said in the beginning, all reported SH episodes were 399. Of these episodes 343 had any SMBG reading available in the preceding 24 hours (additional 3 episodes had any reading wdthin the preceding 48 hours and additional 4 episodes had any reading within the preceding 72 hours). It follows that more than 50 SH episodes (14%) did not have any reasonable preceding SMBG reai^ng that would help with their prediction. The 343 episodes that had at least one prior SMBG reading within 24 hours were used for the computation of the hit rates in the previous section. The other episodes were naturally excluded from the computation. Further analysis shows that the hit rate increases rapidly with the number of readings taken before an SH episode. However, if we impose a strict requirement for a certain number of readings to be available in order to consider an SH episode, we see that the number of SH episodes that meet this requirement rapidly decreases (Table 8), This is due to subjects' non-compliance with the study requirements and is maybe a good reason to incorporate in fiiture meters some sort of a warning message that Algorithm 3 will not be useful and would be switched off if there are no SMBG readings taken at an appropriate rate. Table 8 presents the number of SH episodes that had available certain nurnber of preceding SMBG re^ngs and the hit rate of Algorithm 3 for these episodes. The highlighted TOW of thb table contains the optimal solution &om Table 7 that was iised as a base for all subsequent computations. All hit rates are given in terms of a 24-hour prediction period, i.e. flag within the 24 hours preceding SH. We can conclude ttiat widi an increased subjects' compliance the accuracy of Algorithm 3 in prediction SH would increase substantially. With 5 SMBG rea&ng per day the accuracy is up 10% from its base of 50% hit: Table 8: Performance of Aleoritiim 3. Given a Certain Number of Prior SMBG Readings Number of Preceding SMBG Readings SH episodes that satisfy the requirement in column 1 (% of total number of SH) Hit Rate At least 1 withit^ 24 hoUre ■ 343 (86%) , . T ■ 49.9% At least 3 within 24 hours 260 (65%) 54.2% At least 4 within 24 hours — _i , 180 (45%) 57.2% At least 5 within 24 hours 103 (26%) e4.i% At least 4 within 36 hours 268 (67%) 52.6% At (east 5 vmthln 36 hours 205 (51%) 54.6% At least 6 within 36 hours 146 (37%) 60.3% At least 7 within 36 hours 107 (27%) 60.7% At least 6 within 48 hours 227 (57%) 53.3% At least 7 within 48 hours , 187 (47%) 54.0% At least 8 wflthin 48 hours 143 (36%) 55.9% At least 9 \Mthln 48 hours 107(27%) 59.8% other Potential Enhancements That Were Tested The attempte to increase the predictive power of Algorithm 3 by inclusion of external parameters, such as number of SH episodes in the previous year, or baseline HbAlc were unsuccessfiU. Evidently, the short-term prediction of SH is mainly dependent on current or recent events. However, a limitation of this study is that all participatmg subjects had a history of > 2 SH episodes in the previous year. Finally, we tested whether an alert for SH should be issued if an increased risk for hypoglycemia is detected and there is no SMBG for certain period of time. This was done in an attempt to predict at least some of the SH episodes that were not preceded by any SMBG readings. This was not successful, generating predominantly false alarms. This result comes as an additional confirmation of the importance of compliance with an SMBG protocol comprised of sufficiently frequent SMBG readings. Appendix: Abstract Example No. 1 evaluates the fi-equency of recurrent hypoglycemia and SH (defined as stupor or unconsciousness that preclude self-treatment) following a low blood glucose (BG Ejghty-five patients (41 female) vrithTlDM and history of >2 episodes of SH in the last year performed SMBG 3-5 times per day for 6 to 8 months and recorded in diaries any SH episodes by date and time. Subjects' average age was 44±10 years, duration of diabetes 26±11 years, HbAic7,7±l.l%. All SMBG readings (n=75,495) were merged by date and time with subjects' SH episodes (n=399; SH events generally do not have a corresponding SMBG reading). For each SMBG reading, or SH episode, the elapsed time since the nearest previous low BG ( SH episodes) are randomly distributed across time. The negative Z-values of the tests show "clustering" of days with and without hypoglycemic readings or SH episodes. Table 9: Percentage of hypoglycemla/SH preceded by a low BG: BG 72h. Runs Test Z p-l6vel 2.9-3.9 mmoVl 52% 20% 10% 18% -18.3 <.0oo1> 1.9-2.8 mmol/1 55% 20% 7% 18% -14.7 SH 64% 11% 6% 19%, -11.1 <.0001> We conclude that more than half of all hypoglycemic SMBG readings and approximately 2/3rds of all SH episodes, are preceded by at least one hypoglycemic reading within the pre^^ous 24 hours. In addition, hypoglycemic events tend to appear in clusters. Thus, an imtial hypoglycemic episode may be a warning sign for upcoming recurrent hypoglycemia. n. EXAMPLE NO. 2 This method uses routine self-monitoring blood glucose (SMBG) data and pertains directly to enhancement of home SMBG devices by introducing intelligent data interpretation logic, capable of predicting both HbAu and periods of increased risk for significant hypoglycemia. The method has two components; (1) Algorithm 1 estimating HbAic, and (2) Algorithms 2 & 3 js^dicting long-terra and short-term (within 24 hours) significant hypoglycemia, respectively. In this report we describe the steps of development, optimization and validation of the HbAio estimation Algorithm 1, as well as its accuracy in estimating laboratory acquired HbAic. Objective: The primary goal was to reach an accuracy of 95% of measurements within +1 HbAio unit of a laboratory reference, which is the National Glycohemoglobm Standardization Program (NGSP) Criterion for accuracy for HbAu assays. Methods: Subjects; SMBG data was captured for 100 subjects with Type 1 and 100 subjects with Type 2 diabetes mellitns (TIDM, T2DM) for 6 months and 4 months respectively, with HbAic tests taken at months 0, 3 and 6 in TIDM and months 0, 2 and 4 in T2DM, Development and Optimization of Algorithm 1: The Training Data Set consisted of SMBG and HbAu data collected up to monlb 3 for TIDM and up to month 2 for T2DM. These Training Data were used for optimization of Algorithm 1 and for evaluation of a number of sample selection criteria that would ensure better accuracy. The sample selection criteria are requirements for any SMBG sample collected by the meter, which, if met, ensure accurate estimation of HbAic from that sample. Consequently, the meter will scan every SMBG sample and if the sample selection criteria are met, will compute and display HbAic estimate. After analyzing various cut points the following criteria were selected: 1. Test Frequency: In order to generate an estimate of HbAic, the meter will require an average of 2.5 tests or more per day over the last 60 days, e.g. a total of 150 SMBG readings over the past two months. It is important to note that this is an average per day, testing every day is not required. 2. Randomness of data: Certain 60-day samples with only post-prandial testing, or ins^ifficient nighttime tests ( Results; Prospective Validation and Accuracy of Algorithm 1: The algorithm, including the sample selection criteria, was then applied to Test Data Set I, which included SMBG and HbAie data for two months prior to TIDM and T2DM subjects' last HbAio. and to an mdependent Test Data Set 2 consisting of 60 TIDM subjects who participated in a previous NIH study. The estimates obtained by Algorithm 1 were compared to reference HbAic levels for validation purposes. In Test Data Set I the algorithm reached the NGSP criteria with an accuracy of 95.1% within +1 Hbaio iirat of the lab reference. In Teat Data Set 1 the algorithm reached the NGSP criteria as well wifli an accuracy of 95.5% within +1 Hba:c unit of the lab reference. Investigation of the sample selection criteria showed that 72.5% of all subjects would generate such an accurate estimate every day, and 94% of all subjects would generate such an accurate an estimate about once every 5 days. Conclusion: Routine SMBG data allow for accurate estimate of HbA\|o that meets the NGSP criterion for accuracy of direct HbAic assays. STIBJECTS & INCLUSION CRITERION We have consented 100 subjects with Type 1 Diabetes (TIDM) and 100 subjects with Type 2 Diabetes (T2DM). One hundred seventy-nine subjects, 90 with TIDM and 89 with T2DM, completed significant portions of the SMBG data collection. The data of these 179 subjects were used for testing Algorithms 2 and 3. However, the testing of Algorithm 1 required that the subjects had not only SMBG data, but HbAic data and SMBG records taken in the 60 days prior to SMBG. At month 3 of this study (month 2 for T2DM), 153 subjects (78 with TIDM) had completed HbA,c data and SMBG data meeting the above criterion, In adchtion, we used for testing of Algorithm 1 data for N=60 subjects with TIDM who participated in our previous NIH study (NIH). The demographic characteristics of all subjects are presented in Table 10. Table 10: Demoeraphic characteristics of the subjects. Variable TIDM T2DM NIH Age (years) 41.5(11.6) 50.9 (8.1) 44.3 (10.0) Gender: % Male 41% 43% 46% Duration of diabetes (years) 20.1 (10.1) 11.7(8.2) 26.4 (10.7) Body mass index 25.4(4.7) 34.2 (8.1) 24.3 (3.4) Baseline HbAjo 7.5(1.1) 8.5(2.1) 7.6(1.0) Second HbAic 7.3 (1.2) 7.9 (1.6) 7.4 (0.8) Thhxl HbAic 7.0 (0.9) 7,5(1.1) - # SMBG readings / subject / day 5.4 (2.3) 3.5 (0.8) 4.1 (1.9) # Days with SMBG readings in the 2 montiis preceding second HbAio , 56.9(5.4) 57.3 (4.3) 37.5 (14.3) Our investigation shewed that the major reason for incomplete data within 60 days prior to HbAic assays, or elsewhere, was not subject noncompliance, but meter failure. The time and date of the ONE TOUCH ULTRA meter could "jump" to a random date/time (e.g. Novenlber 2017), apparently if the patient depressed the "M" button for too long. We were checking the date/time of each meter upon return and we found that such event occurred in 60 meters throughout the course of the study. The time/date sloift affected 15,280 readings, or approximately 10% of all readings. We stoied these reiuiings separately and had a student review them. In many, but not in all cases he was able to restore Gie date/time sequence of the readings. This error, together with a few meters lost in the mail, reduced the number of subjects with good data for Algorithm 1 analyses from 179 to 141. The data of 12 subjects were restored, which brought the final count to 153 subjects, 78 with TIDM and 75 with T2DM, who had uninterrupted time sequence of data prior to HbAic, suitable for testing of Algorithm 1. PROCEDURE All subjects signed IRB-approved consent forms and attended orientation meetings where they were introduced to the ONE TOUCH ULTRA meter and completed sra^ening questionnaires. Inunediately after the introductory meeting all subjects visited a UVA laboratory and had blood drawn for baseline HbAic- TIDM subjects were followed for 6 months with laboratory HbAu assays at months 3 and 6; T2DM subjects were followed for 4 months with laboratory HbAic assays at months 2 and 4. Self-nonitoring (SMBG) data were regularly downloaded from the meters and stored in iatabases. Parallel recording of significant hypoglycemic and hyperglycemic episodes vas done by an automated e-mail/telephone tracking system every two weeks. MTA STORAGE AND CLEANING The raw data fi'om ONE TOUCH ULTRA were stored in InTouch databases separately for TIDM and T2DM subjects. These raw data were cleaned for subject and WE CLAIM: 1. A method for evaluating the HbA of a patient based on BG data collected over a first predetermined duration, said method comprising: preparing the data for estimating HbAic using a predetermined sequence of mathematical formulas defamed as: pre-processing of the data; validation of a sample of the BG data via sample selection criteria; and estimating HbAi’ if the sample is valid. 2. The method as claimed in claim 1, wherein said first predetermined duration is about 60 days. 3. The method as claimed in claim 1, wherein said first predetermined duration ranges from about 45 days to about 75 days, 4. The method as claimed in claim 1, wherein said first predetermined duration ranges from about 45 days to about 90 days. 5. The method as claimed in claim 1, wherein the preprocessing of the data for each patient comprise: conversion of plasma to whole blood BG mg/dl; conversion of BG measured in mg/dl to units of mmol/I; and computing Low Blood Glucose Index(RLOl) and High Blood Glucose Index{RHll). 6. The method as claimed in claim 1, wherein the preprocessing of the data for each patient using predetermined mathematical formulas defined as: conversion of plasma to whole blood BG mg/dl via BG’PLASBG (mg/dl) /1.12; conversion of BG measured in mg/dl to units of mmol/1) viaBGMM=BG/l 8 ; and computing Low Blood Glucose Index (RLOl) and High Blood Glucose Index(RHIl) using a predetermined mathematical formula defined as: Scale= [In (BG) j'"‘'‘ - 5.381, wherein BG is measured in units of mg/dl, Riskl= 22.765(Scale)’ wherein Ri3kL0= Riskl if (BG is less than about 112.5) and therefore risk of LBGI exists, otherwise RiskLO=0, and RiskHI=Riskl if (BG is greater than about 112.5) and therefore risk of HBGI exists, otherwise RiskHI=0, BGMMl = average of BGMM per patient, RLOl= average of RiskLO per patient, RHI1= average of RiskHI per patient, L06 = average of RiskLO computed only for readings during the night, otherwise missing if there are no readings at night, N06, N12, N24 are percentage of SMBG readings in fame intervals, NCI = total number of SMBG readings in the first predetermined duration; and NDAYS = number of days with SMBG readings in the first predetermined duration. 7. The method as claimed in claim 6, wherein the N06, N12, N24 are percentage of SMBG readings in time intervals of about 0-6:59 hour time period ; about 7-12:59 hour time period, and about 18-23:59 hour time period, respectively. 8. The method as claimed in claim 6, comprising assigning a group depending on the patient's computed High BG Index using a predetermined mathematical formula defined as: if(RHIl is about 5.25 or if RHIl is > about 16) then the assigned group= 0, if(RHIl is > about5.25 and if RHIl is about 7.0 and if RHIl is 9. The method as claimed in claim 8, comprising providing estimates using a predetermined mathemadcal formula defined as: EO = 0.55555BGMM!+2.95, El = 0.50567BGMMl+0.074L06+2.69, E2 = 0.55555BGMMl-0.074L06+2.96, E3 = 0. 44000BGMMl+0.035L06+3. 65; and if (Groups 1) then EST2=E1, or if (Group = 2) then EST2=E2, or if (Group= 3) then EST2=E3, otherwiseEST2=E0. 10. The method as claimed in claim 9, comprising providing further correcdon of the estimates using a predetermined mathematical formula defined as: if (missing(L06)) EST2=E0, if (RLOl is about 0.5 and RHIl is le about 2.0) thenEST2=EO-0.25, if{RL01 is about 2.5 andRHIl is > about 26) thenEST2=EO-l. 5RL01,and if( (RLOl/RHIl) is about 1.3) then EST2=EST2-0.08. 11. The method as claimed in claim 10 for esdmating the HbA ic of a padent based on BG data collected over the first predetermined duration, said method comprising: said estimating HbAjc using at least one of four predetermined mathemadcal formulas defined as: a) HbAic = the EST2 defined by claim 8 or as corrected by claim 10 or b) HbA,, = 0.809098BGMM1 + 0.064540RLOl-0.151673RHll + 1.873325, wherein. BGMMl is the average BG (mmol/1) as claimed in claim 6. RLOl is the Low BG Index as claimed in claim 6. RHIl is the High BG Index as claimed in claim 6; or c) HbA,, = 0.682742HBAO + 0.054377RH1! + 1.553277, wherein HBAO is a previous reference HbAlc reading taken about a second predetermined period prior to the estimate, wherein RHIl = is the High BG Index as claimed in claim 6; or d) HbAlc= 0, 41046BGMM + 4. 0775 wherein BGMMl is the average BG (mmol/1) as claimed in claim 6. 12. The method as claimed in claim 11, wherein said second predetermined duration is about three months. 13. The method as claimed in claim 11, wherein said second predetermined duration ranges from about 2.5 months to about 3.5 months. 14. The method as claimed in claim 11, wherein said second predetermined duration ranges from about 2.5 months to six months. 15. The method as claimed in claim 11, wherein the validation of the sample using sample selection criteria of HbAie estimate only if the first predetermined duration sample meets at least one of the following four criteria: a) a test frequency criterion wherein if the first predetermined duration sample contains an average of at least about 1.5 to about 2.5 tests per day; b) an alternative test frequency criterion only if the predetermined duration sample contains at least a third predetermined sample period with readings with an average frequency of about 1.8 readings/day; c) a randomness of data criterion-1 wherein the HbAlc estimate is validated or displayed only if the ratio(RL01/RHIl > = about 0.005), wherein RLOl is the Low BG Index as claimed in claim 6 RHIl is the High BG Index as claimed in claim 6; or d) a randomness of data criterion-2 wherein HbAic estimate is validated or displayed only if the ratio (N06 > = about 3%). ': Wherein N06 is the percentage of readings during the night as claimed in claim 6. 16. The method as claimed in claim 15, wherein said third predetermined duration is at least 35 days. 17. The method as claimed in claim 15, wherein said third predetermined duration ranges from about 35 days to about 40 days. 18. The method as claimed in claim 15, wherein said third predetermined duration ranges from about 35 days to about as long as the first predetermined duration. 19. A system for evaluating the HbAj’ of a patient based on BG data collected over a first predetermined duration, said system comprising: a database component operative to maintain a database identifying said BG data; and a processor programmed to: prepare the data for estimating HbAji; using a predetermined sequence of mathematical formulas defined as: pre-process the data. Validate the estimate via sample selection criteria, and estimate HbAic if the sample is valid. 20. The system as claimed in claim 19 or 37, wherein said first predetermined duration is about 60 days. 21. The system as claimed in claim 19, wherein said first predetermined duration ranges from about 45 days to about 75 days. 22. The system as claimed in claim 19, wherein said first predetermined duration ranges from about 45 days to about 90 days. 23. The system as claimed in claim 19. wherein the preprocessing of the data for each patient comprise: conversion of plasma to whole blood BG mg/dl; conversion of BG measured, in mg/dl to units of mmol/1 ; and computing Low Blood Glucose Index(RLOI) and High Blood Glucose Index(RHIl). 24. The system as claimed in claim 19, wherein the preprocessing of the data for each patient using predetermined mathematical formulas defined as: conversion of plasma to whole blood BG mg/dl via BG=PLASBG (mg/dl) /I. 12; conversion of BG measured in mg/dl to units of mmol/l) via BGMM=BG/18 ; and computing Low Blood Glucose Index (RLOl) and High Blood Glucose Index(RHn) using a predetermined mathematical formula defined as: Scale=[ln(BG)]' "‘'‘ -5. 381. wherein BG is measured in units of mg/dl, Riskl = 22.765 (Scale)’ wherein RiskLO=Riskl if (BG is less than about 112.5) and therefore risk of LBGI exists, otherwise RiskLO=0, and RiskHl=Riskl if (BG is greater than about 112.5) and therefore risk of HBGI exists, otherwise RiskHI=0, BGMMl = average of BGMM per pafient,RL01 = average of RiskLO per patient,RHIl= averageof RiskHI per patient, L06 = averageof RiskLO computed only for readings during the night, otherwise missing if there are no readings at night, N06, N12, N24 are percentage of SMBG readings in time intervals, NCI = total number of SMBG readings in the first predetermined duration; and NDAYS = number of days with SMBG readings in the first predetermined duration. 25. The system as claimed in claim 24, wherein the N06, N12, N24 are percentage of SMBG readings in time intervals of about 0-6:59 hour time period; about 7-12:59 hour time period, and about 18-23:59 hour time period, respectively. 26. The system as claimed in claim 24, comprising assigning a group depending on the patient's computed High BG Index using a predetermined mathematical formula defined as: if (RHIl is about 16) then the assigned grQup= 0, if(RHIl is > about 5.25 and if RHIl is about 7.0 and if RHIl is about 8,5 and if RHIl is 27. The system as claimed in claim 26, comprising providing estimates using a predetermined mathematical formula defined as: EO =0. 55555BGMMl+2.95, El = 0.50567BGMMl+0.074L06+2.69, E2 = 0.55555BGMMl-0.074L06+2.96, E3 = 0.44000BGMMl+0.035L06+3.65; and if (Group = 1) then EST2=E1, or if (Group = 2) then EST2=E2, or if (Group= 3) then EST2=E3, otherwiseEST2=E0. 28. The system as claimed in claim 27, comprising providing further correction of the estimates using a predetermined mathematical formula defined as: if (missing (L06))EST2=E0, if (RLOl is about 0.5 and RHIl is le about 2.0) thenEST2=EO-0. 25, if (RLOl is about 2.5 and RHIl is > about 26) thenEST2=EO-1.5RL01, and if((RL01/RHIl) is about 1.3) then EST2=EST2-0.08. 29. The system as claimed in claim 28 for estimating the HbAij; of a patient based on BG data collected over the first predetermined duration, said system comprising: said estimating HbAi’ using at least one of four predetermined mathematical formulas defined as: a) HbAje = the EST2 defined by claim 8 or as corrected by claim 10 or b) HbAi,= 0.809098BGMMl + 0.064540RL01-0. 151673RHI1 + 1.873325, wherein BGMMl is the average BG(mmol/l) as claimed in claim 24. RLOl is the Low BG Index as claimed in claim 24. RHIl is the High BG Index as claimed in claim 24 ; or c)HbA,i; = 0.682742HBA0 + O.054377RHll + 1.553277, wherein HBAO is a previous reference HbAi’ reading taken about a second predetermined period prior to the estimate, wherein RHIl = is the High BG Index as claimed in claim 6; or c) HbA,c = 0. 41046BGMM + 4. 0775 wherein BGMMl is the average BG (mmol/1) as claimed in claim 24. 30. The system as claimed in claim 29, wherein said second predetermined duration is about three months. 31. The system as claimed in claim 29, wherein said second predetermined duration ranges from about 2.5 months to about 3.5 months. 32. The system as claimed in claim 29, wherein said second predetermined duration ranges from about 2.5 months to six months. 33. The system as claimed in claim 29, wherein the validation of the HbAjc estimate using sample selection criteria of HbAic estimate only if the first predetermined duration sample meets at least one of the following four criteria: a) a test frequency criterion wherein if the first predetermined duration sample contains an average of at least about 1.5 to about 2.5 tests per day ; b) an alternative test frequency criterion only if the predetermined duration sample contains at least a third predetermined sample period with readings with an average frequency of about 1.8 readings/day; c) a randomness of data criterion-1 wherein the HbAjc estimate is validated or displayed only if the ratio (RLOI/RHIl > = about 0.005), wherein RLOl is the Low BG Index as claimed in claim 24 RHIl is the High BG Index as claimed in claim 24; or d) a randomness of data criterion-2 wherein HbAi’ estimate is validated or displayed only if the ratio (N06 > = about 3%) wherein N06 is the percentage of readings during the night as claimed in claim 24. 34. The system as claimed in claim 33, wherein said third predetermined duration is at least 35 days. 35. The system as claimed in claim 33, wherein said third predetermined duration ranges from about 35 days to about 40 days. 36. The system as claimed in claim 33, wherein said third predetermined duration ranges from about 35 days to about as long as the first predetermined duration, 37. A system for evaluating the HbAjc of a patient based on BG data collected over a first predetermined duration, said system comprising: a BG acquisition mechanism, said acquisition mechanism configured to acquire BG data from the patient; a database component operative to maintain a database identifying said BG data; and a processor programmed to: prepare the data for estimating HbAic using a predetermined sequence of mathematical formulas defined as: pre-process the data; validate a sample of the BG data via sample selection criteria; and estimate HbAic if the sample is valid. 38. A method for evaluating the long term probability for severe hypoglycemia (SH) and/or moderate hypoglycemia (MH) of a patient based on BG data collected over a predetermined duration, said method comprising: computing LBGI based on said collected BG data; and esdmating the number of future SH episodes using a predetermined mathematical formula based on said computed LBGI. 39. The method as claimed in claim 38, wherein: said computed LBGI is mathematically defined from a series of BG readings Xi,X2,..-x„ taken at time points ti, t2,....tn as: where: Ibgi (BG;a)= lO.ffBGf iff(BG) >OmdO otherwise, a = about 2, representing a weighting parameter. 40. The method as claimed in claim 38, further comprising: defining predetermined risk categories (RCAT), each of said risk categories (RCAT) representing a range of values for LBGI; and assigning said LBGI to at least one of said risk categories(RCAT). 41. The method as claimed in claim 40, wherein said risk categories (RCAT) are defined as follows: category 1, wherein said LBGI is less than about 0.25; category 2, wherein said LBGI is between 0.25 and 0.50; category 3, wherein said LBGI is between 0.50 and 0.75; category 4, wherein said LBGI is between 0.75 and 1.0; category 5, wherein said LBGI is between 1.0 and 1.25; category 6, wherein said LBGI is between 1.25 and 1.50; category 7, wherein said LBGI is between 1.5 and 1.75; category 8, wherein said LBGI is between 1.75 and 2.0; category 9, wherein said LBGI is between 2.0 and 2.5; category 10, wherein said LBGI is between 2, 5 and 3.0 category II, wherein said LBGI is between3.0 and 3.5; category 12, wherein said LBGI is between 3.5 and 4.25; category 13, wherein said LBGI is between 4.25 and 5.0; category 14, wherein said LBGI is between 5.0 and 6.5; and category 15, wherein said LBGI is above about 6.5. 42. The method as claimed in claim 40, further comprising: defining a probability of incurring a select number of SH episodes respectively for each of said assigned risk categories (RCAT). 43. The method as claimed in claim 40, further comprising: defining a probability of incurring a select number of SH episodes within a next first predetermined duration respectively for each of said assigned risk categories (RCAT), using the formula: F(x)= 1 -exp(-a.x') for any x > 0 and 0 otherwise, wherein: a = abDut-4.I9 b= about 1. 75 44. The method as claimed in claim 43, wherein said first predetermined duration is about one month. 45. The method as claimed in claim 43, wherein said first predetermined duradon ranges from about 0.5 months to about 1.5 months. 46. The method as claimed in claim 45, wherein said first predetermined duradon ranges from about 0.5 months to about 3 months. 47. The method as claimed in claim 42, further comprising: defining a probability of incurring a select number of SH episodes within a next second predetermined duration respectively for each of said assigned risk categories (RCAT), using the formula: F{x)=l-exp (-a.x ) for any x > 0 and 0 otherwise, wherein: a = about-3.28, b = about 1.50. 48. The method as claimed in claim 47, wherein said second predetermined duration is about three months. 49. The method as claimed in claim 47, wherein said second predetermined duration ranges from about 2 months to about 4 months. 50. The method as claimed in claim 47, wherein said second predetermined duration ranges from about 3 months to about 6 months. 51. The method as claimed in claim 40, further comprising: defining a probability of incurring a select number of SH episodes within the next third predetermined duration respectively for each of said assigned risk categories (RCAT), using the formula: F(x)= 1 -exp (-a.x ) for any x > 0 and 0 otherwise, wherein: a = about-3.06 b = about 1.45. 52. The method as claimed in claim 51, wherein said third predetermined duration is about 6 months. 53. The method as claimed in claim 51, wherein said third predetermined duration ranges from about 5 months to about 7 months. 54. The method as claimed in claim 51, wherein said third predetermined duration ranges from about 3 months to about 9 months. 55. The method as claimed in claim 40, further comprising: defining a probability of incurring a select number of MH episodes within the next month respectively for each of said assigned risk categories (RCAT), using the formula: F(x)= 1 -exp (-a.x ) for any x > 0 and 0 otherwise, wherein: a= about-1.58 b = about 1.05. 56. The method as claimed in claim 40, further comprising: defining a probability of incurring a select number of MH episodes within the next 3 months respectively for each of said assigned risk categories (RCAT), using the formula: F{x)= 1-exp (-a.x') for any x > 0 and 0 otherwise, wherein: a= about-1,37 b = about 1.14. 57. The method as claimed in claim 40, fiirther comprising: defining a probability of incurring a select number of MH episodes within the next 6 months respectively for each of said assigned risk categories (RCAT), using the formula: F(x)= 1 -exp (-a.x') for any x > 0 and 0 otherwise, wherein: a = about-1.37 b = about 1.35. 58. The method as claimed in claim 38, further comprising: assigning classifications of risk for future significant hypoglycemia of the patient. 59. The method as claimed in claim 58, wherein said classifications are defined as follows: minimal risk, wherein said LBGI is less than about 1.25; low risk, wherein said LBGI is between 1.25 and 2.50 ; moderate risk, wherein said LBGI is between 2.5 and 5; and high risk, wherein said LBGI is above about 5.0. 60. A system for evaluating the long term probability for severe hypoglycemia (SH)and/or moderate hypoglycemia (MH) of a patient based on BG data collected over a predetermined duration, said system comprising: a database component operative to maintain a database identifying said BG data; and a processor programmed to; compute LBGI bs’sed on said collected BG data, and estimate the number of future SH episodes using a predetermined mathematical formula b&sed on said computed LBGI. 61. The method as claimed in claim 60, wherein: said computed LBGI is mathematically defined from a series of BG readings Jt/, X2,... x„ taken at time points ti, t2,..., d, as: LBGI =-flbgi{x.\2) where: Ihgi (BG;a) - lO.ffBG)" iif(BG) > 0 and 0 otherwise, a = about 2, representing a weighting parameter. 62. The system as claimed in claim 60, further comprising: defining predetermined risk categories (RCAT), each of said risk categories (RCAT) representing a range of values for LBGI; and assigning said LBGI to at least one of said risk categories (RCAT). 63. The system as claimed in claim 62, wherein said risk categories (RCAT) are defined as follows: follows: category 1, wherein said LBGI is less than about 0.25 ; category 2, wherein said LBGI is between 0.25 and 0.50 ; category 3, wherein said LBGI is between 0.50 and 0.75 ; category 4, wherein said LBGI is between 0.75 and 1.0 ; category 5, wherein said LBGI is between 1.0 and 1.25 category 6, wherein said LBGI is between. 25 and 1.50 category 7, wherein said LBGI is between 1.5 and 1.75 category 8, wherein said LBGI is between 1.75 and 2.0 category 9, wherein said LBGI is between 2.0 and 2.5 ; category 10, wherein said LBGI is between 2.5 and 3.0 category 11, wherein said LBGI is between 3.0 and 3.5 ; category' 12, wherein said LBGI is between 3.5 and 4.25 ; category' 13, wherein said LBGI is between 4.25 and 5.0 ; category 14, wherein said LBGI is between 5.0 and 6.5 ; and category 15, wherein said LBGI is above about 6.5. 64. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of SH episodes respectively for each of said assigned risk categories (RCAT). 65. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of SH episodes within a next first predetermined durafion respecdvely for each of said assigned risk categories (RCAT), using the formula: F(x)= I-exp (-a.x') for any x > 0 and 0 otherwise, wherein: a = about-4.19 b = about 1.75. 66. The system as claimed in claim 65, wherein said first predetermined duration is about one month. 67. The system as claimed in claim 65, wherein said first predetermined duration ranges from about 0.5 months to about 1.5 months. 68. The system as claimed in claim 65, wherein said first predetermined durafion ranges from about 0.5 months to about 3 months. 69. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of SH episodes within a next second predetermined duration respectively for each of said assigned risk categories (RCAT), using the formula: F(x)= ! -exp (-a.x') for any x > 0 and 0 otherwise, wherein: a = about-3.28 b = about 1.50. 70. The system as claimed in claim 69, wherein said second predetermined duration is about three months. 71. The system as claimed in claim 69, wherein said second predetermined duration ranges from about 2 months to about 4 months. 72. The system as claimed in claim 69, wherein said second predetermined duration ranges from about 3 months to about 6 months. 73. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of SH episodes within the next third predetermined duration respectively for each of said assigned risk categories (RCAT). using the formula : F(x)’ 1-exp (-a.x') for any x > 0 and 0 otherwise, wherein: a = about-3.06 b = about 1.45. 74. The system as claimed in claim 73, wherein said third predetermined duration is about 6 months. 75. The system as claimed in claim 73, wherein said third predetermined duration ranges from about 5 months to about 7 months. 76. The system as claimed in claim 73, wherein said third predetermined duration ranges from about 3 months to about 9 months. 77. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of MH episodes within the next month respectively for each of said assigned risk categories (RCAT), using the formula: F(x)= 1-exp (-a.x’) for any x > 0 and 0 otherwise, wherein: a = about-1.58 b = about 1.05. 78. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of MH episodes within the next 3 months respectively for each of said assigned risk categories (RCAT), using the formula: F{x)= 1 -exp (-a.x') for any x > 0 and 0 otherwise, wherein: a =about-1.37 b = about 1.14. 79. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of MH episodes within the next 6 months respectively for each of said assigned risk categories (RCAT), using the formula: F(x)= 1-exp (-a.x ) for any x > 0 and 0 otherwise, wherein: a = abQut-1.37 b = about 1.35. 80. The system as claimed in claim 62, flirther comprising: assigning classifications of risk for future significant hypoglycemia of the patient. 81. The system as claimed in claim 80, wherein said classifications are defined as follows: minimal risk, wherein said LBGI is less than about 1.25 ; low risk, wherein said LBGI is between 1.25 and 2.50 ; moderate risk, wherein said LBGI is between 2.5 and 5 ; and high risk, wherein said LBGI is above about 5.0. 82. A system for evaluating the long term probability for severe hypoglycemia (SH) and/or moderate hypoglycemia (MH) of a patient based on BG data collected over a predetermined duration, said system comprising: a BG acquisition mechanism, said acquisition mechanism configured to acquire BG data from the patient; a database component operative to maintain a database identifying said BGdata ; and a processor programmed to: compute LBGI based on said collected BG data, and estimate the number of future SH episodes using a predetermined mathematical formula based on said computed LBGI. 83. A method for evaluating the short term probability for severe hypoglycemia (SH) ofa patient based on BG data collected over a predetermined duration, said method comprising: computing scale values based on said collected BG data ; and computing the low BG risk value (RLO) for each BG data. 84. The method as claimed in claim 83, wherein: said computed RLO (BG) is mathematically defined as: Scale = [In (BG)] 1.0845 -5.381, wherein BG is measured in units of mg/dl Risk = 22. 765(Scale)’I f (BG is less than about 112.5) then: RLO (BG) = Risk, otherwise RLO (BG) = 0. 85. The method as claimed in claim 83, wherein: said computed RLO (BG) is mathematically defined as: Scale= [In (BG) ] -1. 861, wherein BG is measured in units of mmol/1 Risk = 32.184 (Scale) 2 if (BG is about 112.5) then: RLO (BG) = Risk, otherwise RLO (BG) = 0. 86. The method as claimed in claim 83, wherein: computing LBGI based on said collected BG data, said computed LBGI is mathematically defined from a series of BG readingsxl,A:2,... x„ taken at time points ti, t2,.--, t„ as: LBGI =-Y,lbgi{x-,2) where: Ibgi (BG;a) = RLO (BG). 87. The method as claimed in claim 83, wherein: computing provisional LBGI based on said collected BG data, said computed provisional LBGI is mathematically defined from mathematically defined as: LBGI(l)=RLO(xl) RL02(1) = 0 LBGI (j) =(a-iyj) * LBGIG-l) + (1/j) * RLO(xo) RL02a)=(a-l)/j)RL02a-l) +(l/j) (RLO (x))-LBGI 0))'- 88. The method as claimed in claim 87, wherein: computing SBGI, said computed SBGI is mathematically defined as: SBGI(n)= yj{RL02{n)). 89. The method as claimed in claim 88, comprising qualifying or providing a warning of upcoming short term SH wherein if: (LBGI(150) > 2. 5 and LBGI (50) > (1. 5LBGI(150) and SBGI(50) > SBGI (150)) then said issue of warning is qualified or provided, or RLO > (LBGI(150)+1.5SBGI (150)) then said issue of warning is qualified or provided ; otherwise: a warning is not necessarily qualified or provided. 90. The method as claimed in claim 88, comprising qualifying or providing a warning of upcoming short term SH wherein if: (LBGI(n) > a and SBGI(n) ge (P) then said issue of warning is qualified or provided, and/or (RLO (n) (LBGI (n) +y * SBGI (n))) then said issue of warning is qualified or provided ; otherwise: a warning is not necessarily qualified or provided, wherein a, P, and y are threshold parameters. 91. The method as claimed in claim 90, wherein said threshold parameters a, p and y are defined as a = about 5, p = about 7.5, y = about 1.5. 92. The method as claimed in claim 90, wherein said threshold parameters a, P and y are defined as any combination in a, b, and/or c, or as any intermediate combination of values of said parameters between the values of said parameters in a, b, and/or c below: a) a - 6. 4, p = 8. 2, y = 1.5, a = 5. 0, P = 7. 5, y = 1.3 ; b) a = 6.0, p - 7.5, y = 1.5,a = 4.9, p = 7.0, y = 1.2 ; and/or c) a = 5, 5, p = 7.5, y = 1.5, a=4.8, p = 7.0, y = 1.2. 93. The method as claimed in claim 90, wherein said threshold parameters a, p and y are defined as any combination in a, b, and/or c, or as any intermediate combination of values of said parameters between the values of said parameters in a, b, and/or c below: a), a about 6.4, P about 8. 2, y about 1.5, a about 5.0, p about 7.5, y about 1.3 ; b). a about 6.0, p about 7.5, y aboutl.5, a about 4.9, P about 7.0, y about 1.2 ; and/or c). a about 5. 5, p about 7.5, y about 1.5, a about 48, p about 7.0, y about 1.2. 94. A system for evaluating the short term probability for severe hypoglycemia(SH) of a patient based on BG data collected over a predetermined duration, said system comprising: a database component operative to maintain a database identifying said BG data; and a processor programmed to: compute scale values based on said collected BG data; and compute the low BG risk value (RLO) for each BG data. 95. The system as claimed in claim 94, wherein: said computed RLO (BG) is mathematically defined as: Scale = [In (BG)]' °’’’-5.381, wherein BG is measured in units of mg/dl Risk = 22. 765 (Scale)’ if (BG is less than about 112.5) then: RLO (BG) = Risk, otherwise RLO (BG) = 0. 96. The system as claimed in claim 94, wherein: said computed RLO (BG) is mathematically defined as: Scale=[ln (BG) ]"‘‘‘-1.861, wherein BG is measured in unitsof mmol/1 Risk = 32. 184(Scale)’ if (BGis RLO (BG) = Risk, otherwise RLO (BG) = 0. 97. The system as claimed in claim 94, wherein: computing LBGI based on said collected BG data, said computed LBGI is mathematically defined from a series of BG readings Xi, x’,... x„ taken at time points tit2,..., ?„as : LBGI = -YlbgKx-,l) where: Ibgi (BG;a) = RLO (BG). 98. The system as claimed in claim 94, wherein: computing provisional LBGI based on said collected BG data, said computed provisional LBGI is mathematically defined from mathematically defined as: LBGI(l)=RLO(x,) RL02(1)=0 LBGIG)=(a-l)/j) * LBGIO-l) + (1/j) * RLO (x,) RLO2(j)=0-l)/J)RLO2a-l) +(l/j) (RLO (x’LBGIO))'. 99. The system as claimed in claim 98, wherein: computing SBGI, said computed SBGI is mathematically defined as: SBGI(n)= yj{RL02{n)). 100. The system as claimed in claim 99, comprising qualifying or providing a warning of upcoming short term SH wherein if: (LBGI (150) 2. 5 and LBGI (50) (1.5LBGI (150) and SBGI (5) SBGI (150)) then said issue of warning is qualified or provided, or RLO (LBGI (150) +1.5SBGI (150)) then said issue of warning is qualified or provided; otherwise: a warning is not necessarily qualified or provided. 101. The system as claimed in claim 99, comprising qualifying or providing a warning of upcoming short term SH wherein if: (LBGI (n) : a and SBGI(n) ge(P)) then said issue of warning is qualified or provided, and/or (RLO (n) (LBGI (n) + y * SBGI (n))) then said issue of warning is qualified or provided; otherwise: a warning is not necessarily qualified or provided, wherein a, P and y are threshold parameters. 102. The system as claimed in claim 101, wherein said threshold parameters a, p and y are defined as a = about 5, P = about 7.5, y = about 1.5. 103. The system as claimed in claim 101, wherein said threshold parameters a, p and y are defined as any combination in a, b, and/or c, or as any intermediate combination of values of said parameters between the values of said parameters in a, b, and/or c below: a), a = 6.4, p = 8. 2, y = 1.5, a = 5.0, p = 7.5, y = 1.3 ; b). a = 6.0, p - 7.5, y =1.5, a = 4.9, p =t 7.0, y = 1.2 ; and/or c). a - 5. 5, p =7.5, y = 1.5, a = 48, p = 7.0, y = 1.2. 104. The system as claimed in claim 101, wherein said threshold parameters a, p and y are defined as any combination in a, b, and/or c, or as any intermediate combination of values of said parameters between the values of said parameters in a, b, and/or c below: a), a about 6.4, p about 8. 2, y about 1.5, a about 5.0, p about 7.5, y about 1.3 ; b). a about 6.0, p about 7.5, y aboutl.5, a about 4.9, P about 7.0, y about 1.2 ; and/or c). a about 5. 5, P about 7.5, y about 1.5, a about 48, p about 7.0, y about 1.2. 105. A system for evaluating the short term probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said system comprising: a BG acquisition mechanism, said acquisition mechanism configured to acquire BG data from the patient; a database component operative to maintain a database identifying said BG data;, and a processor programmed to: compute scale values based on said collected BG data; and compute the low BG risk value (RLO) for each BG data. 106. The system as claimed in claim 11, wherein the validation of sample estimate using sample selection criteria of HbA(c estimate only if the first predetermined duration sample meets at least one of the following four criteria: a) a test frequency criterion wherein if the first predetermined duration sample contains an average of at least about 1.5; and b) a randomness of data criterion-1 wherein the HbAic estimate is validated or displayed only if the ratio (RL01/RHI1>= about 0.005), wherein RLOl is the Low BG Index as claimed in claim 6 RHIl is the High BG Index as claimed in claim 6; or c) a randomness of data criterion-2 wherein HbAic estimate is validated or displayed only if the ratio (N06 > = about 3%), wherein N06 is the percentage of readings during the night as claimed in claim 6. 107. The method as claimed in claim 106, wherein said third predetermined duration is at least about 35 days. 108. The system as claimed in claim 29, wherein the validation of sample estimate using selection criteria of HbAic estimate only if the first predetermined duration sample meets at least one of the following criteria: a) a test frequency criterion wherein if the first predetermined duration sample contains an average of at least about 1.5; and b) a randomness of data criterion-1 wherein the HbAjc estimate is validated or displayed only if the ratio (RL01/RHIl>=about 0.005), wherein RLOl is the Low BG index as claimed in claim 24 RHIl is the High BG index as claimed in claim 24; or c) a randomness of data criterion-2 wherein HbAjc estimate is validated or displayed only if the ratio (N06>= about 3%). wherein N06 is the percentage of readings during the night as claimed in claim 24. 109. The system as claimed in claim 108, wherein said third predetermined duration is at least about 35 days. 110. The method as claimed in claim 1, wherein said method for evaluating the HbAic of a patient based on said BG data collected is accomplished without the need for prior HbAic information. 111. The system as claimed in claim 19 or 37, wherein said system for evaluating the HbAic of a patient based on said BG data collected is accomplished without the need for prior HbAic information.

Full Text

The present application is a national stage filing of International Application No. PCT/US2003/025053, filed August 8, 2003, which claims the benefit of priority under 35 U.S.C. Section 119(e) from U.S. Provisional Application Serial No. 60/402,976, filed August 13 2002 entitled( Method, System, and Computer Program Product for Processing of Self-monitoring Blood Glucose (SMBG) Data to Enhance Diabetic Self-management," and No. 60/478,377, filed June 13, 2003J entitled "Method, System, and Computer Program Product for Processing of Self-monitoring Blood Glucose (SMBG) Data to Enhance Diabetic Self-management," the entire disclosures of all three disclosures are hereby incorporated by reference herein.
The present application is related to International Application No. PCT/US0:/09884, filed march 29, 2001 (Publication Nos. WO 01/72208 A2, WO 01/72208 A3), entitled "Method, System, and Computer Program Product for the Evaluation of Glycemic Control in Diabetes from Self-monitoring Data," and U.S. Patent Impaction Serial No.: 10/240,228 filed September 26, 2002, entitled "Method, System, and Computer Program Product for the Evaluation of Glycemic Control in Diabetes from Self-monitoring Data," the entire disclosures of which are hereby incorporated by reference herein.
FIELD OF THE INVENTION
The present system relates generally to Glycemic Control of individuals! with diabetes, and more particularly to a computer-based system and method for evaluation of predicting glycosylated hemoglobin (HbAic and HbA|) and risk of incurring hypoglycemia.

BACKGROUND OF THE INVENTION
Extensive studies, including the Diabetes Control and Complications Trial (DCCT) (See DCCT Research Group: The Effect Of Intensive Treatment Of Diabetes On The Development And Progression Of Long-Term Complications Of Insulin-Dependent Diabetes Mellitus. New England Journal of Medicine, 329: 978-986,1993), the Stockholm Diabetes Intervention Study {See Reichard P, Phil M: Mortality and Treatment Side Effects During Long-term Intensified Conventionallnsului Treatment in the Stockholm Diabetes Intervention Study. Diabetes, 43: 313-317, 1994), and the United Kingdom Prospective Diabetes Study {See UK Prospective Diabetes Study Group: Effect of Intensive Blood Glucose Control With Metformin On Complications In Patients With Type 2 Diabetes (UKPDS 34). Lancet, 352; 837-853,1998), have repeatedly demonstrated that the most effective way to prevent the long term complications of diabetes is by strictly maintaining blood glucose (BG) levels within a normal range using intensive insulin therapy.
However, the same studies have also documented some adverse effects of intensive insulin therapy, the most aconite of which is the increased risk of frequent severe hypoglycemia (SH), a condition defined as an episode of neuroglycopenia which precludes self-treatment and requires external help for recovery {See DCCT Research Group: Epidemiology of Severe Hypoglycemia In The Diabetes Control and Complications Trial. American Journal of Medicine, 90: 450-459,1991, and DCCT Research Croup: Hypoglycemia in the Diabetes Control and Complications Trial. Diabetes, 46: 271-286, 1997). Since SH can result in accidents, coma, and even death, patients and health care providers are discouraged from pursuing intensive therapy. Consequently, hypoglycemia has been identified as a major barrier to improved glycemic control (Cryer PE: Hypoglycemia is the Limiting Factor in the Management Of Diabetes. Diabetes Metah Res Rev, 15: 42-46,1999).
Thus, patients with diabetes face a life-long optimization problem of maintaining strict glycemic control without increasing their risk of hypoglycemia. A major challenge related to this problem is the creation of simple and reliable methods that are capable of

evaluating both patients' glycemic control and their risk of hypoglycemia, and that can be applied in their everyday environments.
It has been well known for more than twenty years that glycosylated hemoglobin is a marker for the glycemic control of individuals with Diabetes Mellitus (Type I or Type II). Numerous researchers have investigated this relationship and have found that glycosylated hemoglobin generally reflects the average BG levels of a patient over the previous two months. Since in the majority of patients with diabetes the BG levels fluctuate considerably over time, it was suggested that the real connection between mtegrated glucose control and HbAic would be observed only in patients known to be in stable glucose control over a long period of time,
Early studies of such patients produced an almost deterministic relationship between the average BG level in the preceding 5 weeks and HbAic, and this curvilinear association yielded a correlation coefficient of 0.98 {See Aaby Svendsen P, Lauritzen T, Soegard U, Nerup J (1982). Glycosylated Hemoglobin and Steady-State Mean Blood Glucose Concentration in Type 1 (Insulin-Dependent) Diabetes, DJabetologJa. 23,403-405). hi 1993 the DCCT concluded that HbAu was the "logical nommee" for a gold-standard glycosylated hemoglobin assay, and the DCCT established a linear relationship between the preceding mean BG and HbAic(5ee Santiago A'(1993). Lessons from the Diabetes Control and Complications Trial, Diabetes. 42,1549-1554).
Guidelines were developed indicating that an HbAic of 7% corresponds to a mean BG of 8.3 mM (150 mg/dl), an HbAic of 9% corresponds to a mean BG of 11.7 mM (210 mg/dl), anda l%increaseinHbAicCorresponds to an increase in mean BG of 1.7 mM (30 mg/dl, 2). The DCCT also suggested that because measuring the mean BG directly is not practical, one could assess apatient's glycemic control with a single, simple test, namely HbAio. However, studies clearly demonstrate that HbAjc is not sensitive to hypoglycemia.
Indeed, there is no reliable predictor of a patient's immediate risk of SH from any data. The DCCT concluded that only, about 8% of future SH could be predicted from known variables such as the history of SH, low HbAic, a^id hypoglycemia unawareness. One recent review details the current clinical status of this problem, and provides options

for preventic^ SH, that are available to patients and their health care providers {See BoUi, GB: How To Ameliorate The Problem of Hypoglycemia In Intensive As Well As Nonintensive Treatment Of Type I Diabetes. Diabetes Care, 22, Supplement 2: B43-B52,1999).
Contemporary home BG monitors provide the means for frequent BG measmements through Self-Monitoring of BG (SMBG). However, the problem with SMBG is that tliere is a missing link between the data collected by the BG monitors, and HbAic and hypoglycemia. In other words, there are currently no reliable methods for evaluating HbAic and recognizing imminent hypoglycemia based on SMBG readings {See Bremer T and Gough DA: Is blood glucose predictable from previous values? A solicitation for data. Diabetes 48:445-451,1999).
Thus, an object of this invention is to provide this missing link by proposing three distinct, but compatible, algorithms for evaluating HbAic and the risk of hypoglycemia from SMBG data, to be used to predict the short-term and long-term risks of hypoglycemia, and the long-tenm risk of hyperglycemia.
The inventors have previously reported that one reason for a missing link between the routinely available SMBG data and the evaluation of HbAio and the risk of hypoglycemia, is that the sophisticated methods of data collection and clinical assessment used in diabetes research, are infrequently supported by diabetes-specific and mathematically sophisticated statistical procedures.
Responding to the need for statistical analyses that take into account the specific distribution of BG data, the inventors developed a symmetrizing fransformation of the blood glucose measurement scale (See Kovatchev BP, Cox DJ, Gonder-Frederick LA and WL Clarke (1997). Symmetization of the Blood Glucose Measurement Scale and Its Applications, Diabetes Care. 20,1655-1658) that works as the follows. The BG levels are measured in mg/dl in the United States, and in mmol/L (or mM) in most other countries. The two scales are directly related by 18 mg/dl = 1 mM. The entire BG range is given in most references as 1.1 to 33.3 mM, and this is considered to cover practically all observed values. According to the recommendations of the DCCT {See DCCT Research Group (1993) The Effect Of Intensive Treatment of Diabetes On the Development and

Progression of Long-Term Complications of Insulin-Dependent Diabetes Mellitus, New England Journal of Medicine, 329, pp 978-986) the target BG range - also known as the euglycemic range- for a person with diabetes is 3.9 to 10 mM, hypoglycemia occurs when the BG falls below 3.9 mM, and hyperglycemia is when the BG rises above 10 mM. Unfortunately, this scale is numerically asymmetric -- the hyperglycemic range (10 to 33.3mM) is wider than the hypoglycemic range {1.1 to 3.9mM), and the euglycemic range(3.9to lOmM) is not centered withintiie scale. The inventors correct this asymmetry by introducing a transformation^fBGj, which is a continuous function defined on the BG range {1.1, 33.3], having the two-parameter analytical fomi:
f(BG, a,fi)-[(ln(BG)r~/3J. a,p>0 and which satisfies the assumptions:
A\:f(33.3. a,P) = -f(U.a,P) and
A2:f(10.0,a,P)-.f(3.9.a,P). Next, f(.) is multiplied by a third scaling parameter to fix the minimum and
maximum valuesoffhetransfonnedBGrangeat-VlO and->^ respectively. These values are convenient since a random variable with a standard normal distribufion has 99.8% ofits values within the interval [-Vio^, -JXQ]. IfBG is measured in mmol/I, when solved iiumerically with respect to the assimipfions Al and A2, the parameters of the fiinction/fBG, a,^ are « = 7,026,0=1.861, and the scalmg parameter is j' = /. 794. If BG is measured in mg/dl iflstead, the parameters are computed to be a=\ .084, p=5.381,aa. Thus, when BG is measured in mmol/1, the symmetrizing transformation is f(BG)=l. 794[(ln (BG)) ^'^ - IMl]. and when BG is measured in mg/dl the symmetrizing transformation is/(2G; = 1.509[(ln (BG)) ^■'^*- 5.381].
On the basis of the symmetrizing transformation/f^J the inventors introduced the Low BG Index - a new measure for assessing the risk of hypoglycemia from SMBG readings {See Cox DJ, Kovatchev BP, Julian DM, Gonder-Frederick LA, Polonsky WH, Schlundt DG, Clarke WL: Frequency of Severe Hypoglycemia In IDDM Can Be Predicted From Sqlf-Monitoring Blood Glucose Data. Journal of Clinical Endocrinology

and Metabolism, 79: 1659-1662,1994, and Kovatchev BP, Cox DJ, Gonder-Frederick LA Young-Hyman D, Schlimdt D, Clarke WL. Assessment of Risk for Severe Hypogiycemia Among Adults With EDDM: Validation of the Low Blood Glucose Index, Diabetes Care 21:1870-1875,1998). Given a series of SMBG data the LowBG Index is computed as the average of 10. f(BG/ taken for values off(BG) Using the Low BG Index in a regression model the inventors were able to account for 40% of the variance of SH episodes in the subsequent 6 months based on the SH history and SMBG data, and later to enhance this prediction to 46% (See Kovatchev BP, Straume M, Farhi LS, Cox DJ: Estimating the Speed of BloBd Glucose Transitions and its Relationship With Severe Hypoglycemia. Diabetes, 48: Supplement 1, A363,1999).
In addition, the inventors developed some data regarding HbAio and SMBG (See Kovatchev BP, Cox DJ, Straume M, Farhy LS. Association of Self-monitoring Blood Glucose Profiles with Glycosylated Hemoglobm. In: Methods m Enzvmolosv, vol. 321: Numerical Computer Methods. Part C, Michael Johnson and Ludvig Brand, Eds., Academic Press, NY;'2O0O).
These developments became a part of the theoretical background of this invention. In order to bring tiiis.theory into practice, several key theoretical components, among other things, as described in the following sections, were added. In particular, three methods were developed for employing the evaluation of HbAic, long-term and short-term risk for hypoglycemia. The development of these methods w£^, but not limited thereto, based on detailed analysis of data for 867 individuals with diabetes that included more than 300,000 SMBG readings, records of severe hypoglycemia and determinations ofHbAic.
The mventors have therefore sought to improve upon the aforementioned limitations associated with the conventional methods, and thereby provide simple and reliable methods that are capable of evaluating both patients' glycemic control and their risk of hypoglycemia, and that can be applied in their everyday environments.

SUMMARY OF THE EWENTIOW
The invention includes a data analysis method and computer-based system for the simultaneous evaluation, from routinely collected SMBG data, of the two most important components of glycemic control in diabetes: HbAic and the risk of hypoglycemia. For the purposes of this document, self-monitoring of BG (SMBG) is defined as any method for determination of blood glucose at diabetic patients' natural environment and includes the methods used by contemporary SMBG devices customarily storing 200-250 BG readings, as well as methods used by emerging continuous monitoring technologies. Given this broad definition of SMBG, this mvention pertains directly to the enhancement of existing home blood glucose monitoring devices (but not limited thereto) by introducing an intelligent data interpretation component capable of predicting both HbAic and periods of increased risk of hypoglycemia, as well as to enhancement of future continuous monitoring devices by the same features.
One aspect of the invention includes a method, system, and computer program product for evaluating HbAic from a predetermined period of collected SMBG data, for example about 4-6 weeks. lo one embodiment, the invention provides a computerized method and system for evaluating the HbAjg of a patient based on BG data collected over a predetermined duration. The method (or system or computer useable medium) includes evaluating the HbAu of a patient based on BG data collected over a first predetermined duration. The method comprising; preparing the data for estimating HbAic using a predetermined sequence of mathematical formulas defmed as: pre-processing of the data; estimating HbAic using at least one of four predetermined formulas; and validation of the estimate via sample selection criteria.
Another aspect of the invention includes a method, system, and computer program product for estimating the long-term probability for severe hypoglycemia (SH). This method uses SMBG readmgs fiom a predetermined period, for example about 4-6 weeks, and predicts the risk of SH within the following approximate 6 months, hi one embodiment, the invention provides a computerized method and system for evaluating the long term probability for severe hypoglycemia (SH) of a patient based on BG data

collected over a predetermined duration. The method (or system or computer useable medium) includes evaluating the long term probability for severe hypoglycemia (SH) or moderate hypoglycemia (MH) of a patient based on BG data collected over a predetemained duration. The method comprising: computing LBGI based on the collected BG data; and estimating the number of future SH episodes using a predetermined mathematical formula based on the computed LBGI.
Still yet another aspect of the invention includes a method, system, and computer program product for identifying 24-hoiu" periods (or other select periods) of increased risk of hypoglycemia. This is accomplished through the computation of the short-term risk of hypoglycemia using SMBG readings collected over the previous 24 hours. In one embodiment, the invention provides a computerized method and system for evaluating the short term risk for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration. The method (or system or computer useable medium) includes evaluating the short term probabiUty for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration. The method comprising: computing scale values based on the collected BG data; and computing the low EG risk value (RLO) for each BG daia.

An aspect of an embodiment of the present invention includes a method (or ahematively a computer program) for evaluating the HbAi^ of a patient based on BG data collected over a first predetermined duration. The method includes preparing the data for estimating HbAic using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-processing of the data; validation of a sample of the BG data via sample selection criteria; and estimating HbAic if the sample is valid,
An aspect of an embodiment of the present invention includes a system for evaluating the HbAu of a patient based on BG data collected over a first predetermined duration. The system included a database component operative to maintain a database identifying said BG data and a processor, wherein the processor is programmed to prepare the data for estimating HbAic using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-process the data, validate a sample of the BG data via sample selection criteria, and estimate HbA^ if the sample is valid.
An aspect of an embodiment of the present invention includes a system for evaluating the HbAic of a patient based on BG data collected over a first predetermined duration. The system comprising: a BG acquisition mechanism, which is configured to acquire BG data from the patient; a database component operative to maintain a database identifying said BG data; and a processor. The processor is programmed to prepare the data for estimating HbAjc using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-process the data; validate a sample of the BG data via sample selection criteria; and estimate HbAic if the sample is valid.
An aspect of an embodiment of the present invention includes a method (or alternatively a computer program) for evaluating the HbAu of a patient without the need for prior HbAic information based on BG data collected over a first predetermined duration. The method includes preparing the data for estimating HbAic using a predetermined sequence of mathemadcal formulas. The mathematical formulas defined as: pre-processing of the data; validation of a sample of the BG data via sample selection criteria; and estimating HbAic if the sample is valid.
An aspect of an embodiment of the present invention includes a system for

evaluating the HbAu of a patient without the need for prior HbAie information based on BG data collected over a first predetermined duration. The system includes a database component operative to maintain a database identifying the BG data and a processor. The processor being programmed to prepare the data for estimating HbAj^ using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-process the data, validate a sample of the BG data via sample selection criteria, and estimate HbAie if the sample is valid.
An aspect of an embodiment of the present invention includes a system for evaluating the HbAic of a patient without the need for prior HbAic information based on BG data collected over a first predetermined duration. The system comprising: a BG acquisition mechanism, which is configured to acquire BG data from the patient; a database component operative to maintain a database identifying said BG data; and a processor. The processor programmed to prepare the data for estimating HbAi^ using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-process the data; validate a sample of the BG data via sample selection criteria; and estimate HbAic if the sample is valid.
These aspects of the invention, as well as other aspects discussed throughout this document, can be integrated together to provide continuous information about the glycemic control of an individual with diabetes, and enhanced monitoring of the risk of hypoglycemia.
These and other objects, along with advantages and features of the invention disclosed herein, will be made more apparent from the description, drawings and claims that follow.
BRIEF DESCRIPTION OF THE DRAWINGS
The foregoing and other objects, features and advantages of the present invention, as well as the invention itself, well be more fiilly understood from the following

description of preferred embodiments, when read together with the accompanying drawings in which:
FIG. 1 graphically presents the empirical and theoretical probabilities for moderate (dashed line) and severe (black line) hypoglycemia within one month after the SMBG assessment for each of the 15 categories of risklevel defined by the Low BG Index of Example No. 1.
FIG. 2 graphically presents the empirical and theoretical probabilities for moderate (dashed line) and severe (black Ime) hypoglycemia within three months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index of Example Np. 1,
PIG. 3 graphically presents the empirical and theoretical probabilities for moderate (dashed line) and severe (black line) hypoglycemia within six months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index of Example No. 1.
FIG. 4 graphically presents the empirical and theoretical probabilities for 2 or more moderate (dashed line) and sever (black line) hypoglycemic episodes within three months after the SMSG assessment for each of the 15 categories ofrisk level defmed by the Low BG Index of Example No. 1.
FIG. 5 graphically presents the empirical and theoretical probabilities for 2 or more moderate (dashed line) and severe (black line) hypogf ycemic episodes within six months after the SMBG assessment for each of the 15 categories of risk level defmed by the Low BG Index of Example No. 1.
FIG. 6 is a fimctional block diagram for a computer system for implementation of the present invention.
FIGS. 7-9 are schematic block diagrams of altemative variations of the present invention related processors, communication links, and systems.
FIG, 10 graphically presents the empirical and theoretical probabilities for 3 or more moderate (dashed line) and severe (black line) hypoglycemic episodes within six months after the SMBG assessment for each of the 15 categories ofrisk level defined by theLowBGIndexofExampleNo. 1.

FIG. 11 graphically shows the analysis of the residuals of this model showed a close to normal distribution of the residuals for Training Data set lof Example No. 1.
FtG. 12 graphically shows the analysis of the residuals of this model showed a close to norma] distribution of the residuals lof Example No. 1
FIG. 13 graphically shows a statistical evidence for that is given by the normal probability plot lofExarapleNo. 1.
FIG. 14 graphically presents the smoothed dependence between the hit rate and the ratio Rud expressed m percentage in Example No. 1.
FIG. 15 graphically presents the dependence between the prediction period and the corresponding hit rate in Example No. 1.
FIGS. 16 (A)-(B) graphically present a one-month risk for significant hypoglycemia in TIDM predicted by the LBGI for ANOVA of number of severe hypoglycemic episodes by risk group (F=7.2, pO.OOl) and ANOVA of number of moderate hypoglycemic episodes by risk group (F=13.9, p FIGS. 17 (A)-{B) graphically present a 3-month risk for significant hypoglycemia in TIDM predicted by the LBGI for ANOVA of number of severe hypoglycemic episodes by risk group (F=9.2, p FIGS. 18 (A)-(B) graphically present a one-month risk for significant hypoglycemia in T2DM predicted by the LBGI for ANOVA of nmciber of severe hypoglycemic episodes by risk group (F=6.0, p FIGS. 19 (A)-(B) graphically present a 3-month risk for significant hypoglycemia in T2DM predicted by the LBGI for ANOVA of number of severe hypoglycerriic episodes by risk group (F=5.3, p DETAILED DESCRIPTION OF THE INVENTION
The invention makes possible, but not limited thereto, the creation of precise

methods for the evaluation of diabetics' glycemic control, and include, firmware and software code to be used in computing the key components of the method. The inventive methods for evaluating HhAu, the long-term probability of SH, and the short-term risk of hypoglycemia, are also validated based on the extensive data collected, as will be discussed later in this document. Finally, the aspects of these methods can be combined in structured display or matrix.
I. Evaluating HbAir
One aspect of the invention includes a method, system, and computer program product for evaluating HbAjc from a predetermined period of collected SMBG data, for example 4-6 weeks. In one embodiment, the invention provides a computerized (or other type) method and system for evaluating the HbAic of a patient based on BG data collected over a predetermined duration. The method includes evaluating the HbAic of a patient based on BG data collected over a first predetermined duration, the method comprismg: preparing the data for estimating HbAio using a predetermined sequence of mathematical formulas. The mathematical formulas defined as: pre-processing of the data; estimating HbAic using at least one of four predetermined formulas; and validation of the estimate via sample selection criteria. The first predetermined duration can be about 60 days, or alternatively the first predetermined duration ranges from about 45 days to about 75 days, or &om about 45 days to about 90 days, or as desired; The preprocessing of the data for each patient comprise: conversion of plasma to whole blood BG mg/dl; conversion of BG measured in mg/dl to units of mmol/1; and computing Low Blood Glucose Index (RLOl) and High Blood Glucose Index {RHIl). The preprocessing of the data for each patient uses a predetermined mathematical formulas defined as: conversion of plasma to whole blood BG mg/dl via BG=PLASBG (mg/dl) /1.12; conversion of BG measured in mg/dl to units of mmol/I) via BGMM=BG/18; and computing Low Blood Glucose hidex (RLOl) and High Blood Glucose Index (RHIl). The preprocessing of the data further uses a predetermined mathematical formula defined as: Scale=[In(BG)]^°^'^ - 5.381, wherein BG is measured m units of mg/dl; Riskl = 22.765 (Scale)^ wherein RiskLO=Riskl if (BG is less than about 112.5) and therefore risk of LBGI exists, otherwise RiskLO=0;

RiskHI=Riskl if (BG is greater than about ] ]2.5) and therefore risk of HBGI exists, otherwise RisfcHI=0; BGMMI = average of BGMM per patient; RLOl = average of RiskLO per patient; RHIl = average of RiskHI per patient; L06 = average of RiskLO computed only for readings during the night, otherwise missing if there are no readings at night; N06, NI2, N24 are percentage of SMBG readings in time intervals; NCI = total number of SMBG readings in the first predetermined duration; and NDAYS = number of days with SMBG readings in the first predetermined duration. The N06, N12, N24 are percentage of SMBG readings in time intervals of about 0-6:59 hour time period; about 7-12:59 hour time peri6d, and about 18-23:59 hour time period, respectively, or other desired percentages and number of intervals.
The method fijrther comprises assigning a group depending on the patient's computed High BG Index using a predetermined mathematical formula. This formula may be defined as: if (RHIl is about 16) then the assigned group= 0; if (RHIl is > about 5.25 and if RHIl is about 7.0 and if RHU is about8.5 and if RHIl is Next, the method may iiirther include providing estimates using a predetermined mathematical formula defined as: EC = 0.55555*BGMMl+2.95; El = 0.50567*BGMMl+0.074*L06+2.69; E2 = 0.55555*BGMMl-0.074*L06+2.96; E3 = 0.44000*BGMMl+0.035*L06+3.65; and if (Group = 1) then EST2=E1, or if (Group = 2) then EST2=£2, or if (Group = 3) then EST2=E3, otherwise EST2=E0.
The method comprise providing fiirther correction of the estimates using a predetermined mathematical formula defmed as; if (missing(L06)) EST2=E0, if (RLOl is about 26) then EST2=EO-1.5*RL01; and if ((RLOl/RHIl) is about 1.3) then EST2=EST2-0.08.
The estimation oftheHbAicofa patient based on BG data collected over the first predetermined duration can be accomplished by estimating HbAio Using at least one of four predetermined mathematical formulas defined as:
a) HbAlc = the EST2 defmed above or as corrected above;

b) HbAic = 0.809098*BGMM1 + 0.064540*RLOl - 0.15I673*RHn + 1.S73325, wherein BGMMl.is the average BG (mmol/1), RLOl is the LowBG Index, RHIl is the High BG Index;.*
c) HbAlt; = 0.682742*HBAO + 0.054377*RHIl +1.553277, wherein HBAO is a previous reference HbAl c reading taken about a second predetermined period prior to the estimate, wherein KHIl = is the High BG Index; or
d) HbAlc = 0.41046*BGMM + 4.0775 wherein BGMMl is the average BG (mmol/I). The second predetermined duration can be about three months; about 2.5 months to about 3.5 months; or ahout 2.5 months to six months, or as desired.
The validation of the estimate using sample selection criteria of HbAlc estimate is achieved only if the first predetermined duration sample meets at least one of the following four criteria;
a) a.test firequency criterion wherein if the first predetermined duration sample contains an average of at least about 1.5 to about 2.5 tests per day; b) an alternative test frequency criterion only if the predetermined duration sample contains at least a third predetermined sample period with readings with an average firequency of about 1.8 readings/day (or other desired average fl'equency);
c) a randomness of data criterion-1 wherein the_HbAlc estimate is validated or displayed only if the ratio (RL01/RHII>= about 0.005), wherein: RLOl is the Low BG Index, RHIl is the High BG Index; or
d) a randomness of data criterion wherein HbAlc estimate is validated or displayed only if the ratio {N06 >= about 3%), and wherem N06 is the percentage of readings during the night. The third predetermined duration can be at least 35 days, range firom about 35 days to about 40 days, or fiom about 35 days to about as long as the first predetermined duration, or as desired.
n. Loae-term Probability for Severe Hypoglycemia fSH).
Another aspect of the invention includes a method, system, and computer program product for estimating the long-term probability for severe hypoglycemia (SH). This method uses SMBG readings fiom a predetermined period, for example about 4-6 weeks,

and predicts the risk of SH within the following approximate 6 months. In one embodiment, the invention provides a computerized method (or other type) and system for evaluating the long term probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration. The method for evaluating the long term probability for severe hypoglycemia (SH) or moderate hypoglycemia (MH) of a patient based on BG data collected over a predetermined duration comprises: computing LBGI based on the collected BG data; and estimating the number of fiiture SH episodes using a predetermined mathematical formula based on the computed LBGI. The computed LBGI is mathematically defined fi:om a series of BG readings ;i:;, X2,... Xn taken at time points fj, fa ...,t„ as:
LBGI=^yibgi(x,;2).v!hsTe:lbgJ(BG;a)= lO.ffBG)' iif(BG)>OmAO
othenvise, and a = about 2, representing a weighting parameter (or other weighting parameter as desire).
A predetermined risk categories(RCAT) is defined, whereby each of the risk categories(RCAT) represent a range of values for LBGI; and the LBGI is assigned to at least one of said risk categories(RCAT). The risk categoriesCRCAT) are defined as follows:
category 1, wherein said LBGI is less than about 0.25;
category 2, wherein said LBGI is between about 0.25 and about 0.50;
category 3, wherein said LBGI is between about 0.50 and about 0,75;
category 4, wherein said LBGI is between about 0.75 and about 1.0;
category 5, wherein said LBGI is between about 1.0 and about 1.25;
category 6, wherein- said LBGI is between about 1.25 and about 1.50;
category 7, wherein said LBGI is between about 1.5 and about 1.75;
category 8, wherein said LBQI is between about 1.75 and about 2.0;
category 9, wherein said LBGI is between about 2.0 and about 2.5;
category 10, wherein said LBGI is between about 2.5 and about 3.0
category 11, wherein said LBGI is between about 3.0 and about 3.5;
category 12, wherein said LBGI is between about 3.5 and about 4.25;

category 13, wherein said LBGI is between about 4.25 and about 5.0;
category 14, wherein said LBGI is between about 5.0 and about 6.5; and
categoryl5, wherein said LBGI is above about 6.5.
Next, the probability of incurring a select number of SH episodes is defined respectively for each'of said assigned risk categoriesfRCAT). Defining a probability of incurring a select number of SH episodes within a next first predetermined duration respectively for each of said assigned risk categories(RCAT), using the formula: F(x) = / -exp(-a.x') for any :t>(? and 0 otherwise, wherein: a = about-4.19 and b = about 1.75 (a and/or b may be other desired values). The first predetermined duration can be about one month; range from about 0,5 months to about 1.5 months, or ranges from about 0.5 months to about 3 months, or as desired.
Also, the probability,of incurring a select number of SH episodes within a next second predetermined duration respectively for each of said assigned risk categories(RCAT) is defined, usmg the formula: F(x) = / - exp(-a.x^) for any x>0 and 0 otherwise, wherein: a = about -3.28 and b = about 1.50 (a and/or b may be other desired values). The second predetermined duration can be about three months, range from about 2 months to about 4 months, or about 3 months to about 6 months, or as desired.
Further, a probability of incurring a select number of SH episodes within the next third predetermined duration is defined respectively for each of the assigned risk categories(RCAT), using the formula: F(x) = J - exp(-a.y^) for any x>0 and 0 otiiervi^se, wherein: a = about -3.06 and b = about 1.45 (a and/or b may be other desired values). rhe third predetermined duration can be about 6 months, range from about 5 months to about 7 months, or range from about 3 months to about 9 months, or as deshed.
Alternatively, a probability of incurring a select number of MH episodes within ihe next first predetermined period (ranges of about 1 month, about 0.5-1.5 months, about 3.5-3 months, or as desired) is defined respectively for each of said assigned risk :ategories(RCATO, using the foiinula: F(x) = i - ey^(-a.x^) for snyx>0 and 0 otherwise, Alierein; a = about-V.58 and b = about 1.05 (aand/orbmay be other desired values).
Alternatively, a probability of incurring a select number of MH episodes within he next second predetermined period (ranges of about 3 months, about 2-4 months, about

3-6 montiis, or as desired) is defined respectively for each of said assigned risk categories(RCAT), using tke formula: F(x) = i - exp(-a.x') for any x>0 and 0 otherwise, wherein: a = about-1.37andb-about 1.14 (a and/orb may be otlier desired values).
Alternatively, a probability of incurring a select number of MH episodes within the next third predetermined period (ranges of about 6 months, about 5-7 months, about 3-9 months, or as desired) is defined respectively for each of said assigned risk categories(RCAT), using the formula; F(x) - 1 - expi-a.x) for any x>0 and 0 otherwise, wherein; a = about-1.37 andb = about 1.35 (a and/orb may be other desired values).
Moreover, classifications of risk for future significant hypoglycemia of the patient are assigned. The classifications are defined as follows: minimal risk, wherein said LBGI is leas than, about 1.25; low risk, wherein said LBGI is between about 1.25 and about 2.50; moderate risk, wherein said LBGI is between about 2.5 and about 5; and high risk, wherein said LBGI is above about 5.0 (other classification ranges can be implemented as desired).
in. Short-tenp ProbabHity for Severe Hypoglycemia fSH).
Still yet another aspect of the invention includes a method, system, and computer program product for identifying 24-hour periods (or other select periods) of increased risk of hypoglycemia. This is accomplished through the computation of the short-term risk of hypoglycemia using SMBG readings collected over liie previous 24 hours. In one embodiment, the invention provides a computerized method and system for evaluating the short term risk for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration. The method for evaluating the short temi probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration comprises: computing scale values based on said collected BG data; and computing the low BG risk value (RLO) for each BG data. The computed RLO(BG) is mathematically defined as: Scale = [ln(BG)]' '^^'^ - 5.381, wherein BG is measured in units of mg/dl; Risk = 22.765(Scale)^; if (BG is less than about 112.5) then: RLO(BG)=Risk, otherwise RLO(BG)= 0. Alternatively, the computed RLO(BG) is mathematically defined as: Scale=[In(BG)]''^ -1.861, wherein BG is measured in units

of mmolA; Risk= 32.184(Scale)^; if (BG isSibout 112.5) then: RLO(BG) = Risk, otherwise RLO(BG) = 0.
LBGI can be computed based on the collected BG data. The computed LBGI is mathematically defined irom a series of BG readings ;ci, Xj,... a:„ taken at time points r^, t2,
..., t„ as: LBGI = -y]lbgiix,;2). where: lbgi(BG;a) = RLO(BG).
Provisional LBGI can be computed based on the collected BG data. The computed provisional LBGI is mathematically defined from mathematically defined as: LBGI(l) - RLO(x,); RL02(1) = 0; LBGI(j)= ((j-l)/]) * LBGI (j-l) + (1/j) * RLOC:^); and RLO2G)=(a-l)/j)*RL02Ci-l) + (l/j)*(RL0C:5)-LBGIG)f.
SBGI can be computed using a mathematical formula defined as: SBGI(n)=
Next, the invention provides a quahfication or warning of upcoming short term SH. The qualification or warning is provided if CLBGIC150) >2.5 and LBGI(50) > (1.5*LBGI(150) and SBG1(50) > SBGI(150)) then said issue of warning is qualified or provided, or RLO >(LBGI(150)+1.5*SBGI(150)) then said issue of warning is qualified or provided, otherwise, a warning is not necessarily qualified or provided.
Altematively, Next, the invention provides a qualification or warning of upcoming short term SH. The qualification or warning i^ provided if:
(LBGI(n) >a and SBGI(n.) ge (P)) then said issue of warning is qualified or provided, and/or (RLO(n) >(LBGI(n)+ y * SBGI(n))) then said issue of warning is qualified or provided; otherwise a warning is not necessarily qualified or provided, wherein a, p, and y are threshold parameters.
The threshold parameters a, p, and y are defined as a = about 5, p = about 7.5, y = about 1,5. Other possible parameter combinations are presented in the table below. The values may be approximations of the values presented below as well as any intermediate combination of values in the table below.

a P Y a P y
6.4 8.2 1.5 5.0 7.5 1.3
6.0 7.5 1.5 4.9 7,0 1.2
5.5 7.5 1.5 4.8 7.0 1.2
IV. Exemplary Systems
The method of the invention may be implemented using hardware, software or a combination thereof and may be implemented in one or more computer systems or other processing systems, such as persona! digit assistants (PDAs), or directly in blood glucose self-monitoring devices (SMBG memory meters) equipped with adequate memory and processing capabilities. In an example embodiment, the invention was implemented in software runniag on a general purpose computer 900 as illustrated in FIG. 6. Computer system 600 includes one or more processors, such as processor 604 Processor 604 is connected to a commimication in&astructure 606 (e.g., a communications bus, cross-over bar, or network). Computer system 600 may include a display interface 602 that forwards graphics, text, and other data ftom the communication infrastructure 606 (or from a frame buffer not shown) for display on the display unit 630.
Computer system 600 also includes a main memory 608, preferably random access memory (RAM), and may also mclude a secondary memory 610. The secondary memory 610 may include, for example, a hard disk drive 612 and/or a removable storage drive 614, representiiig a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, etc. The removable storage drive 614 reads from and/or writes to a removable storage unit 618 in a well known manner. Removable storage unit 618, represents a floppy disk, magnetic tape, optical disk, etc, which is read by and written to by removable storage drive 614. As will be appreciated, the removable storage unit 618 includes a computer usable storage medium having stored therein computer software and/or data.
In alternative embodiments, secondary memory 610 may include other means for

allowing computer programs or other instructions to be loaded into computer system 600. Such means may include, for example, a removable storage unit 622 and an interface 620. Examples of such removable- storage units/interfaces include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as a ROM, PROM, EPROM or EEPROM) and associated socket, and other removable storage units 622 and interfaces 620 which allow software and data to be transferred from the removable storage unit 622 to computer system 600.
Computer system 600 may also include a communications interface 624, Communications interface 624 allows software and data to be transferred between computer system 600 and external devices. Examples of communications interface 624 may include a modem, a network interface (such as an Ethernet card), a communications port (e.g., serial or par^lcl, etc.), a PCMCIA slot and card, a modem, etc. Software and data, transferred via communications interface 624 are in the form of signals 628 which maybe electronic, electromagnetic, optical or other signals capable of being received by communications interfece 624. Signals 628 are provided to communications interface 624 via a communications path (i.e., channel) 626. Channel 626 carries signals 628 and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, an infrared link, and other communications channels.
In this docLunent, the terms "computer program medium" and "computer usable medium" are used to generally refer to media such as removable storage drive 614, a hard disk installed in hard disk drive 612, and signals 628. These computer program products are means for providing software to computer system 600. The invention includes such computer program products.
Computer programs (also called computer control logic) are stored in main memory 608 and/or secondary memory 610. Computer programs may also be received via communications interface 624. Such computer programs, when executed, enable computer system 600 to perform the features of the present invention as discxissed herein. In particular, the computer programs, when executed, enable processor 604 to perform the fimctions of the present invention. Accordingly, such computer programs represent controllers of computer system 600.

In an emboi^ment where the Invention is implemented using software, tiie software may be stored in a computer program product and loaded into computer system 600 using removable storage drive 614, hard drive 612 or communications interface 624. The control logic (software), when executed by the processor 604, causes the processor 604 to perform the fimctions of the invention as described herein.
In another embodiment, the invention is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine to perform the fimctions described herein will ,be apparent to persons skilled in the relevant art(s).
In yet another embodiment, the mvention is implemented using a combination of both hardware and'software.
In an example software embodiment of the mvention, the methods described above were implemented in SPSS control language, but could be implemented in other programs such as, but not limited to, C + + programming language or other programs available to those skilled in the art.
FIGS. 7-9 show block diagrammatic representation of alternative embodiments of the invention. Referring FIG. 7, there is shown a block diagrammatic representation of the system 710 essentially cornprises tiie glucose meter 728 used by a patient 712 for recording, inter aha, insulin dosage readings and measured blood glucose ("BG") levels. Data obtained by the glucose meter 728 is preferably transferred through appropriate communication links 714 or data modem 732 to a processing station or chip, such as a personal computer 740, PDA, or cellular telephone, or via appropriate Internet portal. For instance, data stored may be stored within the glucose meter 728 and may be directly downloaded into the persona! computer 740 through an appropriate interface cable and then transmitted via the Internet to a processing location. An example is the ONE TOUCH monitoring system or meter by LifeScan, Inc. which is compatible with IN TOUCH software which includes an interface cable to down load the data to a personal computer.
The glucose meter is common in the industry and includes essentially any device that can fimctions as a BG acqxiisition mechanism. The BG meter or acquisition

mechanism, device, tool, or system incliides various conventional methods directed toward diawing a blood sample (e.g. by fmgea-prick) for each test, and a determination of the glucose level using an instrument that reads glucose concentrations by electromechanical or claorimetric methods. Recently, various m.ethods for determining the concentration of blood analytes without drawing blood have been developed. For example, U.S. Pat. No. 5,267,152 to Yang et al. Qiereby incorporated by reference) describes a noninvasive technique of measuring blood glucose concentration using near-IR radiation diffiise-reflection laser spectroscopy. Similar near-IR spectrometric devices are also described m U.S. Pat, No. 5,086,229 to Rosenthai et al. and U.S. Pat. No. 4,975,581 to Robinson et al. (of which are hereby incorporated by reference). .
U.S.Pat. No. 5,139,023 to Stanley (hereby incorporated by reference) describes a transdermal blood glucose monitoring apparatus that relies on a permeability enhancer (e.g., a bile salt) to facilitate transdermal movement of glucose along a concentration gradient established between interstitial fluid and a receiving medium. U.S. Pat. No. 5,036,861 to Sembrowich (hereby incorporated by reference) describes a passive glucose monitor that collects perspiration through a skin patch, where a cholinergic agent is used to stimulate perspiration secretion &om the eccrine sweat gland. Similar perspiration collection devices are described in U.S. Pat, No. 5,076,273 to Schoendorfer and U.S. Pat. No. 5,140,985 to Schroeder (of which are hereby incorporated by reference).
In addition, U.S. Pat. No. 5,279,543 to Glikfeld (hereby incorporated by reference) describes the use of iontophoresis to noninvasively sample a substance through skin into a receptacle on the skin surface. Glikfeld teaches that this sampling procedure can be coupled with a glucose-specific biosensor or glucose-specific electrodes in order to monitor blood glucose. Moreover, International Publication No. WO 96/00110 to Tamada (hereby incorporated by reference) describes an iontophoretic apparabis for transdermal monitoring of a target substance, wherein an iontophoretic electrode is used to move an analyte into a collection reservoir and a biosensor is used to detect the target analyte present in the reservoir. Finally, U.S, Pat, No. 6,144,869 to Bemer (hereby incorporated by reference) describes a sampling system for measuring the concentration of an analyte present.

Further yet, the BG meter or acquisition meohanism may include indwelling catheters and subcutaneous tissue fluid sampling.
The computer or PDA 740 includes the software and hardware necessary to process, analyze and mterpret the self-recorded diabetes patient data in accordance with predefined flow sequences (as described above in detail) and generate an appropriate data interpretation output. Preferably, the results of the data analysis and interpretation performed upon the stored patient data by the computer 740 are displayed in the form of a paper report generated through a printer associated with the personal computer 740. Alternatively, the results of the data interpretation procedure may be directly displayed on a video display unit associated with the computer 740.
FIG. 8 shows a block diagrammatic representation of an alternative embodiment having a diabetes management system that is a patient-operated apparatus 810 having a bousing preferably sufficiently compact to enable apparatus 810 to be hand-held and carried by a patient. A strip guide for receiving a blood glucose test strip (not shown) is located on a surface of housing 816. Test strip is for receiving a blood sample from the patient 812. The apparatus includes a microprocessor 822 and a memory 824 connected to microprocessor 822. Microprocessor 822 is designed to execute a computer program stored in memory 824 to perform the various calculations and control fiinctions as discussed in great detail above. A keypad 816 is connected to microprocessor 822 through a standard keypad decoder 826. Display 814 is connected to microprocessor 822 through a display driver 830. Microprocessor 822 communicates with display driver 830 via an mterface, and display driver 830 updates and refreshes display 814 under the control of microprocessor 822. Speaker 854 and a clock 856 are also connected to raicioprocessoi 822. Speaker 854 operates under the control of microprocessor 822 to emit audible tones alerting the patient to possible future hypoglycemia. Clock 856 supplies the current date and time to microprocessor 822.
Memory 824 also- stores blood glucose values of the patient 812, ihe insulin dose values, the insulin types, and the parameter values used by microprocessor 822 to calciUate future blood glucose values, supplemental insulin doses, and carbohydrate supplements. Each blood glucose value and insulin dose value is stored in memory 824

with a corresponding date and time. Memory 824 is preferably a non-volatile memory, such as an electrically erasable read only memory (EEPROM).
Apparatus 810 also includes a blood glucose meter 828 comiected to microprocessor 822. Glucose meter 828 is designed to measure blood samples received on blood glucose test strips and to produce blood glucose values from measurements of the blood samples. As mentioned previously, such glucose meters are ■well known in the art. Glucose meter 828 is preferably of the type which produces digital values which are output directly to microprocessor 822. Alternatively, blood glucose meter 828 may be of the type which produces analog values. In this altemative embodiment, blood glucose meter 828 is connected to microprocessor 822 through an analog to digital converter (not shown).
Apparatus 810 further includes an input/output port 834, preferably a serial port, which is connected to microprocessor 822. Port 834 is connected to a modem 832 by an interface, preferably a standard RS232 interface. Modem 832 is for estabhshing a communication link between apparatus 810 and a persona! computer 840 or a healthcare piovidei computer 838 through a communication network 836. Specific techniques for connecting electronic devices through cormection cords are well knovra in the art. Another altemative example is "bluetooth" technology communication.
Alternatively, FIG. 9 shows a block diagrammatic representation of an altemative embodiment having a diabetes management system that is a patient-operated apparatus 910, similar as shown in FIG. 8, having a housing prefeiably suSiciently compact to enable the apparatus 910 to be hand-held and carried by a patient. For example, a separate or detachable glucose meter or BG acquisition mechanism/module 928. There are already self-monitoring devices that are capable of directly computing Algorithms 1, 1, 3 and displaying the results to the patient without transmitting the data to anything else. Examples of such devices are ULTRA SMART by Lifescan Inc., Milpitas, CA and FREESTYLE TRACKER by Therasense, Alameda, CA.
Accordingly, the embodiments described herein are capable of being implemented over data communication networks such as the internet, making evaluations, estimates, and information accessible to any processor or computer at any remote location, as

depicted in FIGS. 6-9 and/or U.S. Pat. No. 5,851,186 to Wood, of which is hereby incorporated by reference herein. Alternatively, patients located at remote locations may have the BG data transmitted to a central healthcare provider or residence, or a different remote location.
In simmiary, the invention proposes a data analysis computerized (or noncomputerized) mefcod and system for the simultaneous evaluation of the two most important components of glycemic control in individuals with diabetes: HbAlc and the risk of hypoglycemia. The method, v^'hile using only routine SMBG data, provides, among other things, three sets of output.
The potential implementations of the method, system, and computer program product of the invention is that it provides the following advantages, but are not limited thereto. First, the invention enhances existing home BG monitoring devices by producing and displaying; 1) estimated categories for HbAl c, 2) estimated probability for SH in the subsequent six months, and 3) estimated short-term risk of hypoglycemia (i.e. for the next 24 hours). The latter may include warnings, such as an alarm, that indicates imminent hypoglycemic episodes. These three components can also be integrated to provide continuous information, about the glycemic control of individuals with diabetes, and to enhance the monitoring of their risk of hypoglycemia.
As an additional advantage, the invention enhances existing software or hardware that retrieves SMBG data. Such software or hardware is produced by virtually every manufacturer of home BG monitoring devices and is customarily used by patients and health care providers to interpret SMBG data. The methods and system of the invention can be directiy incorporated into existing home blood glucose monitors, or used for the enhancement of software that retrieves SMBG data, by introducing a data interpretation component capable of .predicting both HbAl c and periods of increased risk of hypoglycemia.
Still yet another advantage, the invention evaluates the accuracy of home BG monitoring devices, both in the low and high BG ranges, and over the entire BG scale.
Moreover, another advantage, tiie invention evaluates the effectiveness of various treatments for diabetes

Further still, as patients with diabetes face a hfe-Iong optimization problem of maintaining strict glycemic control without increasing their risk of hypoglycemia, the present invention alienates this related problem by use of its simple and reliable methods, i.e., the invention is capable of evaluating both patients' glycemic control and their risk of hypoglycemia, and at the same time applying it in then- everyday environments.
Additionally, the invention provides the missing link by proposing three distinct, but compatible, algorithms for evaluating HbAlc and the risk of hypoglycemia ftom SMBG data, to fae used to predict the short-term and long-term risks of hypoglycemia, and the long-term risk of hyperglycemia.
Another advantage, the invention evaluates the effectiveness of new insulin or insulin delivery devices. Any manufacturer or researcher of kisulin or insulin delivery devices can utilize the embodiments of the invention to test the relative success of proposed or tested insulin types or device delivery designs.
Finally, another advantage, the invention evaluates the effectiveness of drugs that are adjunct to insulin therapy.
EXAMPLES OF INVENTION
I. EXAMPLE NO. 1
This Example No. i consists of three algorithms for simultaneous evaluation, fiom routine SMBG data, ofthetwomost important components of glycemic control in diabetes, HbAjc and risk for hypoglycemia. This metliod pertains directly to enhancement of existing home BG-monitoring devices by introducing an intelligent data interpretation component capable ($f predicting both HbAu and periods of increased risk for hypoglycemia. The data analysis method has three components (algorithms);
■ Algorithm 1: Evaluation of HbAic!
■ Algorithm' 2: Evaluation of long-term risk for severe hypoglycemia (SH), and
« Algorithm 3: Evaluation of short-term (within 24-48 hours) risk for
hypoglycemia.

Algorithm 1 and 2 provide uninterrupted monitoring and information about the overall glycemic control of an individual with Type I or Type 2 diabetes mellitus (TIDM, T2DM), covering both the high and the low end of the BG scale. Algorithm 3 is supposed to be activated when Algorithm 2 mdicates an increased long-term risk for hypoglycemia. Upon activation. Algorithm 3 requires more ftequent monitoring (4 times a day) and provides 24 to 48-hour forecast of the risk for moderate/severe hypoglycemia.
Another important objective of Example 1 was to test with existing data a number of hypotheses and ideas that could potentially lead to alternative algorithms estimating HbAic and computing risk for hypoglycemia in a manner that is conceptually different from the one proposed in the invention disclosure. The goal vras to find potentially better solutions, or simply to verify that certain ideas do not lead to better results, which is essential for optimization and promptness of the analysis of the data that are currently being collected in Example No. 2 of the study.
DATA SETS
In order to ensure that the results of our optimization can be generalized to population level, Algorithms 1 and 2 were first optimized using traming data sets and then tested for accuracy using an vmrelated test data set. For Algorithm 3 we cunently have only one data set containing parallel SMBG and records of SH. A detailed description of the patient population follows:
(1) Training Data set 1: Ninety-six patients with TIDM, who were diagnosed at least 2 years prior to the study. Forty-three of these patients reported at least two episodes of severe hypoglycemia in the past year and 53 patients reported no such episodes during the same period. There wepe 38 males and 58 females. The mean age was 35±8 yr., mean duration of disease I6±10 yr., mean msulin units/kg per day 0,58±0.19, and meanHbAic was 8.6±1.8yo. These subjects collected approximately 13,000 SMBG readings over 40-45-day period. The frequency of SMBG was approximately 3 readmg/day. This data collection was followed by 6 months of monthly diaries of modeiate and severe hypoglycemic episodes. This data set was used as a training data set for Algorithm 1 (no prior HbAic) and for Algorithm 2.

(2) Traming Data set: Eighty-five patients with TIDM, diagnosed at least 2 years prior to the study, all of whom reported SH episodes ia the past year. There were 44 males and 41 females. The mean age was 44±I0 years, mean duration of disease 26±11 yr., meaninsulinumts/kgperday0.6±0.2, the mean baseline HbAio was 7,7±1.1%, and the mean 6-month HbAu was 7.4±1% (6-month HbAio available for 60 subjects). These subjects collected approximately 75,500 SMBG readmgs during the 6 months between the two HbAic assays. The frequency of SMBG in Data set 2 was higher - 4-5 readings per day. In addition, during the 6 months of SMBG the subjects kept diaries of moderate and severe hypoglycemic episodes by date and time of their occurrence, resulting in 399 SH episodes. This data set was used as a training data set for Algorithm 1 (with prior HfaAic) and for all analyses concerning Algorithm 3.
(3) The Test Data Set that we used contains data for N=600 subjects, 277 with TIDM and 323 with T2DM, all of whom used insulin to manage their diabetes, These data were collected by Amylin Pharmaceuticals, San Diego, CA and included 6-8 months of SMBG data (approximately 300,000 readings), accompanied by baseline and 6-month HbAic determinations and some demographic data. These subjects were participating in a clinical trial investigating the effects of pramlintide (in doses of 60 to 120 micrograms) on metabolic control. The subjects' use pramlintide was randomized across the TIDM and T2DM groups (Table 1).

Table 1: Demoeyaphjc characteristics of the subjects in die Test Data Set.

Variable TIDM-
Meaii(SD) T2DM-Mean(SD) p-level
Age (years) 38.0(13.4) 58.1 (9.4) Gender; Male/Female 136/141 157/166 Ns
Baseline HbAic 9.74(1.3) 9.85(1.3) Ns
HfaAi(.atmonth6 8.77(1.1) 8.98 (1.3) 0,04
Duration of diabetes (years) 14,6 (9.8) 13.5 (7,6) Ns
Age at onset (years) 23,4 (12.8) 44.6(10.4) # SMBG readings / subject / day 3.2(1.1) 2.9 (0.9) Table 1 presents demographic characteristics and comparison of TIDM vs. T2DM subjects. For the Srst 6 months of fee studyflie average HbAjc declined significantly in both TIDM and T2 DM groups, perhaps due to the use of medication, which is out of the scope of this presentation (Table 1). This relatively rapid change in HbAic allowed for a better estimation of the predictive ability of Algoritlim 1. hi all data sets SMBG was performed using Lifescan ONE TOUCH 11 or ONE TOUCH PROFILE meters.
ALGORITHM 1: EVALUATION OF HBA.r
Example No. 1 provides for, but not limited thereto, an optimization of the prediction of HbAu (Algorithm 1) through: (I) Weighting higher the more proximal SMBG; (2) Weighting higher more prolonged high BG events: (3) Calibrating the High BG Index with an earlier HhAu, and (4) Incorporating other patient variables, such as age, gender and duiition of disease.
Algorithm I includes an optimal fimction of SMBG data that evaluates subsequent HbAic, as well as recommendations for the optimal duration of the data collection period and the optunal frequency of self-monitoring during that period. It is essential to note, however, that the broader goal of Algorithm 1 is to evaluate the status of patients' glycemic control. Although HbAic is the accepted "gold standard" for evaluation of

glycemic control, cimently it is unclear whether anolher measure, such as average SMBG or ffigh BG Index, would not be a better predictor of long-term complications in diabetes than HbAic. Until this issue is clarified, the goal of Algorithm 1 will be to estimate HbAu- In order to approximate as closely as possible future real applications of Algorithm 1 we proceeded as follows;
(1) First, several optimal ftmctions using different independent variables, optimal duration, and optimal frequency of SMBG were derived from two training data sets 1 and 2, collected in our previous studies involving patients with TIDM;
(2) Then, all coefficients were fixed and Algorithm 1 was applied to the much larger test data set containing data for both TIDM and T2DM subjects collected imder very different conditions in a clinical trial conducted by Amylin Pharmaceuticals.
(3) Detailed estimation ofthe preciseness of Algorithm 1 for various optimal fiinctions was made using the test data set only.
This separation of training and test data sets allows us to clainithat the estimated preciseness of Algorithm 1 can be generalized to any other data of subjects with Tl DM or T2DM. Moreover, since the Amylin data (test data set) were collected from subjects who were undergoing treatment to lower their HbAic, and therefore exhibited unusually large variation of their HbAic over the 6rmonth period of observation, we can claim that Algorithm 1 is predictive not only of relatively constant HbAu, but also of large and unusually rapid changes in HbAu. Along these same lines. Algorithm 1 would be most useful for patients who have their goal to optimize their HbAio, which is presumably the patient group most likely to be interested in purchasing a meter with advanced features such as continuous evaluation of HbAio.
Summary of the Results
■ The optunal SMBG data collection period is 45 days;
■ The optimal frequency of SMBG is 3 reading per day;

■ Two optimal HbAio estimating functions were developed: Fl - using only SMBG data, Eind F2 - -using SMBG data plus an HbAi^ reading taken approximately 6 months prior to the HbAic that is being predicted;
■ The evaluation of the accuracy of HbAic prediction in the test data set {N=573 subjects) was done by several criteria that are detailed in the following pages (Table 2). Here we will mention that m TIDM the overall accuracy (within 20% of measured HbAic) of FI was 96.5% and the overall accviracy of F2 was 95.7%. For T2DM the overall accuracy of Fi was 95.9%, the overall accuracy of F2 was 98.4%. Thus, the accuracy of both Fl and F2 is comparable to a direct measurement of HbAic;
■ Most importantly, for patients whose HbAio changed 2 or more units from their baseline reading CN=68), the accuracy of F/ in predicting this change was 100% ui both TIDM and T2DM, while the accuracy of F^ was 71% and 85% in TIDM and T2DM respectively;
■ Both Fl and F2 provided substantially more accurate estimation of HbAjc at 6 months than Qie original HbAio estimate at month 0. Using liie average BG as a direct estimate of HbAic is not accurate as well;
■ A number of alternative approaches were tested, such as selecting specific times of the day (postprandial reading) for evaluation of HbAic, different weightmg of SMBG readings according to the elapsed time between each SMBG reading and HbAic determination, separate evaluation of subjects with different average blood glucose -to HbAic ratio, etc. While some of these alternative approaches achieved certain better results than the two functions proposed above, none was better overall. We can conclude that the optimal functions Fl and F2 will be used in future applications of AJgoritiiml.
Detailed Results - Test Data Set
Themostimportantpartof the evaluation of Algorithm 1 is the evaluation of its performance on a data that are not related to the data used for its development and

optimization. From the test data set, the data of 573 subjects, N=254 with TIDM and N=319 with T2DM, were complete enough to be used for the evaluation of Algorithm 1.
Optimal Aleoritkm 1: For each subject, a 45-day subset of his/her SMBG reading was selected. This subset had a starting date of approximately 75 days before the subject's 6-month HbAic'assay and ending date approximately 30 days before that assay. Since in this data set the time of HhAjc assay is imown only approximately, the elapsed time between last SMBG reading taken mto analysis and HbAic is not exact. This time period was selected through sequential optimization of its duration and its ending point (how long before HbAio). The optunal duration was 45 days. The optimal ending time was 1 month prior to HbA]c. In other words, a 45-day SMBG would predict the values of HbAic approximately one month ahead. However, the prediction of any other HbAic value between days 45 and 75 is almost as good - the differences are numerical rather than of clinical significance. Similarly, the difference between a 45-day monitoring period and a 60-day monitoring period is not great. However, monitoring periods shorter than 45 days cause a rapid decline ia predictive power.
The optimal estimation functions are linear and are given by the formulas:
Estimate 1 - Without prior knowledge of HbAic:
Fl = 0.80909S*BGMM1 + 0.064540*LBGn - 0.151673*RHI1 + 1.873325
Estimate 2 - Knowing a prior HbAic (appioximately 6 months ago).
F2 =0.682742'*HBAO + 0.054377*RHI1 + 1.553277
In these formulas BGMMl is the average blood glucose computed from the 45 days of SMBG readings; LBGU and RHIl are the Low and the High BG hidices computed from the same readings, and HBAO is the baseline HbAic reading that is used for Estimate 2 only. The values of the coefficients are optimized using the training data set and relevant statistics and plots are presented in section Detailed Resuhs - Training data set.
The fimctions Fl and F2 produce point estimates of HbAic, i-e. each function produces an estimated value of HbAio. Interval estimates can be obtained by using the

regression error estimates presented in section Detailed Results - Training data set. However, applied to the test data set, these mterval estimates will not be tme 90% or 95% confidence intervals for HbAic because they are originally derived from the training data set and are only applied to the test data (see also the statistical note in the next section).
Evaluation oftlie accuracy of Algorithm 1: Tables 2A and 2B present results from the evaluation of the optimal Algorithm 1 with data from the test data set for subjects with TIDM and T2DM, respectively. Several criteria were used:
(1) Absolute deviation (AERR) of Estimated from measured HbAio;
(2) Absolute percent deviation (PERR) of Estimated from measured HbAic;
(3) Percent Estunates within 20% of measured HbAic (HIT 20),
(4) Percentreadings within 10 ofmeasured HbAic (HIT 10), and
(5) Percent readings outside of a 25%i-zone around measured HbAio (MISS 25).

Table 2A: Accuracy of Alaorit im 1 in TIDM fN=254 subiects).
Fl F2 Average BG Prior HbAu P-value
AERR 0.77 0.61 1.68 1.1 PERR (%) 8.3 7.1 19.4 12.8 HIT20(%) 96.5 95,7 61.0 81.0 HIT10(%) 65.4 75.5 29.9 48.2 MISS 25 (%) 2.4 1.6 28.4 9.9

Table 2B; Accuracvof Aleorifhm 1 inT2DMn^=319 subiects).
Fl F2 Average BG Prior HbAu P-value
AERR 0.72 0.57 . 1.92 0.87 PERR (%) 7.6 6,4 20.9 U.7 HIT20 (%) 95.9 98.4 56.4 82.8 HITIO (%) 70.2 79.3 29.5 53.3 MISS 25 (%) 1.2 0.6 36.7 8.2
The first two columns in Tables 2A and 2B present the results for the optimal functions F2 and F2 respectively. The third column presents the accuracy of the estimation if the average BG (in mmolA) was taken as an estimate of HbAio. The fourth column presents the same accuracy measures computed using the HbAic assay at time 0 as an estimate of HbAjc at 6 months. It is evident that for both TIDM and T2DM F2 is a little better overall estunate of HbAic than FJ. Most unportantly, both Fl and F2 are substantially better estimates of HbAuthan its earlier value, or than the average BG. This is especially true for the % estimates that fell outside of the 25% accuracy zoiie. The difference between the performance of FZ and F2 and the estimate from a prior HbAic assay is highly significant (column 4).
Statistical Note: It is important to note that it is not appropriate to evaluate the accuracy of Algorithm 1 using traditional, regression-type criteria, such as R^ or F and p values from ANOVA table. This is because the parameter estimates were derived fi'ora another unrelated data set (the training data) and are only applied to this test data set. Thus, statistical assumptions for the underlying model are violated (for example in the test data set the sum of the residuals will not be zero) and therefore R^, F, and p lose their statistical meaning.
Further evaluation of the accuracy of Algorithm 1 in the test data set was done by reviewing the TIDM and T2DM subjects who had a substantial change in their SMBG readmg from the baseline to 6-month follow-up. Tables 3A and 3B present list of the TIDM and T2DM subjects who had an absolute change in their HbAic equal to or greater than2units. In each subject group 34 subjects had such a change in HbAic- Algorithm 1, function Fl, predicted 100% of such changes m both TIDM and T2DM. The predictive power of F2 was diminished due to the inclusion of baseline HbAic in the equation (vAich partially pulls the estimates back to the baseline value of HbAic) and was 71% in TIDM and 85% in T2DM. The baselme HbAio was outside of the 20% zone from 6-month HbAic for all but 2 subjects:

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Table 3B: T2DM subjects who experienced change in their HbAi, >= 1 units.
ID HBAO HBA6 DHBA Fl F2 HIT Fl HIT F2 HI'
HBAO
6754 10.8 7.0 3.80 ■ 6.90 9.03 100.00 .00 .00
6361 11.3 7.6 3.70 8.51 10.20 100.00 .00 .00
6270 12.0 8.6 3.40 7.85 10.03 100.00 100.00 .00
6264 11.1 7.8 3.30 8.31 9.70 100.00 .00 .00
6355 11.8 8.6 3.20 ." 7.99 9.90 100.00 100.00 .00
3961 10.8 B.O 2.80 9.13 9.73 100.00 ,00 .00
6555 11.1 8.3 2.80 B.ll 9.55 100.00 100.00 .00
8052 11.7 8.9 2.80 7.68 9.80 100.00 100.00 .00
5356 5.7 7.0 2.70 5.75 8.20 100.00 100.00 .00
3966 10.3 7.7 2.60 8.08 9.07 100.00 100.00 .00
908 9.5 6.9 2.60 ■7.47 8.23 100.00 100.00 .00
6554 10.7 8.1 2.60 8.16 9.42 100.00 100.00 .00
2353 11.1 8.7 2.40 8.99 9.90 100.00 100.00 .00
4064 11.3 8.9 2.40 7.89 9.88 100.00 100.00 .00
6351 10.1 7.7 2.40 7.92 B.63 100.00 100.00 .00
7551 12.2 9.8 2.40 9.17 11.02 100.00 100.00 .00
6358 8.4 6.1 2.30 7.00 7.32 100.00 .00 .00
3965 10.1 7.8 2.30 7.83 8.64 100.00 100.00 .00
914 11.1 S.8 2.30 9.57 10.33 100.00 100.00 .00
1603 10.2 7.9 2.30 8.02 8.88 100.00 100.00 .00
1708 10.8 8.6 2.20 7.62 9.24 100.00 100.00 .00
3761 12.4 10. 2 2.20 9.13 10.B6 100.00 100.00 ,00
37 68 11.2 9.0 2.20 8.29 9.74 100.00 100.00 .00
326 10.3 8.2 2.10 7.45 8.78 100.00 100.00 .00
109 9.3 7.2 2.10 7.70 8.18 100.00 100.00 .00
1501 11.9 9.8 2.10 8.52 10.18 100.00 100.00. .00
3964 13.7 11.6 2.10 10.08 12.65 100.00 100.00 100.00
4352 12.2 10.1 2.10 9.51 11.14 100.00 100.00 .00
7858 12.1 10.0 2.10 9.53 11.01 100.00 100.00 .00
4256 10.6 8.6 2.00 8.76 9.69 100.00 100.00 .00
47S2 10.1 8.1 2.00 8.51 a. 87 100.00 100.00 .00
6556 11.1. 9.1 2.00 8.72 9. 66 100.00 100.00 ■.00
65 62 7.9 5.9 2.00 7.07 7.04 100.00 100.00 .00
8255 10.9 8.9 2.00 8.90 9.87 100.00 100.00 .00
In Tables 3A aad 3B:
ID - sul^ject's ID niOTiber;
HBAO - baseline HbAic;
HBA6 - measured 6-month HbAic;
DHBA - absolute difference between baseline and 6-monl3i HBAic;

Fl - Estimated HbAic by Function Fl, SMBG data only;
F2 - Estunated HbAjc by Function F2 using prior HbAic assay;
Hit Fl = 100 if Fi is within 20% of 6-montb Hbaic reading, 0 otherwise;
Hit F2 = 100 itF2 is within 20% of 6-month Hbau reading, 0 otherwise, and
Hit HbAO = 100 if baseline HhAic is within 20% of 6-month Hba:c reading, 0 otherwise,
Detailed Results - Trainine Data Set
This section describes the steps to optimization of Algorithm 1. This optimization included two parts; (1) Assuming that no previous HbAic reading is available, and (2) Assuming that a prior HbAic could be used for prediction of HbAio.
Several different functions were considered for description of the relationship between SMBG data and HbAu. Optimal, in terms of accuracy and simplicity of computation, appeared to be a linear function of the average of SMBG readings. Low and High BG Indices, if no prior HbAlc reading is used and another linear function of a prior HbAlc and the High BG Index. Nonlinear relationships did not enhance the goodness-of-fit of the models and therefore are not coBsidered for practical application.
Trainine Data set 1 -No prior HbAir A linear regression model was used to optimize the coefficient of function F2. The optimal coefEcients were presented in the previous sectioru Here we give data about the goodness-of-fit of the model:
Multiple R .71461
R Square .51067
Analysis ofVariance
DF Sum of Squares Mean Square
Regression 3 154.57097 51.52366
Residual 90 148.10903 1.64566
F= 31.3Q8S9 SignifF= .0000
Analysis of the residuals of this model showed a close to normal distribution of the residuals (see FIG. 11). The SD of the residuals was 1.2 (the mean is 0 by definition). Therefore we can accept that this model described the data well.

Training Data set 2 - Prior HbA !„■ Again, a linear regression model was used to optimize the coefficient of function F2. The optimal coefficients were presented in the previous isection. Here we give data about the goodness-of-fit of the model;
Multiple R .86907
R Square .75528
Analysis of Variance
DF Sum of Squares Mean Square
Regression 4 38.70237 ' 9.67559
Residual 54 12.54000 .23222
F= 41.66522 SignifF= .0000
Analysis of the residuals of this model showed a close to normal distribution of the residuals (see FIG. 12). The SD of the residuals was 0.47. Therefore we can accept ■ that this model described the data well.
In addition, comparing the models without and with a prior HbAl c, we can conclude that if a prior HbAlc is available for inclusion in.the computations, the resulting model is substantially better both in terms of R^ and in terms of residual error.
However, as we saw in the previous section, a prior HbAic does not contribute to the overall accuracy of prediction in an unrelated dataset and, in certain cases when HbAlc changed substantially, is even obstructing the ability of the algorithm to account for rapid changes. Thus, we can conclude that, even if a prior HbAic maybe better from a statistical point of view, it may not have a sufficient practical utility to j ustify an inclusion of input of a reading in fiiture meters. We also don't know what could be the elapsed time between an HbAlc assay and SMBG profile that would still render the HBAlc input isefiil. Perhaps this depends on the change of HBAlc during that time period- as we aw in the previous section, a change of 2 HbAlc units makes a prior HbAl c reading ompletely useless.
:he Ratio of SMBG to HbAu
We will now present an alternative way to improve the statistical accuracy of the lodel fit and to keep a reasonable cimical ^plicability. It turns out that the ratio

between tiie average of 45 days of SMBG readings and HbAk is a measure that has an almost perfect norma! distribution (as evidenced by Kolmogorov-Smimov test) and, most importantly, identifies three groups of subjects for whom this ratio is below 1.0, between 1.0 and 1.2,andabove 1.2. Each ofthe first two groups accounts for approximately 40% of the subjects, the third group accounts for approximately 20%) ofthe subjects. This is valid for both TIDM and T2DM and is observed in the traming as well as in the test data sets. In addition, this jatio seems to be pretty stable over time and is perhaps a measure that reflects patients SMBG habits (for example, if SMBG is performed mostly at times when BG is low, the resulting average will underestimate HbAic and the corresponding ratio will bebelow 1.0). Keeping in roind that this is just a hypothesis that cannot be validated with the available data, we make some analyses that seem to demonstrate certain utility in knowing each person's ratio at some point of time. This may seem equivalent to knomng a prior HbAic, and it is perhaps equivalent in terms of data input, however the me of th6 ratio is very different than the use of a prior HbAio.' Instead of being included directly into the prediction formuja, the ratio is used to classify the person into usii^ one of three different prediction formulas. These new formulas do not include HbAio directly and therefore do not suffer by the inertia of prediction that such inclusion may cause, hi addition the average HbAu is not substantially different between the three groups defined by the ratio and is not correlated to the ratio, so the reason for different ratios in different persons must be imrelated to HbAio.
If we first classify the subjects in three groups according to thek ratio and perform separate regression in the training data set, the goodness-of fit ofthe regression models increases substantially: (1) In group 1 (Ratio 1.2) the fit is worst R=0.69, R^=0.47. Since all three re^ssion models do not include a prior HbAio, we can conclude that the goodnessnsf-fit mcreases dramatically for about 80%i ofthe subjects, remains the same for the rest 20% of the subjects, and these subjects for whom the fit will be worse can be identified in advance.

Further, separating the test data set in three groups according to the subject's ratio, we get prediction accuracy similar to the accuracy we have achieved before (Tables 4A and 4B):
Table 4A! Accuracy of Aleorilhm t in TIDM fN=254 subjects').
Ratio 1.2
AERR 0.70 0.63 ,0.74 ■
PERR (%) 7.8 7.4 7.9
H1T20 (%) 93.8 93.0 95.5 -—
HITIO (%) 68.8 73.4 72.7

MiSS25(%) 3.1 2.6 0.0

Table 4B: Accuracy of Akorithm I inT2DM n^=319 subiectsl.
Ratio 1.2
AERR 0.63 0.68 0.89
PERR(%) 7.6 7.8 8.8
HIT20{%) 97.4 95.0 95.3
HITIO (%) 67.2 65.3 57.7
MISS 25 (%) 0.0, 1.7 0.0 ,
In short, knowing the SMBG-to HBAlc ratio for each subject and using separate estimates accordingly, seem to improve the statistical performance of the models without losing clinical accuracy.
Other hypotliesea and ideas that were tested
We have tested a number of other hypotheses and ideas which may prove useful at least for prompt and more focused analysis of the data that are collected by Example No. 2, A brief account of the results follows:

(1) HbAic is most associated (correlated) with SMBG readings taken in the afternoon hours - from 12 noon to 6 p.m. and least associated with fasting SMBG readings (4 a.m. - 8a.m.). However, it does not follow that taking only postprandial SMBG readmgs would improve the prediction of HbAic. on the contrary, the prediction would become worse if the [relatively small but important] contribution of al! hours throughout the day is ignored. It is possible to unprove a the prediction of HbAic a little if different hours throughout the day get different weighting, however the improvement is not sufEcient to justify this additional complication of the model;
(2) The relationship between HbAie and average SMBG is substantially stronger ,m T2DM compared to TIDM, even if the two groups are matched by HBAie. In terms of direct correlation, in TIDM the coefficient is about 0.6 while in T2DM the coefficient is about 0.75 throughout the studies;
(3) Experiments with different weighting of SMBG reading dependent on the elapsed time between SMBG and PBAic assay (such as weightmg higher more projcimal results) did not yield better prediction of HB Aic;
(4) Inclusion of demographic variables, such as age, duration of diabetes, gender, etc., does not improve the prediction of HBAlc;
(5) The simplest possible linear relationship between HbAic and average SMBG (measured in mmol/1) is given by the formula: HbAic = 0.41046*BGMM + 4.0775. Although statistically inferior to the formulas Fl and F2, this formula provides HbAic estimates that are about 95% accurate in both TIDM and T2DM (in terms of deviation less than 20% from HbAic assay) and maybe usefiil if the computation of the Low and High BG Indices presents an issue for incorporation in a meter (however, the prediction of hypoglycemia cannot be done without computing the Low BG Index and therefore this formula might be usefiil only for meters that include Algorithm 1 but not include Algorithms 2 and 3).

ALGORITHM 2; EVALUATION of LONG-TERM RISK for SH.
Example No. I provides for, but not limited thereto, an expansion of Algorithm 2 to include estimating individual probabilities for biochemical significant hypoglycemia (BSH, defined as BG reading Algorithm 2 is a classification algorithm. That is, based on SMBG data for a subject, it classifies the subject in a certain risk category for future BSH or MSH. In order to approxunate as closely as possible future real applications of Algorithm 2 we proceeded as follows:
(4) First, several optimal classification variables and optmial classification categories optimal duration, and optimal fi^quency of SMBG were derived^fi:om training data set 1;
(5) Then, the test data set was split into two sections: fmst 45 days, and the rest of the data. The optimal parameters of Algorithm 2 were applied to the first 45-day portion of the data and the so estimated probabilities for fiiture BSH or MSH were used to predict BSH and MSH in the second portion of the data;
(6) Detailed estimation of the preciseness of Algorithm 2, was made using test data only.
This separation of training and test data sets allows us to claim that the estimated preciseness of Algorithm 2 can be generalized to any other data of subjects vwth TIDM or T2DM. Moreover, since the Amylm data were collected from subjects who were undergoing intensive treatment, we can speculate that Algorithm 2 is tested and proven useful in subjects with changing and increasing risk for hypoglycemia.
Summary of the Results
■ The optunal SMBG data collection period needed for estimation of the probabiHty for fimire BSH or BMH is 40 to 45 days. The optimal frequency of SMBG is 3 to 4

readings per day. Larger number of readings does not lead to a substantial increase of the predictive power of Algorithm 2. With less than 3 reading per day the predictive power declines. However, this requirement refers to average number of readings per day for the 45-day observation period, it does not necessarily mean that 3-4 readings need to be performed every day;
■ The relationship between predictor variables and future SH and MH is strictly
nonlinear. Consequently, linear methods are not applicable for optimal prediction,
although an R^=50% can be achieved by a direct linear model (in comparison, the best
result in the DCCT was 8% prediction of future SH);
• A separate prediction of nocturnal SH is generally weaker than prediction of daytime
SH;
■ Fifteen risk categories for future BSH and BMH were identified. The best separation of categories was achieved on the basis of the Low BG Index alone, although combinations between the low BG Mdex and other variables worked similarly well;
■ Although the frequencies of BSH and BMH were different between TIDM and T2DM (see Table 5), the conditional frequencies, given a risk category, were not different between TIDM and T2DM. This allowed for xmified approach to the risk of SH and MH;
■ Various empirical probabilities for future were computed and compared for the 15 risk categories. All comparisons were highly significant, p's ■ These empirical probabilities were approximated by a two-parameter WeibuU distributions yielding theoretical probabilities for future BSH and BMH in each risk category.
• The goodness-of-f!t of these approximation was very good - all coefficients of
determination were above 85%, some as high as 98% (see FIGS. 1-5 and 9-10).
Detailed Results - TestDgtaSet
Identifvine personal risk cateeories for SH/MH: The data for aU 600 subjects were used for these analyses. The Low BG Index (LBGI was computed for each subject from his/her first 45 days of SMBG data collection. Then, the LBGI for was classified in one

of the 15 optimal risk categories (variable RCAT rangmg from 0 to 14) as derived in
training data set 1. These rislc categories are defined by the inequalities:

if(LBGIle0.25)RCAT=0. if (LBGI gt 0.25 and LBGI le if (LBGI gt 0.50 and LBGI le if (LBGI gt 0.75 and LBGIle if(LBGIgt 1.00 andLBGIle if (LBGI gt 1.25 and LBGIle if(LBGIgt 1.50 and LBGIle if (LBGI gtl.75 and LBGIle if(LBGIgt 2.00 and LBGIle if(LBGIgt 3.00 and LBGIle if(LBGlgt 3.50and LBGIle if (LBGI gt 4.00 and LBGI le if (LBGI gt 4.50 and LBGI le if (LBGI gt 5.25 and LBGI le if (LBGI gt 6.50) RCAT=14.

0.5)RCAT=1.
0.75) RCAT=2.
1.00)RCAT=3.
1.25)RCAT=4.-
1.50)RCAT=5.
1.75)RCAT=6.
2.00) RCAT=7.
2.50)RCAT=8.
3.50)RCAT=9.
4.00)RCAT=10.
4.50)RCAT=11.
5.25)RCAT=12.
6.50)RCAT=13.

Observed frequency of BSH and BMH: For each subject, any occurrences of BSH and BMH registered by SMBG were counted for 1-month, 3-month, and 6-month periods following the initial 45-day data collection, Table 5A presents the observed frequencies of 0, >= 1, >=2. and >= 3 B SH and BMH for T1 DM, Table SB presents the same data for
T2DM:

Table 5A: Observed freauencv of BSH and BMH in TIDM
BSH(BG 1 month 3 months 6 months 1 month 3 months 6
months
Average #/Subject 0.82 1.77 2.74 3.64 8.33 12.93
% Ss with 0 episodes 62.8 50.8 46.6 25.2 18.0 17.7
% Ss with > 2 episodes 18.8 33.1 38 64.3 75.6 77.1
% Ss with > 3 episodes 9.8 23.3 28.2 50.8 68.0 71.1

Table 5B: Observed freauencv of BSH and BMH in T2DM
BSH(BG I month 3
months 6 months I month 3 months 6
months
Average # / Subject 0.18 0.53 0.76 1.11 2.93 4.59
% Ss with 0
episodes 91.4 84.9 81.3 73.0 61,1 55.8
% Ss with > 2 episodes 3.6 8.6 10,1 18.1 26,7 30.3
% Ss with > 3 episodes 1.5 5.9 7.4 13.9 21.4 25.8
Nocturnal BSH and BMH represented approximately 15% of all episodes registered by SMBG. As in the training data set the correlation between nocturnal episodes and all predictor variables was weaker. We conclude Ihat a targeted prediction of nocturnal episodes would be ineffective.
Enmirical Pr^babUUies for future BSH and BMH: Certain empirical probabilities for fiiture BSH and BMH were computed in each of the 15 risk categories. These probabilities include: (1) Probabilities for at least one BSH or BMH within the next 1 month, 3 months, and 6 months; (2) Probabilities for at least two BSH or BMH within the next 3 months and 6 months, and (3) Probabilities for at least three BSH or BMH within the next 6 months. Of course, it is possible to compute any other combinations probabilities upon request.
A most important conclusion &om this analysis was that, given a risk category, the probabilities for future BSH and BMH did not differ significantly between TIDM and T2DM. This allows for an unified approach to empirical and theoretical estimation of these probabilities in both TIDM and T2DM. Consequently, flie data for TIDM and T2DM patients were combined for the following analyses.
Fl
presented by black triangles, Virhile the empirical probabilities for BMH are presented by red squares.
All sets of empirical probabilities were compared across the 15 risk categories using univariate ANOVAs, and all p-levels were below 0.0005. Therefore, we observe highly significant differences between the fi-equencies of BSH and BMH episodes in the different risk categories.
Theoretical Probabilities for future BSH and BMH: Jn order to be able to use direct-formula estimation of the probabilities for future BSH and BMH, we approximated the empirical probabilities using two-parameter Weibull probability distribution. The Weibull distribution fimction is given by the formula:
F(x) — 1 - expf-a.x') for any x>0 and 0 otherwise
Statistical Note: The parameters a and b are greater than 0 and are called scale and shape parameter, respectively. ID the special case b=l, Weibull's distribution becomes exponential. This distribution is fi^quently used in engineering problems as the distribution of randomly occurring technical failures tiiat are not completely unrelated to each other (If the failures are completely unrelated, then they would form a Poisson process that would be described by an exponential distribution, e.g. b=l). The situation here is remotely similar - we need to describe the distribution of events (failures) that are not completely independent and tend to occur in clusters as evidenced by our previous research.
Each set of empirical probabilities was approximated by the theoretical formula given above. The parameters were estimated using nonlinear least squares (with initial parameter estimates given by a linear double-logarithmic model). The goodness-of-fit of each model was evaluated by its coefficient of determination (D^). This statistics has a meaning similar to that of R^ in linear regression, however R^ is not applicable to nonlinear models.
. The model fits are presented in FIGS. 1-6 as black Iraes for the probabilities of BSH and as dashed lines for the probabilities of BMH. Above each figure we present the

parameter estimates for the corresponding models, thus we give direct formulas for computing probabilities of 0, >=1, >=2, >=3 BSH or BMS episodes jn periods of 1 month, 3 months, and 6 months following initial SMBG. Some of these formulas, or their versions, can be included in monitoring devices or software as indicators of risk for SH andMH.
The values ofD^ (and its square root D) are given below each figure as indicators ofthepreciseness of approximation. All values are above 85% and some reach 98%, which demonstrates that the approximation is very good and confirms that theoretical, instead of empirical probabilities could be used in future studies/appUcation.
The theoretical probabilities for one or more moderate or severe hypoglycemic episodes are given by the formulas as shown in FIG. 1:
P (MH >= 1) = 1 - exp (-exp (-1.5839) * Risk** 1.0483)
P (SH >= 1) = 1 - exp (-exp (-4.1947) * Risk** 1.7472)
FIG. 1 presents the empirical and theoretical probabilities for moderate (dashed line) and severe (black line) hypoglycemia within one month after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index. Since the models are nonlinear, the goodness-of-fit is evaluated by their coefficient of determination D^, an analog of R^ in linear models. The coefficients of determination and their square roots are as follows;
SH Model: D^ = 96%, D=98%. MHModel: D'= 87%, D = 93%.
The theoretical probabilities for one or more moderate or severe hypoglycemic episodes are given by the formulas as shown in FIG. 2:
P(MH>=l) = l~exp(-exp(-I.373I)* Risk** 1.1351) P (SH >= 1) = 1 - exp (-exp (-3.2802) * Risk** 1.5050)

FIG. 2 presents the empirical and theoretical probabilities for moderate (dashed line) and severe (black line) hypoglycemia within three months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index.
The coefScients of determination and their square roots are as follows:
SH Model: D^ = 93%, D=97%.
MH Model: D^ ^ 87%, D = 93%.
The theoretical probabilities for one or more moderate or severe hypoglycemic episodes are given by the formulas as shown in FIG. 3:
P (MH >= 1) = 1 - exp (-exp (-1.3721) * Risk** 1.3511)
P (SH >= 1) = 1 - exp (-exp (-3.0591) * Risk** 1.4549)
FIG. 3 presents the empirical and theoretical probabilities for moderate (dashed hne) and severe (black line) hypoglycemia within six months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index.
The coeiBcients of determination and their square roots are as follows:
SH Model: D^ = 86%, D=93%.
MH Model: D^ = 89%, D = 95%.
The theoretical probabilities for two or more moderate or severe hypoglycemic episodes are given by the formulas as shown in FIG. 4:
P (MH>= 2) = 1 - exp (-exp (-1.6209) * Risk** 1.0515)
P (SH >= 2) = 1 - exp C-exp (-4.6862) * Risk** 1.8580)
no. 4 presents the empirical and tiieoretical probabilities for 2 or more moderate (dashed line) and severe (black line) hypoglycemic episodes within three months after the SMBG assessment for each of the' 15 categories of risk level defined hy the Low BG hidex.

The coefficients of determination and their square roots are as follows:
SH Model: D* = 98%, D=99%. MH Model: D* = 90%, D - 95%.
The theoretical probabilities for two or more moderate or severe hypoglycemic episodes are given by the formulas as shown in FIG. 5:
P (MH>=2) = ] -exp (-exp (-1.7081) * Risk** 1.1955)
P (SH >= 2) = 1 - exp (-exp (-4.5241) * Risk** 1.9402)
FIG. 5 presents the empirical and theoretical probabilities for 2 or more moderate (dashed line) and severe (black line) hypoglycemic episodes within six months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index,
The coefScients of determination and their square roots are as follows:
SH Model: D^ = 98%, D=99%.
MH Model: D^ = 89%, D = 95%.
The theoretical probabilities for three or more moderate or severe hypoglycemic episodes are given by the formulas as ahovm in FIG. 9:
P (MH >= 3) = 1 - exp (-exp (-2.0222) * Risk** 1.2091)
P (SH >= 3) = 1 - exp (-exp (-5.5777) * Risk** 2.2467)
FIG. 10 presents the empirical and theoretical probabilities for 3 or more moderate (dashed hne) and severe (black line) hypoglycemic episodes withm six months after the SMBG assessment for each of the 15 categories of risk level defined by the Low BG Index.
The coefBcients of determination and their square roots are as follows:

SH Model: D^ = 97%, D=99%.
MH Model: D^ = 90%, D = 95%. Detailed Results - Trainine Data Set
The training data set contained SMBG data followed by monthly diaries of severe hypoglycemia. As opposed to the test data set where BSH and BMH were identified by cutoff BG values, the monthly diaries contained report of symptomatic severe episodes defined as unconsciousness, stupor, inability for self-treatment, or significant cognitive impairment due to hypoglycemia. Within 6 months following SMBG the subjects reported on average 2.24 such episodes per person with 67% of the subjects reporting no such episodes. From a statistical point of view, ihis alone makes fee distribution of SH episodes substantially skewed and unsuitable for application of linear methods. Nevertheless linear regression could be used to evaluate the relative contribution of various variables to the prediction of SH, but not for building the final model. We performed the following three analyses;
(1) No knowledge of SH history: Ignoring any knowledge of history of SH, we used regression to predict future SH from baseline HbAic and SMBG characteristics such as average BG, Low BG Index, and estimated BG risk rate of change (all variables are described in the original invention disclosure). As repeatedly found before, HbAic and average BG did not have any contribution to the forecast of SH. The final regression model included the Low BG Index and the BG risk rate of change and had the following goodness-of-fLt;
Multiple R .61548
R Square .37882
Rnalysis of Variance
F - 27.74772 Signif F.= .0000
—. Variables in the Equation
Variable B SE B Beta T Sig T
LBGI 4.173259' .649189 2.104085 6.428 .0000
RATE -5.749637 '1.091007 -1.724931 -5.270 .0000
(Constant) -2.032859 .790491 -2.572 .0117

(2) Knowledge of prior SH: When we included the number of SH episodes in the previous year as reported in a screening questionnaire, this variable accounted for an additional 11% of the variance of ftiture SH:
Multiple R . .70328
R Square .49461
Analysis of Variance
F = 29.35999 Signif F = .0000

Variable B SE B Beta T Sig T
SH .337323 .074286 .375299 4 .541 .0000
LDR -4.350779 1.036380 -1.305264 -4 .198 .0001
RLO 3.134519 .631684 1.560371 4 ,962 .0000
(Constant) -2.136619 .717334 -2. ,979 .0037
(3) Without knowledge of the number of prior SH, just knowing whether a person had or did not have prior SH, we were able to account for 45% of ftie variance of future SH using only SMBG variables;
(4) Finally, two separate linear models accounted for 55% of the variance in daytime SH vs. 25% of the variance in nocturnal SH. The direct correlations of all predictor variables with nocturnal SH were also weaker. Nocturnal episodes represented 30% of all SH.
We conclude that a linear predictive model could directly account for about 40 to 50% of the variance of future SH. However, such a model is not well balanced in terms of its residual errors (which is due to the highly skewed distribution of die number of SH episodes across tiie diabetic population). A statistical evidence for that is given by the normal probability plot of FIG. 13, which shovfs a substantial deviation of the standardized residuals from their expected values:
Thus, we adopt anodier approach to predicting SH based on classification of subjects mto risk categories using their SMBG data and estimation of the probabilities for subsequent SH in these categories. We attempted various classification models

maximizing the difference between the risk categories and trying to achieve a maximum resolution of risk evaluation (in terms of maximal number of categories).
The best results were achieved by the classification based on the Low BG Index alone that had 15 risk categories (presented in the beginning of the previous section).
In addition to its best separation between categories, this result has other advantages as well: (1) No prior knowledge of history of SH is required; (2) The calculation is relatively simple and does not require tracking of temporal variables such as BG rate of change, and (3) The classification appeared to be equally applicable to both TIDM and T2DM patients (which is coherent with no requirements for knowledge of prior SH).
ALGORITHMS: EVALVATION of SHORT-TERM RISK FOR HYPOGLrCEMU
Example No. 1 provides for, but not limited thereto, an optimization of Algorithm 3 in terms of:
(1) Utilization of baseline long-term risk (^m Algorithm 2) and HbAio (fcom Algorithm
1);
(2) Risk criterion/threshold for hypoglycemia alert;
(3) Frequency of SMBG;
(4) Whether a hypoglycemia alert should be issued if an mcreased risk for hypoglycemia is detected and there is no SMBG for certain period of time, and
(5) Contribution of demographic variables such as history of severe hypoglycemia.
Introduction
As opposed to Algorithms 1 and 2, which have a longer history of development, Algorittun 3 deals wth a proposition ftrnt was, until recently, considered impossible, hi fact, there is still a general perception that prediction of any future BG value (hypoglycemia in particular) is not possible on the basis of previously known values (Bremer T and Gough DA. Is blood glucose predictable from previous values? A solicitation for data. Diabetes, 1999,48: 445-451.). Our previous work, reported in one manuscript and presented in detail in the invention disclosure available to Ltfescan, Inc.

disputes this general perception. In order to explain the basis for this dispute and to clarify the reasoning behind Algoiitkn 3, we inclucle the following paragraph.
Our "philosophy" in auantifyitte characteristics of diabetes: Hormonal interactions are governed by dynamic-control biochemical networks that have a more or less complex structure of principal nodes and conduits, depending on the studied endocrine system. Diabetes disrupts the network control of insulin-glucose dynamics at various levels. For example, in TIDM the natural production of insulin is completely eliminated, while in T2DM the utilization of insulin in the cell is obstructed by a greater insulin resistance. In TIDM (and frequently m T2DM) some form of external insulin replacement is required, which makes the control system vulnerable to imperfect external factors, including the timing and amount of pill or insulin injection, food eaten, physical activity, etc. This frequently leads to extreme BG excursions into hypoglycemia and hyperglycemia. In many, but not in all cases, hypoglycemia triggers an endocrine response, known as counterregulation. Thus, in mathematical terms, BG fluctuations over time are the measurable result of the action of a complex dynamic system, influenced by a number of internal and external factors. However, it is well known from the theory of dynamical systems that when the complexity of control increases, a purely deterministic system evolves to display random macro-behavior. Consequently, within short periods of time (minutes) the BG fluctuations observed at a human level would be nearly-deterministic, while over longer periods of time the fluctuations woidd be nearly-random, including extreme transitions, such as SH episodes. Thus, stochastic modeling and statistical inference are most appropriate for analysis of the system over longer periods of time - a paradigm adopted by Algorithms 1 and 2 that use our originally developed measures, such as the LBGI and HBGI, to predict, after a certain observation period, a range of values, or a probability of an event. Over short periods of time BG fluctuations can be modeled and predicted using deterministic networks, which would be the case with future intelligent insulin-delivery devices linked to continuous monitoring.
Algorithm 3 operates in an mtermediate time scale of a few hours to a few days and therefore requires a combination of statistical inference and deterministic modeling. The former will be used to assess the baseline risk for SH for an individual, while the

latter will be used for a dynamical tracking of individual parameters and forecast of SH spisodes prior to their occurfence. When implemented in a device Algorithm 3 would ivork as follows:
;1) The device collects certain baselme information for the subject and establishes individual baseline pararneters;
[2) Then, the device begins tracking a certam set of properties of the SMBG data;
'3) The device is equipped with a decision-making rule ttiat decides when to raise a flag for upcoming SH and when to lower this flag if the data indicate that the threat is reduced;
4} When the flag is raised, we assume that the subject is warned for SH in the following 24 hours (prediction time).
[his dynamical prediction creates theoretical problems at both the level of model larameter optimization and at the level of evaluation of the preciseness of the optimal
solution. We will begin with clarifying the second problem, as it is most important for
understanding the action of Algorithm 3.
Evaluatins the preciieness {fAlEorithm 3: While Algorithms 1 and 2 employ a static forecast and the criterion for evaluation of these algorithms is theoreticaily apparent - a better predictive value, with Algorithm 3 the optimization criterion is no longer straightforward. This is because by increasing the percentage of predicted SH episodes, we unavoidably increases the number of "raised flags," which in turn increases the number of potential "false alarms." The matter is additionally complicated by the fact that a "felse alarm" is not clearly defined. In its pure form, a false alarm would be a raised flag that is not followed by and SH episode. However, SH could be avoided if the person perceives symptoms and takes an appropriate action. Thus, even if the biochemical potential for SH may present, an event may not occur. In order to deal with this problem we adopt the following optimization criterion:
(1) Maximize the prediction of upcommg SH within 24 hours;
(2) Minimize the ratio Rnd of duration periods of "flag up" to "flag down".

While the first of these two pomts is clear, the second may need an additional explanation. Looking fixim the perspective of an implementation of Algorithm 3 in a meter, at every SMBG determination the meter decides whether to r^se a flag or not to raise a flag for upcoming SH. When the flag is raised, it may stay up for some time (along several subsequent SMBG readings) imtil a decision is made to take the flag down. Thus, we will have an altematmg process of "flag up" and "flag down" with the changes happening at points of SMBG. The ratio Rud referred to in point (2) above, is the average tune for a person, counted while the flag is up, divided to the average time counted while the flag is down.
Our previous best result presented in the Invention disclosure was a prediction of 44% of SH episodes within 24 hours, and Rud=l:7, e.g. one day of high-risk alert was alternating with 7 days of no alert. Since at that time we assumed that the warning period was at least 24 hours, the algorithm was optimized to raise a flag no more frequently than once a week. Given that this analysis was done using data for subjects who were experiencing high rate of SH episodes, this ratio was considered acceptable.
During Example No, 1 of this study we had to use the same data set for refinement of Algorithm 3 since there is no other data available that include simultaneous SMBG records and records of SH. We also used a similar criterion to evaluate the preciseness of Algorithms. However, v/e changed substantially everything else. The tracking of the data, the parameter estimation, all threshold values and the decision-making rule are no longer the same. These changes were caused by a new idea that SH is preceded by cert^ "depletion" of the body's reserves to counterregulate and that tins depletion can be tracked by using SMBG data. ■ The exact implementation of this idea is described in the section "Decision-making Rule." Since the decision-making rule involves a continuous criterion and a somewiiat artificial cutoff, several solutions are presented and one is selected as optimal for fiirther investigatidn. However, upon presentation of these results, we may decide to select anottier solution to be implemented in future applications of Algorithm 3.

Summary of the Results
First, it is important to note that all results presented helow go well beyond statistical significance. As we will see in a few examples in the next section, the observed differences are always highly significant (with p-values below any imaginable significance level). The point of Algorithm 3 is to predict occurrence of SH episodes on an individual basis. The results are;
(1) The minimum baseline observation period is 50 SMBG readings taken over approximately two weeks with a frequency of 3-4 readings a day. After this time each subject is classified in one of two risk groups that later use different decision-making rules;
(2) From the 6 months of data that we have we find that it is sufficient to make this group assignment once in the beginning of observation. Thus, we can assume that about every 6 months the meter would use 50 reading to reevaluate its owner's group assignment;
(3) The optimallag of SMBG tracking is 100 to 150 readings taken with a frequency of 3-4 readings per day. In other words, the optimal decision-making criterion would be based on a computation using all 150 readings in a meter's memory. This was done to simulate the memory capacity of ONE TOUCH ULTRA. In general, good results are achieved using a lag of only 20 readings taken over a week, but a longer lag yields better prediction;
(4) The decision-making rules is based on a new computational procedure that tracks subjects Low BG Index and other related parameters using "provisional mean" computation. Special software was designed to implement this procedure and to process the data that we had available. From a programming point of view, the code needed for implementation of this procedure is only about 20 lines, which includes the computation of the LBGI;
(5) Several decision-maldng rules (using various parameters) were investigated. Regardless of the frequency of SMBG, thfese rules achieved prediction of SH within

24 hours anywhere fi-om 43.4% with Rud=I :25 to 53.4% with Rud=l :7. Thus, compared to our previous result, the prediction of SH within 24 hours increased by 10%;
(6) As an optimal solution for further investigation we choose the decision-making rule that predicted 50% of SH within 24 hours and had R„d=l: 10. The followmg results refer to this optimal solution under different conditions:
(7) The optimal frequency of SMBG is 4 readings per day. If this frequency is achieved, the prediction of SH within 24 hours mcreases to 57.2% with the same Rud=l:10. Other frequencies of SMBG are investigated and reported as well;
(8) Ifweextendthepredicitionperiodto36, or 48 hours, the prediction of SH increases to 57% and 63% respectively, with the same Rud=l:10;
(9) Utilizing baseline information increases substantially the prediction of SH. In fact, the 10% increase over our previous version of Algorithms is entb:ely due to the use of baseline tracking. However, this baseline tracking is now modeled as a two-week period of self-calibration of the meter that does not use any additional input from the subject;

(10) Personal/demographic information, such as history of SH or prior HbAic, does not contribute to a better short-term prediction of SH;
(11) Raising a flag whenever there is a prolonged period of no SMBG activity is not justified. The only times when the meter would issue warning for upcoming SH would be the times of usage. This is because a major part of the prediction of SH is based on the recurrence (clustering) of very low BGs. An assessment of this recurrence is presented in an abstract (Kovatchev et al. Recurrent Hypoglycemia and Severe Hypoglycemia (SH) in TIDM Patients With History of Multiple SH) prepared for the June 2002 ADA meeting (See Appendix).
Detailed Pescription of the Data Processing
The meter stores SMBG readings together with the date and exact time (hour, minute, second) of each reading. Thus, in Training Data set 2 we have for each subject a certain

temporal sequence of SMBG records. During the study, a total of 75,495 SMBG readings (on average 4.0±1.5 per subject per day) were downloaded &om the participants' memory meters. From subjects' monthly diaries, we had the date and time of SH episodes that had occurred. Subjects reported 399 (4.7±6.0 per subject) SH episodes. Sixty-eight (80%) of the participants experienced one or more episodes of SH. These subjects did not differ from those who did not experience SH (the remaining 20% of the subjects) in terms of any of their demographic characteristics.
Pre-ProcessinB ofihehata: Special software was developed iorpre processing of the
data. This included: (1) Assembling of the memory meter data for each subject into a continuous 6-8-month sequence of BG readii^s, and (2) Matching of each subject's records of SH with this sequence by date and time, The latter was performed as follows: for each SMBG reading the time (hours/mmutes) until the nearest SH episode, and the time elapsed &om the latest SH episode, were computed. Thus, it was possible to: (1) time 24-hour, 48-hour, etc. periods backward and forward from each SH episode, and (2) time periods between SMBG readings. Due to the nature of SH (stupor, unconsciousness), no SMBG was performed exactly at the time of SH, thus SH episodes for flie purposed of Algorithm 3 do not include biochemical significant hypoglycemia that was used for Algorithm 2. The average per SH episode minimum elapsed lime between SH and the nearest preceding SMBG reading was 5.2±4.1 hours; 29 SH episodes (7%) were preceded by a SMBG readmg within 15 minutes. For each SH episode, we counted how many SMBG readings were performed within 24h, 36h, 48h, and 72h prior to that episode.
Computing of Baseline Risk Values and Self-Calibration: The Low BG Index for each subject is computed on his/her first SNBG readings. It was determined that the minimum numbei of reading required to compute a baseline LBGI is SO taken over approximately 2 weeks. Therefore for each new meter we need to anticipate an initial two-week self-calibration period during which the meter would be scanning the overall risk for SH of its owner. After the initial period, the person is classified into one of two risk groups: Low-moderate risk (LBGI 3.5, MH

Group), Our test data show that a more precise classification would not fae necessary. This classification allows for different decision-making rules to be used in the LM and MH groups and raises the hit rate of the algorithm by approximately 10% as compared to its original hit rate presented in the invention disclosure.
With the test data re-calibration of the b^eline risk was not necessary. Thus, we can assume that if the person does not undergo changes in treatment, re-calibration would be performed approximately every 6 months. This is consistent with the results of Algorithm 2 showing that the long-term prediction of SH is quite valid for 6 months after the initial observation period.
However, if the person experiences rapid changes in his/her glycemic control, re-calibration maybe required more frequently. The decision for re-calibration can probably be automated and based on observed increasing differences between the running risk value (see the next paragraph) and the baseline LBGI. However, the available data do not allow us to clarify this issue suice the subjects that we observed did not have substantial changes in their risk for hypoglycemia.
Computitts SMBG Parameters: After the pre-processing step, another piece of software was designed to compute SMBG parameters that ■would be used for prediction of imminent SH. This software included;
(1) Computing of a Low BG Risk value (RLO) for each BG readii^ that is done by the following code (here BG is measured in mg/dl, if the units are mmol/l the coefficients are different):
scale=(ln(bg))**!.08405 - 5.381 ri3k=22.765*3cale*scalG
if [bg_l le 112.5) then
RLO=risk else
RLO=0 endif

(2) For each SMBG reading with a sequential number n, BG(n), computing of a running value of the LBGI(n), and another statistics, SBGI(n) that is the standard deviation of the low BG risk values. These two parameters were computed with a certain lag (k) backwards from each SMBG readmg, e.g. included that readmg, BG(n), and (k-1) readings taken prior to BG(n).
(3) The computation of LBGI(n) and SBGI(n) used a new provisional means procedure that is based on the following recursive code:
Initial values at n-k (or at the max (l,n-k) to be exact in order to account for meter readings with a sequential number less than k):
LBGI (n-k) = rlo (n-k) rio2(n-k)=0
Values for any consecutive iteration/' between n-k and «:
LBGI (i) = ((i-iyj)*LBGI G-l) + (l/j)*RLO Q)
rlo2 0) = {Q-mrrhl Q-l) + (l/j)*(RLO (j)-LBGI Q)) **2
After this cycle is completed we have the value of LBGI (n) and we compute
SBGI (n) = sqrt (rlo2 (n))
Since the maximum of n is 150 for ONE TOUCH ULTRA meters, the search for an
optimal lag ^ was performed within the range of k=10 to k=150. Although the difference
in performance was not sipificant, the optimal lag was determined to be k-150 (see the
next section for examples).
Decision-Makine Rule: At each SMBG reading the procedure decides whether to raise a flag vraming for upcoming SH, or not. If the flag is raised, the procedure decides whether to bring it down. These decisions depend on three threshold parameters, a, p, y that work as follows:
For subject at low-to-moderate risk (LM group):
FLAG=0
if (LBGI(n) > a and SBGI(n) > P) FLAG=1
if (RLOm) > (LBGI(n) + 7*SBGI{n))} FLAG=1
For subjects in the moderate-to-high risk group only the second if-statement is active. In other words, the flag is raised (e.g. becomes equal to 1) if both the running

value of LBGI(n) and its standard deviation SBGI(n) exceed certain threshold values, and is also raised if the current value of the low BG risk RLO(n) exceeds the value of LBGI(n) plus y standard deviations.
An heuristic explanation: The values of LBGI(n) and SBGI(n) reflect slower changes in risk for hypoglycemia - it takes a few days of SMBG to substantially change these values. Since elevated LBGICn) means more ftequent and extreme recent hypoglycemia, we can conclude that LBGI(n) and SBGI(n) reflect a persistent depletion (or lack of replenishment) of counterregulatory reserves over the course of several days. In addition, SBGI(n) is a marker of the stability of the system - a larger SBGI(n) indicates that a subjects' BG fluctuations increase and therefore the control system becomes unstable and vulnerable to extreme aberrations. Thus, the first logical expression reflects the notion that SH occurs whenever the couterregulatory defenses are exhausted and the controls (external or internal) become unstable. The second logical expression accounts for acute changes in the low BG risk, triggering a flag whenever the current Low BG risk value suddenly becomes greater than its running average. The fact that for subjecis in the moderate-to-high risk group only the second logical expression is relevant goes along with these subjects' eventual 'permanent depletion" and "permanent instability" status. Since these subjects continuously run low BG values, and thek BG is unstable, any acute hypoglycemic episode would be capable of triggering SH. In general, a flag for severe hypoglycemia is raised either after aperiod of low unstable BG, or after an acute hypoglycemic event tha;t deviates substantially (in a risk space) from the latest running risk average (that maybe ah^ady high). It follows that SH episodes that are not preceded by any of these warning signs will remain unaccounted for by this algorithm. Below in Table 5C we present a sample output that illustrates the action of Algorithm 3 for several subjects:

Table 5C; A Sample Output that Illuatrates the Action of Algorithm 3 for Several Subjects:

ID SG SH FLAG TIME

Herea=5, p=7.5,Y=I.5

3^35 -jQ _QQ _gg 53 75 For subject # 135 the first flag is raised
I2s 77 .00 .00 4l!o9 about 30 hours prior to SH and Stays up for
135 124 .00 .00 35.02 ^^ next reading whish is taken 16 hours
135 51 .00 1.00 30.44 later and 14 hour prior to SH. This latter
135 50 .00 1,00 H . 72 reading and the tivo readings that follow
135 66 .00 135 1.00
135 49 ^.00 1.00 8.30 considerthis episode to be predicted.
Nevertheless, the flag is brought up again about 8 hours and 20 minutes prior to SH.

135 97 .00 .00 140.05
135 130 .00 .00 25.17
135 59 .00 .00 20.20
135 76 .00 .00 5,23
135 41 .00 1.00 .62
135 1.00
219 200 .00 .00 40.72
219 64 .00 .00 37.8 8
219 43 .00 1.00 28.73
219 225 ,00 ' ,00 16.22
219 3a .00 1.00 11.18
219 43 .00 219 75 .00 . 219 1.00
222 156 .00 .00 19.08
222 176 .00 .00 13.23
222 83 .00 ,00 9.72
222 86 .00 .00 T.83
222 42 .00 1.00 4.75
222 1.00
223 228 .00 .00 IS.80
223 149 .00 .00 14.15
223 41 .00 l.OO 5.85
223 1.00
223 110 .00 223 1.00

A second SH episode for this subject is flagged 36 minutes in advance.
This subject gets two warnings -approximately 28,7 and 11.2 hours prior to this SH episode.
This subject gets a warnings approximately 4 hours and 45 minutes prior to this SH episode.
This subject experienced two recurrent SH episodes within 12 hours. The flag is raised approximately 6 hours before the first episode and tiierefore we consider both episodes to be in the predicted risk hi^-risk 24-hour time period.

Each line of this output presents an SMBG reading, or an SH episode (without a reading). ID is subject's ID number, BG is BG level in mg/dl, SH=1 whenever SH

episode occurs. FLAG=1 if Algorithm 3 decides to raise the flag; TIME is the time to the nearest SH episode in hours.
Optimizing the of Las of the Provisional Means Procedure: In a prior publication we have reported that in the period 48 to 24 hours before SH the average BG level decreased and the BG variance increased. In the 24-houi period immediately preceding SH average BG level dropped further, the variance of BG continued to increase, and there was a sharp increase in the LBGI. In the 24-houT period following SH, average BG level normalized, however the BG variance remained greatly increased. Both the average BG and its variance returned to baseline levels v^thin 48 hours after SH (see Kovatchev et al. Episodes of Severe Hypoglycemia in Type 1 Diabetes are Preceded, and Followed, within 48 Hours by Measurable Disturbances in Blood Glucose. J of Clinical Endocrinolo^ and Metabolism. 85: 4287-4292,2000). We now use these observations to optimize the lag of the provisional means procedure, k, employed by Algorithm 3 on the basis of the deviations in the average values of LBGI(n) and SBGI(n) observed within 24 hour prior to SH. In short, the lag for computing LBGI(n) and SBGI(n) was chosen to maximize the difference that these measures display within 24 hours prior to SH compared to the rest of the study, excluding periods immediately after SH when the system is out of balance. The optimal lag was found to be ^150. Tables 6A and 6B present the means of LBGI(n) and SBGI(n) for several values of the parameter k. and for both subject groups, low-moderate risk and moderate-high risk. It is evident that the difference between variousvaluesof (fe is not great, thus in a practical application any value of i> 10 would be appropriate. However, based on the current data we would recommend lc=l 50, and all fiirther computations use this lag. "Hiis recommendation is also based on the reduced variance in LBGI(n) and SBGI(n) at larger lag values, that is reflected by larger t-values below.

Table 6A : LBGWn^ within 1)4 hour prior to SH vs the rest of the time for different laps:
LBGI Low-moderate risk (LM Group) Moderate-hiqh risk (MH Group)

24h prior SH Rest of
the time t P . 24h prior
SH Rest of
the
time t p
k=in Am St^-3 n? i(=?n 4 7.^ a:?4 fl4 k=.in 4fi4 ;i:ifi fli k=fin dR!^ .■^•u OR k=inn 4,4fi 3 59 11 7 n _a5a_ R4n 17,3 k='15d*, 4.46 : a 7R 12.1^^
Table 6S : SBGirn) withm 24 hour orior to SH vs the rest of the time for different laes:
SBGI Low-moderate risk (LM Group) Moderate-hiqh risk (MH Group)
24h prior SH Rest of
.the time t P 24h prior SH Rest of the time t P
k=in 7fiO ■ fi?ifi fin k=?n fl.li B7fl in 1 k=rin a fin fiRR Qfi k=fin R7R 704 inn k=1fVl flifl 7Sa 11 7 k=i'in* 043 '7 4? 1:4V Optimal solution
As seen in Tables 6A and 6B both LBGI and SBGI become hi^y significandy elevated in the 24-hour periods preceding SH. Thus, one is tempted to run a direct discriminant or logistic model to predict upcoming SH. Unfortunately such standard statistics don't woric very well, although both models are highly statistically significant. The discriminant model (that worked better than logistic regression) predicted correctly 52.6% of upcoming SH episodes. However, its flag-up to flag-down ratio was quite poor - Rud=I:4. Therefore this model was biased towards the larger amount of data points, a

bias that is to be expected in any statistical procedure. Consequently, we had to employ the decision-making rule presented above.
Accuracy of Prediction of Severe Hypoglycemia
Optimization of the Threshold Parameters a, B. and y: Below we present a detailed account of the predictive power of Algorithm 3 using various combinations of its threshold parameters a, p, and y. Since the relationship between these parameters and the desired outcome (high prediction of SH and minimal ratio Rud) is quite complex, the optimization procedure that we used did not reach a single solution. Also, it seems that there is no need for a single solution either. It is probably a business, rather than mathematical decision, what would be an acceptable % of prediction of SH, given a "flag up" to "flag down" ratio. Thus, we do not claun that any of the presented below solution is optunal. However, m order to explore this subject further, we accept that a 50% prediction of future SH with Rud = 1:10 is a base for investigating other than 24 hours . prediction periods as well as various reqiurements for the number of SMBG readings per day required for a better risk profile.
Table 7 presents the performance of Algorithm 3 at several combinations of the values of a, P, and y that are representative for the relationship between the percentage of predicted SH (hit rate) and the ratio Rud which we could call "annoyance index." Table 7 also includes the average total time (in days) per subject spent in alert vs. no-alert status during the study, i.e. the summary result from tiie alternating process of warning - no-waming periods that a subject would experience using this algorithm which illustrates the meaning of the ratio Rud.

Table 7: Prediction of SH: Hits. Aimovance Index, and Averaee Times:
Total for study (days)
a P Y %Hit Rud Flag up Flag Down
6.4 8.2 1.5 43.4 1:25 7.8 198.9
6.0 7.5 1.5 45.2 1:20 96 197.3
5.5 7.5 1.5 47.2 1 : 15 , 12.9 194.1
.5,0 T.S - 1.5. 49.9 ■ 1:10 , .".,-19.0 ,' 190..1
5.0 7.5 1.3 51.3 1:9.5 19.5 185.7
4.9 7.0 1.2 ■ 53.1 1:8.4 21.6 182.0
4.8 7.0 .-1.2 53.4 1 :7 25.5 178.2
The highlighted solution is used for all fiirther analyses. Given that the participants in this study experienced 4.7 SH episodes on average, 19 days of high-alert periods seem to be acceptable, if these alerts would prevent 50% of SH. In addition, high-alert periods tend to come in clusters. Therefore we can assume that in practice, long and relatively calm periods will alternate with a few days of high-risk warnings. The last line in Table 7 presents a solution with a Rud - 1:7, which is equivalent to the solution presented in the invention disclosure. However, the current solution has almost 10% higher hit rate, 53.4% compared to 44% in our previous algorithm. When the hit rate is comparable to our previous algorithm, the annoyance ratio is below 1:20, i.e. three times better.
FIG. 14 presents fiie smoothed dependence between tiie hit rate and the ratio Rud expressed in percentage. It is evident that the ratio between "flag up" and "flag down" increases rapidly vrfien the hit rate of Algorithm 3 increases. Thus, given these data it maybe unjustified to pursue parameter combinations resulting in a higher than 50% hit rate:
Alternative Prediction Periods: In the beginning of the description of Algorithm 3 we made the basic assumption that an SH episode would be considered predicted if the flag is

raised within the 24-hour period of time preceding this episode. This assumption resulted in the hit rates reported in the previous section, We will now present computations of the hit rate based on other prediction periods ranging from 12 to 72 hours. Throughout this experiment the parameters a, p, and y remain fixed at 5.0, 7.5, and 1.5 respectively, i.e. at their values in the solution highlighted of Table 7. Therefore the flag-i:^ rate remains the same as in this solution with Rnii=l: 10, and only the hit rate changes since we change the definition of a hit. FIG, 15 presente the dependence between the prediction period and the corresponding hit rate.
It is evident that the hit rate increases rapidly with the increase of the prediction period to about 24 hours and then the increase of the hit rate gradually slows down. Therefore, we can conclude that 24 hours ahead is an optimal and a reasonable forecast period.
Optimal Number ofSMBG Readings Per Day. Finally we experiment with the requirement of how many readings per day are needed in order to produce an optimal
forecast of SH.
As we said in the beginning, all reported SH episodes were 399. Of these episodes 343 had any SMBG reading available in the preceding 24 hours (additional 3 episodes had any reading wdthin the preceding 48 hours and additional 4 episodes had any reading within the preceding 72 hours). It follows that more than 50 SH episodes (14%) did not have any reasonable preceding SMBG reai^ng that would help with their prediction. The 343 episodes that had at least one prior SMBG reading within 24 hours were used for the computation of the hit rates in the previous section. The other episodes were naturally excluded from the computation.
Further analysis shows that the hit rate increases rapidly with the number of readings taken before an SH episode. However, if we impose a strict requirement for a certain number of readings to be available in order to consider an SH episode, we see that the number of SH episodes that meet this requirement rapidly decreases (Table 8), This is due to subjects' non-compliance with the study requirements and is maybe a good reason to incorporate in fiiture meters some sort of a warning message that Algorithm 3

will not be useful and would be switched off if there are no SMBG readings taken at an appropriate rate.
Table 8 presents the number of SH episodes that had available certain nurnber of preceding SMBG re^ngs and the hit rate of Algorithm 3 for these episodes. The highlighted TOW of thb table contains the optimal solution &om Table 7 that was iised as a base for all subsequent computations. All hit rates are given in terms of a 24-hour prediction period, i.e. flag within the 24 hours preceding SH. We can conclude ttiat widi an increased subjects' compliance the accuracy of Algorithm 3 in prediction SH would increase substantially. With 5 SMBG rea&ng per day the accuracy is up 10% from its base of 50% hit:

Table 8: Performance of Aleoritiim 3. Given a Certain Number of Prior SMBG Readings
Number of Preceding SMBG Readings SH episodes that satisfy the requirement in column 1
(% of total number of SH) Hit Rate
At least 1 withit^ 24 hoUre ■ 343 (86%) , . T ■ 49.9%
At least 3 within 24 hours 260 (65%) 54.2%
At least 4 within 24 hours
— _i , 180 (45%) 57.2%
At least 5 within 24 hours 103 (26%) e4.i%
At least 4 within 36 hours 268 (67%) 52.6%
At (east 5 vmthln 36 hours 205 (51%) 54.6%
At least 6 within 36 hours 146 (37%) 60.3%
At least 7 within 36 hours 107 (27%) 60.7%
At least 6 within 48 hours 227 (57%) 53.3%
At least 7 within 48 hours , 187 (47%) 54.0%
At least 8 wflthin 48 hours 143 (36%) 55.9%
At least 9 \Mthln 48 hours
107(27%) 59.8%

other Potential Enhancements That Were Tested
The attempte to increase the predictive power of Algorithm 3 by inclusion of external parameters, such as number of SH episodes in the previous year, or baseline HbAlc were unsuccessfiU. Evidently, the short-term prediction of SH is mainly dependent on current or recent events. However, a limitation of this study is that all participatmg subjects had a history of > 2 SH episodes in the previous year.
Finally, we tested whether an alert for SH should be issued if an increased risk for hypoglycemia is detected and there is no SMBG for certain period of time. This was done in an attempt to predict at least some of the SH episodes that were not preceded by any SMBG readings. This was not successful, generating predominantly false alarms. This result comes as an additional confirmation of the importance of compliance with an SMBG protocol comprised of sufficiently frequent SMBG readings.
Appendix: Abstract
Example No. 1 evaluates the fi-equency of recurrent hypoglycemia and SH (defined as stupor or unconsciousness that preclude self-treatment) following a low blood glucose (BG Ejghty-five patients (41 female) vrithTlDM and history of >2 episodes of SH in the last year performed SMBG 3-5 times per day for 6 to 8 months and recorded in diaries any SH episodes by date and time. Subjects' average age was 44±10 years, duration of diabetes 26±11 years, HbAic7,7±l.l%.
All SMBG readings (n=75,495) were merged by date and time with subjects' SH episodes (n=399; SH events generally do not have a corresponding SMBG reading). For each SMBG reading, or SH episode, the elapsed time since the nearest previous low BG (
SH episodes) are randomly distributed across time. The negative Z-values of the tests show "clustering" of days with and without hypoglycemic readings or SH episodes.
Table 9: Percentage of hypoglycemla/SH preceded by a low BG:
BG 72h. Runs Test

Z p-l6vel
2.9-3.9 mmoVl 52% 20% 10% 18% -18.3 <.0oo1> 1.9-2.8 mmol/1 55% 20% 7% 18% -14.7 SH 64% 11% 6% 19%, -11.1 <.0001> We conclude that more than half of all hypoglycemic SMBG readings and approximately 2/3rds of all SH episodes, are preceded by at least one hypoglycemic reading within the pre^^ous 24 hours. In addition, hypoglycemic events tend to appear in clusters. Thus, an imtial hypoglycemic episode may be a warning sign for upcoming recurrent hypoglycemia.
n. EXAMPLE NO. 2
This method uses routine self-monitoring blood glucose (SMBG) data and pertains directly to enhancement of home SMBG devices by introducing intelligent data interpretation logic, capable of predicting both HbAu and periods of increased risk for significant hypoglycemia. The method has two components; (1) Algorithm 1 estimating HbAic, and (2) Algorithms 2 & 3 js^dicting long-terra and short-term (within 24 hours) significant hypoglycemia, respectively. In this report we describe the steps of development, optimization and validation of the HbAio estimation Algorithm 1, as well as its accuracy in estimating laboratory acquired HbAic.
Objective:
The primary goal was to reach an accuracy of 95% of measurements within +1 HbAio unit of a laboratory reference, which is the National Glycohemoglobm Standardization Program (NGSP) Criterion for accuracy for HbAu assays.

Methods:
Subjects; SMBG data was captured for 100 subjects with Type 1 and 100 subjects with Type 2 diabetes mellitns (TIDM, T2DM) for 6 months and 4 months respectively, with HbAic tests taken at months 0, 3 and 6 in TIDM and months 0, 2 and 4 in T2DM,
Development and Optimization of Algorithm 1: The Training Data Set consisted of SMBG and HbAu data collected up to monlb 3 for TIDM and up to month 2 for T2DM. These Training Data were used for optimization of Algorithm 1 and for evaluation of a number of sample selection criteria that would ensure better accuracy. The sample selection criteria are requirements for any SMBG sample collected by the meter, which, if met, ensure accurate estimation of HbAic from that sample. Consequently, the meter will scan every SMBG sample and if the sample selection criteria are met, will compute and display HbAic estimate. After analyzing various cut points the following criteria were selected:
1. Test Frequency: In order to generate an estimate of HbAic, the meter will require an average of 2.5 tests or more per day over the last 60 days, e.g. a total of 150 SMBG readings over the past two months. It is important to note that this is an average per day, testing every day is not required.
2. Randomness of data: Certain 60-day samples with only post-prandial testing, or ins^ifficient nighttime tests ( Results; Prospective Validation and Accuracy of Algorithm 1:
The algorithm, including the sample selection criteria, was then applied to Test Data Set I, which included SMBG and HbAie data for two months prior to TIDM and T2DM subjects' last HbAio. and to an mdependent Test Data Set 2 consisting of 60 TIDM subjects who participated in a previous NIH study. The estimates obtained by Algorithm 1 were compared to reference HbAic levels for validation purposes. In Test Data Set I the algorithm reached the NGSP criteria with an accuracy of 95.1% within +1 Hbaio iirat of the lab reference. In Teat Data Set 1 the algorithm reached the NGSP

criteria as well wifli an accuracy of 95.5% within +1 Hba:c unit of the lab reference. Investigation of the sample selection criteria showed that 72.5% of all subjects would generate such an accurate estimate every day, and 94% of all subjects would generate such an accurate an estimate about once every 5 days.
Conclusion: Routine SMBG data allow for accurate estimate of HbA|o that meets the NGSP criterion for accuracy of direct HbAic assays.
STIBJECTS & INCLUSION CRITERION
We have consented 100 subjects with Type 1 Diabetes (TIDM) and 100 subjects with Type 2 Diabetes (T2DM). One hundred seventy-nine subjects, 90 with TIDM and 89 with T2DM, completed significant portions of the SMBG data collection. The data of these 179 subjects were used for testing Algorithms 2 and 3. However, the testing of Algorithm 1 required that the subjects had not only SMBG data, but HbAic data and SMBG records taken in the 60 days prior to SMBG. At month 3 of this study (month 2 for T2DM), 153 subjects (78 with TIDM) had completed HbA,c data and SMBG data meeting the above criterion, In adchtion, we used for testing of Algorithm 1 data for N=60 subjects with TIDM who participated in our previous NIH study (NIH). The demographic characteristics of all subjects are presented in Table 10.
Table 10: Demoeraphic characteristics of the subjects.

Variable TIDM T2DM NIH
Age (years) 41.5(11.6) 50.9 (8.1) 44.3 (10.0)
Gender: % Male 41% 43% 46%
Duration of diabetes (years) 20.1 (10.1) 11.7(8.2) 26.4 (10.7)
Body mass index 25.4(4.7) 34.2 (8.1) 24.3 (3.4)
Baseline HbAjo 7.5(1.1) 8.5(2.1) 7.6(1.0)
Second HbAic 7.3 (1.2) 7.9 (1.6) 7.4 (0.8)
Thhxl HbAic 7.0 (0.9) 7,5(1.1) -
# SMBG readings / subject / day 5.4 (2.3) 3.5 (0.8) 4.1 (1.9)
# Days with SMBG readings in the 2 montiis preceding second HbAio , 56.9(5.4) 57.3 (4.3) 37.5 (14.3)

Our investigation shewed that the major reason for incomplete data within 60 days prior to HbAic assays, or elsewhere, was not subject noncompliance, but meter failure. The time and date of the ONE TOUCH ULTRA meter could "jump" to a random date/time (e.g. Novenlber 2017), apparently if the patient depressed the "M" button for too long. We were checking the date/time of each meter upon return and we found that such event occurred in 60 meters throughout the course of the study. The time/date sloift affected 15,280 readings, or approximately 10% of all readings. We stoied these reiuiings separately and had a student review them. In many, but not in all cases he was able to restore Gie date/time sequence of the readings. This error, together with a few meters lost in the mail, reduced the number of subjects with good data for Algorithm 1 analyses from 179 to 141. The data of 12 subjects were restored, which brought the final count to 153 subjects, 78 with TIDM and 75 with T2DM, who had uninterrupted time sequence of data prior to HbAic, suitable for testing of Algorithm 1.
PROCEDURE
All subjects signed IRB-approved consent forms and attended orientation meetings where they were introduced to the ONE TOUCH ULTRA meter and completed sra^ening questionnaires. Inunediately after the introductory meeting all subjects visited a UVA laboratory and had blood drawn for baseline HbAic- TIDM subjects were followed for 6 months with laboratory HbAu assays at months 3 and 6; T2DM subjects were followed for 4 months with laboratory HbAic assays at months 2 and 4. Self-nonitoring (SMBG) data were regularly downloaded from the meters and stored in iatabases. Parallel recording of significant hypoglycemic and hyperglycemic episodes vas done by an automated e-mail/telephone tracking system every two weeks.
MTA STORAGE AND CLEANING
The raw data fi'om ONE TOUCH ULTRA were stored in InTouch databases separately for TIDM and T2DM subjects. These raw data were cleaned for subject and

WE CLAIM:
1. A method for evaluating the HbA of a patient based on BG data collected over
a first predetermined duration, said method comprising:
preparing the data for estimating HbAic using a predetermined sequence of mathematical formulas defamed as: pre-processing of the data;
validation of a sample of the BG data via sample selection criteria; and estimating HbAi’ if the sample is valid.
2. The method as claimed in claim 1, wherein said first predetermined duration is about 60 days.
3. The method as claimed in claim 1, wherein said first predetermined duration ranges from about 45 days to about 75 days,
4. The method as claimed in claim 1, wherein said first predetermined duration ranges from about 45 days to about 90 days.
5. The method as claimed in claim 1, wherein the preprocessing of the data for each patient comprise:
conversion of plasma to whole blood BG mg/dl; conversion of BG measured in mg/dl to units of mmol/I; and computing Low Blood Glucose Index(RLOl) and High Blood Glucose Index{RHll).
6. The method as claimed in claim 1, wherein the preprocessing of the data for
each patient using predetermined mathematical formulas defined as:
conversion of plasma to whole blood BG mg/dl via BG’PLASBG (mg/dl) /1.12; conversion of BG measured in mg/dl to units of mmol/1) viaBGMM=BG/l 8 ; and

computing Low Blood Glucose Index (RLOl) and High Blood Glucose Index(RHIl) using a predetermined mathematical formula defined as:
Scale= [In (BG) j'"‘'‘ - 5.381, wherein BG is measured in units of mg/dl, Riskl= 22.765(Scale)’ wherein
Ri3kL0= Riskl if (BG is less than about 112.5) and therefore risk of LBGI exists, otherwise RiskLO=0, and
RiskHI=Riskl if (BG is greater than about 112.5) and therefore risk of HBGI exists, otherwise RiskHI=0, BGMMl = average of BGMM per patient, RLOl= average of RiskLO per patient, RHI1= average of RiskHI per patient,
L06 = average of RiskLO computed only for readings during the night, otherwise missing if there are no readings at night,
N06, N12, N24 are percentage of SMBG readings in fame intervals, NCI = total number of SMBG readings in the first predetermined duration; and NDAYS = number of days with SMBG readings in the first predetermined duration.
7. The method as claimed in claim 6, wherein the N06, N12, N24 are percentage of SMBG readings in time intervals of about 0-6:59 hour time period ; about 7-12:59 hour time period, and about 18-23:59 hour time period, respectively.
8. The method as claimed in claim 6, comprising assigning a group depending on the patient's computed High BG Index using a predetermined mathematical formula defined as:
if(RHIl is about 5.25 or if RHIl is > about 16) then the assigned group= 0, if(RHIl is > about5.25 and if RHIl is about 7.0 and if RHIl is
9. The method as claimed in claim 8, comprising providing estimates using a
predetermined mathemadcal formula defined as:
EO = 0.55555*BGMM!+2.95, El = 0.50567*BGMMl+0.074*L06+2.69, E2 = 0.55555*BGMMl-0.074*L06+2.96, E3 = 0. 44000*BGMMl+0.035*L06+3. 65; and
if (Groups 1) then EST2=E1, or if (Group = 2) then EST2=E2, or if (Group= 3) then EST2=E3, otherwiseEST2=E0.
10. The method as claimed in claim 9, comprising providing further correcdon of
the estimates using a predetermined mathematical formula defined as:
if (missing(L06)) EST2=E0,
if (RLOl is about 0.5 and RHIl is le about 2.0) thenEST2=EO-0.25, if{RL01 is about 2.5 andRHIl is > about 26) thenEST2=EO-l. 5*RL01,and if( (RLOl/RHIl) is about 1.3) then EST2=EST2-0.08.
11. The method as claimed in claim 10 for esdmating the HbA ic of a padent based on BG data collected over the first predetermined duration, said method comprising:
said estimating HbAjc using at least one of four predetermined mathemadcal formulas defined as:
a) HbAic = the EST2 defined by claim 8 or as corrected by claim 10 or
b) HbA,, = 0.809098*BGMM1 + 0.064540*RLOl-0.151673*RHll + 1.873325, wherein.
BGMMl is the average BG (mmol/1) as claimed in claim 6. RLOl is the Low BG Index as claimed in claim 6. RHIl is the High BG Index as claimed in claim 6; or
c) HbA,, = 0.682742*HBAO + 0.054377*RH1! + 1.553277, wherein
HBAO is a previous reference HbAlc reading taken about a second
predetermined period prior to the estimate, wherein
RHIl = is the High BG Index as claimed in claim 6; or

d) HbAlc= 0, 41046*BGMM + 4. 0775
wherein BGMMl is the average BG (mmol/1) as claimed in claim 6.
12. The method as claimed in claim 11, wherein said second predetermined
duration is about three months.
13. The method as claimed in claim 11, wherein said second predetermined duration ranges from about 2.5 months to about 3.5 months.
14. The method as claimed in claim 11, wherein said second predetermined duration ranges from about 2.5 months to six months.
15. The method as claimed in claim 11, wherein the validation of the sample using sample selection criteria of HbAie estimate only if the first predetermined duration sample meets at least one of the following four criteria:

a) a test frequency criterion wherein if the first predetermined duration sample contains an average of at least about 1.5 to about 2.5 tests per day;
b) an alternative test frequency criterion only if the predetermined duration sample contains at least a third predetermined sample period with readings with an average frequency of about 1.8 readings/day;
c) a randomness of data criterion-1 wherein the HbAlc estimate is validated or
displayed only if the ratio(RL01/RHIl > = about 0.005),
wherein
RLOl is the Low BG Index as claimed in claim 6 RHIl is the High BG Index as claimed in claim 6; or
d) a randomness of data criterion-2 wherein HbAic estimate is validated or
displayed only if the ratio (N06 > = about 3%). ': Wherein
N06 is the percentage of readings during the night as claimed in claim 6.

16. The method as claimed in claim 15, wherein said third predetermined duration
is at least 35 days.
17. The method as claimed in claim 15, wherein said third predetermined duration ranges from about 35 days to about 40 days.
18. The method as claimed in claim 15, wherein said third predetermined duration ranges from about 35 days to about as long as the first predetermined duration.
19. A system for evaluating the HbAj’ of a patient based on BG data collected over a first predetermined duration, said system comprising:
a database component operative to maintain a database identifying said BG data; and
a processor programmed to:
prepare the data for estimating HbAji; using a predetermined sequence of mathematical formulas defined as:
pre-process the data.
Validate the estimate via sample selection criteria, and
estimate HbAic if the sample is valid.
20. The system as claimed in claim 19 or 37, wherein said first predetermined
duration is about 60 days.
21. The system as claimed in claim 19, wherein said first predetermined duration ranges from about 45 days to about 75 days.
22. The system as claimed in claim 19, wherein said first predetermined duration ranges from about 45 days to about 90 days.

23. The system as claimed in claim 19. wherein the preprocessing of the data for each
patient comprise:
conversion of plasma to whole blood BG mg/dl; conversion of BG measured, in mg/dl to units of mmol/1 ; and computing Low Blood Glucose Index(RLOI) and High Blood Glucose Index(RHIl).
24. The system as claimed in claim 19, wherein the preprocessing of the data for each
patient using predetermined mathematical formulas defined as:
conversion of plasma to whole blood BG mg/dl via BG=PLASBG (mg/dl) /I. 12; conversion of BG measured in mg/dl to units of mmol/l) via BGMM=BG/18 ; and computing Low Blood Glucose Index (RLOl) and High Blood Glucose Index(RHn) using a predetermined mathematical formula defined as:
Scale=[ln(BG)]' "‘'‘ -5. 381. wherein BG is measured in units of mg/dl,
Riskl = 22.765 (Scale)’ wherein
RiskLO=Riskl if (BG is less than about 112.5) and therefore risk of LBGI exists, otherwise RiskLO=0, and
RiskHl=Riskl if (BG is greater than about 112.5) and therefore risk of HBGI exists, otherwise RiskHI=0,
BGMMl = average of BGMM per pafient,RL01 = average of RiskLO per patient,RHIl= averageof RiskHI per patient,
L06 = averageof RiskLO computed only for readings during the night, otherwise missing if there are no readings at night,
N06, N12, N24 are percentage of SMBG readings in time intervals, NCI = total number of SMBG readings in the first predetermined duration; and NDAYS = number of days with SMBG readings in the first predetermined duration.
25. The system as claimed in claim 24, wherein the N06, N12, N24 are percentage of
SMBG readings in time intervals of about 0-6:59 hour time period; about 7-12:59
hour time period, and about 18-23:59 hour time period, respectively.

26. The system as claimed in claim 24, comprising assigning a group depending on
the patient's computed High BG Index using a predetermined mathematical formula
defined as:
if (RHIl is about 16) then the assigned grQup= 0, if(RHIl is > about 5.25 and if RHIl is about 7.0 and if RHIl is about 8,5 and if RHIl is 27. The system as claimed in claim 26, comprising providing estimates using a
predetermined mathematical formula defined as:
EO =0. 55555*BGMMl+2.95,
El = 0.50567*BGMMl+0.074*L06+2.69,
E2 = 0.55555*BGMMl-0.074*L06+2.96,
E3 = 0.44000*BGMMl+0.035*L06+3.65; and
if (Group = 1) then EST2=E1, or if (Group = 2) then EST2=E2, or if (Group= 3) then
EST2=E3, otherwiseEST2=E0.
28. The system as claimed in claim 27, comprising providing further correction of the
estimates using a predetermined mathematical formula defined as:
if (missing (L06))EST2=E0,
if (RLOl is about 0.5 and RHIl is le about 2.0) thenEST2=EO-0. 25,
if (RLOl is about 2.5 and RHIl is > about 26) thenEST2=EO-1.5*RL01, and
if((RL01/RHIl) is about 1.3) then EST2=EST2-0.08.
29. The system as claimed in claim 28 for estimating the HbAij; of a patient based on
BG data collected over the first predetermined duration, said system comprising:
said estimating HbAi’ using at least one of four predetermined mathematical formulas defined as:
a) HbAje = the EST2 defined by claim 8 or as corrected by claim 10 or

b) HbAi,= 0.809098*BGMMl + 0.064540*RL01-0. 151673*RHI1 + 1.873325,
wherein
BGMMl is the average BG(mmol/l) as claimed in claim 24. RLOl is the Low BG Index as claimed in claim 24. RHIl is the High BG Index as claimed in claim 24 ; or c)HbA,i; = 0.682742*HBA0 + O.054377*RHll + 1.553277, wherein
HBAO is a previous reference HbAi’ reading taken about a second predetermined period prior to the estimate, wherein
RHIl = is the High BG Index as claimed in claim 6; or
c) HbA,c = 0. 41046*BGMM + 4. 0775
wherein BGMMl is the average BG (mmol/1) as claimed in claim 24.
30. The system as claimed in claim 29, wherein said second predetermined duration is about three months.
31. The system as claimed in claim 29, wherein said second predetermined duration ranges from about 2.5 months to about 3.5 months.
32. The system as claimed in claim 29, wherein said second predetermined duration ranges from about 2.5 months to six months.
33. The system as claimed in claim 29, wherein the validation of the HbAjc estimate using sample selection criteria of HbAic estimate only if the first predetermined duration sample meets at least one of the following four criteria:

a) a test frequency criterion wherein if the first predetermined duration sample contains an average of at least about 1.5 to about 2.5 tests per day ;
b) an alternative test frequency criterion only if the predetermined duration sample contains at least a third predetermined sample period with readings with an average frequency of about 1.8 readings/day;
c) a randomness of data criterion-1 wherein the HbAjc estimate is validated or displayed only if the ratio (RLOI/RHIl > = about 0.005),

wherein
RLOl is the Low BG Index as claimed in claim 24
RHIl is the High BG Index as claimed in claim 24; or d) a randomness of data criterion-2 wherein HbAi’ estimate is validated or displayed only if the ratio (N06 > = about 3%) wherein
N06 is the percentage of readings during the night as claimed in claim 24.
34. The system as claimed in claim 33, wherein said third predetermined duration is at least 35 days.
35. The system as claimed in claim 33, wherein said third predetermined duration ranges from about 35 days to about 40 days.
36. The system as claimed in claim 33, wherein said third predetermined duration ranges from about 35 days to about as long as the first predetermined duration,
37. A system for evaluating the HbAjc of a patient based on BG data collected over a first predetermined duration, said system comprising:
a BG acquisition mechanism, said acquisition mechanism configured to acquire BG data from the patient;
a database component operative to maintain a database identifying said BG data; and a processor programmed to:
prepare the data for estimating HbAic using a predetermined sequence of mathematical formulas defined as:
pre-process the data;
validate a sample of the BG data via sample selection criteria; and
estimate HbAic if the sample is valid.
38. A method for evaluating the long term probability for severe hypoglycemia (SH)

and/or moderate hypoglycemia (MH) of a patient based on BG data collected over a predetermined duration, said method comprising:
computing LBGI based on said collected BG data; and
esdmating the number of future SH episodes using a predetermined mathematical formula based on said computed LBGI.
39. The method as claimed in claim 38, wherein:
said computed LBGI is mathematically defined from a series of BG readings Xi,X2,..-x„
taken at time points ti, t2,....tn as:
where:
Ibgi (BG;a)= lO.ffBGf iff(BG) >OmdO otherwise, a = about 2, representing a weighting parameter.
40. The method as claimed in claim 38, further comprising:
defining predetermined risk categories (RCAT), each of said risk categories (RCAT)
representing a range of values for LBGI; and
assigning said LBGI to at least one of said risk categories(RCAT).
41. The method as claimed in claim 40, wherein said risk categories (RCAT) are
defined as follows:
category 1, wherein said LBGI is less than about 0.25; category 2, wherein said LBGI is between 0.25 and 0.50; category 3, wherein said LBGI is between 0.50 and 0.75; category 4, wherein said LBGI is between 0.75 and 1.0; category 5, wherein said LBGI is between 1.0 and 1.25; category 6, wherein said LBGI is between 1.25 and 1.50; category 7, wherein said LBGI is between 1.5 and 1.75; category 8, wherein said LBGI is between 1.75 and 2.0; category 9, wherein said LBGI is between 2.0 and 2.5;

category 10, wherein said LBGI is between 2, 5 and 3.0 category II, wherein said LBGI is between3.0 and 3.5; category 12, wherein said LBGI is between 3.5 and 4.25; category 13, wherein said LBGI is between 4.25 and 5.0; category 14, wherein said LBGI is between 5.0 and 6.5; and category 15, wherein said LBGI is above about 6.5.
42. The method as claimed in claim 40, further comprising:
defining a probability of incurring a select number of SH episodes respectively for each of said assigned risk categories (RCAT).
43. The method as claimed in claim 40, further comprising: defining a probability of
incurring a select number of SH episodes within a next first predetermined duration
respectively for each of said assigned risk categories (RCAT), using the formula:
F(x)= 1 -exp(-a.x') for any x > 0 and 0 otherwise, wherein: a = abDut-4.I9 b= about 1. 75
44. The method as claimed in claim 43, wherein said first predetermined duration is about one month.
45. The method as claimed in claim 43, wherein said first predetermined duradon ranges from about 0.5 months to about 1.5 months.
46. The method as claimed in claim 45, wherein said first predetermined duradon ranges from about 0.5 months to about 3 months.
47. The method as claimed in claim 42, further comprising:
defining a probability of incurring a select number of SH episodes within a next second predetermined duration respectively for each of said assigned risk categories (RCAT), using the formula:

F{x)=l-exp (-a.x ) for any x > 0 and 0 otherwise, wherein: a = about-3.28, b = about 1.50.
48. The method as claimed in claim 47, wherein said second predetermined duration is about three months.
49. The method as claimed in claim 47, wherein said second predetermined duration ranges from about 2 months to about 4 months.
50. The method as claimed in claim 47, wherein said second predetermined duration ranges from about 3 months to about 6 months.
51. The method as claimed in claim 40, further comprising:
defining a probability of incurring a select number of SH episodes within the next third predetermined duration respectively for each of said assigned risk categories (RCAT), using the formula:
F(x)= 1 -exp (-a.x ) for any x > 0 and 0 otherwise, wherein:
a = about-3.06
b = about 1.45.
52. The method as claimed in claim 51, wherein said third predetermined duration is about 6 months.
53. The method as claimed in claim 51, wherein said third predetermined duration ranges from about 5 months to about 7 months.
54. The method as claimed in claim 51, wherein said third predetermined duration ranges from about 3 months to about 9 months.

55. The method as claimed in claim 40, further comprising: defining a probability of
incurring a select number of MH episodes within the next month respectively for each
of said assigned risk categories (RCAT), using the formula:
F(x)= 1 -exp (-a.x ) for any x > 0 and 0 otherwise, wherein: a= about-1.58 b = about 1.05.
56. The method as claimed in claim 40, further comprising: defining a probability of
incurring a select number of MH episodes within the next 3 months respectively for
each of said assigned risk categories (RCAT), using the formula:
F{x)= 1-exp (-a.x') for any x > 0 and 0 otherwise, wherein: a= about-1,37 b = about 1.14.
57. The method as claimed in claim 40, fiirther comprising: defining a probability of
incurring a select number of MH episodes within the next 6 months respectively for
each of said assigned risk categories (RCAT), using the formula:
F(x)= 1 -exp (-a.x') for any x > 0 and 0 otherwise, wherein: a = about-1.37 b = about 1.35.
58. The method as claimed in claim 38, further comprising: assigning classifications of risk for future significant hypoglycemia of the patient.
59. The method as claimed in claim 58, wherein said classifications are defined as follows:
minimal risk, wherein said LBGI is less than about 1.25; low risk, wherein said LBGI is between 1.25 and 2.50 ; moderate risk, wherein said LBGI is between 2.5 and 5; and high risk, wherein said LBGI is above about 5.0.

60. A system for evaluating the long term probability for severe hypoglycemia
(SH)and/or moderate hypoglycemia (MH) of a patient based on BG data collected
over a predetermined duration, said system comprising:
a database component operative to maintain a database identifying said BG data; and
a processor programmed to;
compute LBGI bs’sed on said collected BG data, and
estimate the number of future SH episodes using a predetermined
mathematical formula b&sed on said computed LBGI.
61. The method as claimed in claim 60, wherein:
said computed LBGI is mathematically defined from a series of BG readings Jt/, X2,... x„ taken at time points ti, t2,..., d, as:
LBGI =-flbgi{x.\2)
where:
Ihgi (BG;a) - lO.ffBG)" iif(BG) > 0 and 0 otherwise, a = about 2, representing a weighting parameter.
62. The system as claimed in claim 60, further comprising:
defining predetermined risk categories (RCAT), each of said risk categories (RCAT) representing a range of values for LBGI; and assigning said LBGI to at least one of said risk categories (RCAT).
63. The system as claimed in claim 62, wherein said risk categories (RCAT) are
defined as follows: follows:
category 1, wherein said LBGI is less than about 0.25 ; category 2, wherein said LBGI is between 0.25 and 0.50 ; category 3, wherein said LBGI is between 0.50 and 0.75 ; category 4, wherein said LBGI is between 0.75 and 1.0 ;

category 5, wherein said LBGI is between 1.0 and 1.25 category 6, wherein said LBGI is between. 25 and 1.50 category 7, wherein said LBGI is between 1.5 and 1.75 category 8, wherein said LBGI is between 1.75 and 2.0 category 9, wherein said LBGI is between 2.0 and 2.5 ; category 10, wherein said LBGI is between 2.5 and 3.0 category 11, wherein said LBGI is between 3.0 and 3.5 ; category' 12, wherein said LBGI is between 3.5 and 4.25 ; category' 13, wherein said LBGI is between 4.25 and 5.0 ; category 14, wherein said LBGI is between 5.0 and 6.5 ; and category 15, wherein said LBGI is above about 6.5.
64. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of SH episodes respectively for each of said assigned risk categories (RCAT).
65. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of SH episodes within a next first predetermined durafion respecdvely for each of said assigned risk categories (RCAT), using the formula:
F(x)= I-exp (-a.x') for any x > 0 and 0 otherwise, wherein: a = about-4.19 b = about 1.75.
66. The system as claimed in claim 65, wherein said first predetermined duration is about one month.
67. The system as claimed in claim 65, wherein said first predetermined duration ranges from about 0.5 months to about 1.5 months.
68. The system as claimed in claim 65, wherein said first predetermined durafion ranges from about 0.5 months to about 3 months.

69. The system as claimed in claim 62, further comprising: defining a probability of
incurring a select number of SH episodes within a next second predetermined duration
respectively for each of said assigned risk categories (RCAT), using the formula:
F(x)= ! -exp (-a.x') for any x > 0 and 0 otherwise, wherein: a = about-3.28 b = about 1.50.
70. The system as claimed in claim 69, wherein said second predetermined duration is about three months.
71. The system as claimed in claim 69, wherein said second predetermined duration ranges from about 2 months to about 4 months.
72. The system as claimed in claim 69, wherein said second predetermined duration ranges from about 3 months to about 6 months.
73. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of SH episodes within the next third predetermined duration respectively for each of said assigned risk categories (RCAT). using the formula :
F(x)’ 1-exp (-a.x') for any x > 0 and 0 otherwise, wherein: a = about-3.06 b = about 1.45.
74. The system as claimed in claim 73, wherein said third predetermined duration is about 6 months.
75. The system as claimed in claim 73, wherein said third predetermined duration ranges from about 5 months to about 7 months.

76. The system as claimed in claim 73, wherein said third predetermined duration ranges from about 3 months to about 9 months.
77. The system as claimed in claim 62, further comprising: defining a probability of incurring a select number of MH episodes within the next month respectively for each of said assigned risk categories (RCAT), using the formula:
F(x)= 1-exp (-a.x’) for any x > 0 and 0 otherwise, wherein: a = about-1.58 b = about 1.05.
78. The system as claimed in claim 62, further comprising: defining a probability of
incurring a select number of MH episodes within the next 3 months respectively for
each of said assigned risk categories (RCAT), using the formula:
F{x)= 1 -exp (-a.x') for any x > 0 and 0 otherwise, wherein: a =about-1.37 b = about 1.14.
79. The system as claimed in claim 62, further comprising: defining a probability of
incurring a select number of MH episodes within the next 6 months respectively for
each of said assigned risk categories (RCAT), using the formula:
F(x)= 1-exp (-a.x ) for any x > 0 and 0 otherwise, wherein: a = abQut-1.37 b = about 1.35.
80. The system as claimed in claim 62, flirther comprising: assigning classifications of risk for future significant hypoglycemia of the patient.
81. The system as claimed in claim 80, wherein said classifications are defined as follows:
minimal risk, wherein said LBGI is less than about 1.25 ;

low risk, wherein said LBGI is between 1.25 and 2.50 ; moderate risk, wherein said LBGI is between 2.5 and 5 ; and high risk, wherein said LBGI is above about 5.0.
82. A system for evaluating the long term probability for severe hypoglycemia (SH)
and/or moderate hypoglycemia (MH) of a patient based on BG data collected over a
predetermined duration, said system comprising:
a BG acquisition mechanism, said acquisition mechanism configured to acquire BG data from the patient;
a database component operative to maintain a database identifying said BGdata ; and
a processor programmed to: compute LBGI based on said collected BG data, and
estimate the number of future SH episodes using a predetermined mathematical formula based on said computed LBGI.
83. A method for evaluating the short term probability for severe hypoglycemia (SH)
ofa patient based on BG data collected over a predetermined duration, said method
comprising:
computing scale values based on said collected BG data ; and computing the low BG risk value (RLO) for each BG data.
84. The method as claimed in claim 83, wherein:
said computed RLO (BG) is mathematically defined as:
Scale = [In (BG)] 1.0845 -5.381, wherein BG is measured in units of mg/dl
Risk = 22. 765(Scale)’I
f (BG is less than about 112.5) then:
RLO (BG) = Risk, otherwise
RLO (BG) = 0.
85. The method as claimed in claim 83, wherein:
said computed RLO (BG) is mathematically defined as:

Scale= [In (BG) ] -1. 861, wherein BG is measured in units of mmol/1 Risk = 32.184 (Scale) 2 if (BG is about 112.5) then:
RLO (BG) = Risk, otherwise
RLO (BG) = 0.
86. The method as claimed in claim 83, wherein:
computing LBGI based on said collected BG data, said computed LBGI is mathematically defined from a series of BG readingsxl,A:2,... x„ taken at time points ti, t2,.--, t„ as:
LBGI =-Y,lbgi{x-,2)
where:
Ibgi (BG;a) = RLO (BG).
87. The method as claimed in claim 83, wherein: computing provisional LBGI based
on said collected BG data, said computed provisional LBGI is mathematically defined
from mathematically defined as:
LBGI(l)=RLO(xl)
RL02(1) = 0
LBGI (j) =(a-iyj) * LBGIG-l) + (1/j) * RLO(xo)
RL02a)=(a-l)/j)*RL02a-l) +(l/j)* (RLO (x))-LBGI 0))'-
88. The method as claimed in claim 87, wherein: computing SBGI, said computed
SBGI is mathematically defined as:
SBGI(n)= yj{RL02{n)).
89. The method as claimed in claim 88, comprising qualifying or providing a warning
of upcoming short term SH wherein if:
(LBGI(150) > 2. 5 and LBGI (50) > (1. 5*LBGI(150) and SBGI(50) > SBGI (150)) then said issue of warning is qualified or provided, or

RLO > (LBGI(150)+1.5*SBGI (150)) then said issue of warning is qualified or provided ; otherwise:
a warning is not necessarily qualified or provided.
90. The method as claimed in claim 88, comprising qualifying or providing a warning
of upcoming short term SH wherein if:
(LBGI(n) > a and SBGI(n) ge (P) then said issue of warning is qualified or provided, and/or
(RLO (n) (LBGI (n) +y * SBGI (n))) then said issue of warning is qualified or provided ; otherwise:
a warning is not necessarily qualified or provided, wherein a, P, and y are threshold parameters.
91. The method as claimed in claim 90, wherein said threshold parameters a, p and y are defined as a = about 5, p = about 7.5, y = about 1.5.
92. The method as claimed in claim 90, wherein said threshold parameters a, P and y are defined as any combination in a, b, and/or c, or as any intermediate combination of values of said parameters between the values of said parameters in a, b, and/or c below:

a) a - 6. 4, p = 8. 2, y = 1.5, a = 5. 0, P = 7. 5, y = 1.3 ;
b) a = 6.0, p - 7.5, y = 1.5,a = 4.9, p = 7.0, y = 1.2 ; and/or
c) a = 5, 5, p = 7.5, y = 1.5, a=4.8, p = 7.0, y = 1.2.
93. The method as claimed in claim 90, wherein said threshold parameters a, p and y
are defined as any combination in a, b, and/or c, or as any intermediate combination of
values of said parameters between the values of said parameters in a, b, and/or c
below:
a), a about 6.4, P about 8. 2, y about 1.5, a about 5.0, p about 7.5, y about 1.3 ;

b). a about 6.0, p about 7.5, y aboutl.5, a about 4.9, P about 7.0, y about 1.2 ; and/or
c). a about 5. 5, p about 7.5, y about 1.5, a about 48, p about 7.0, y about 1.2.
94. A system for evaluating the short term probability for severe hypoglycemia(SH) of
a patient based on BG data collected over a predetermined duration, said system
comprising:
a database component operative to maintain a database identifying said BG
data; and a processor programmed to:
compute scale values based on said collected BG data; and compute the low BG risk value (RLO) for each BG data.
95. The system as claimed in claim 94, wherein: said computed RLO (BG) is
mathematically defined as:
Scale = [In (BG)]' °’’’-5.381, wherein BG is measured in units of mg/dl
Risk = 22. 765 (Scale)’
if (BG is less than about 112.5) then:
RLO (BG) = Risk, otherwise
RLO (BG) = 0.
96. The system as claimed in claim 94, wherein: said computed RLO (BG) is
mathematically defined as:
Scale=[ln (BG) ]"‘‘‘-1.861, wherein BG is measured in unitsof mmol/1
Risk = 32. 184(Scale)’
if (BGis RLO (BG) = Risk, otherwise
RLO (BG) = 0.
97. The system as claimed in claim 94, wherein:

computing LBGI based on said collected BG data, said computed LBGI is mathematically defined from a series of BG readings Xi, x’,... x„ taken at time points tit2,..., ?„as :
LBGI = -YlbgKx-,l)
where:
Ibgi (BG;a) = RLO (BG).
98. The system as claimed in claim 94, wherein:
computing provisional LBGI based on said collected BG data, said computed provisional LBGI is mathematically defined from mathematically defined as: LBGI(l)=RLO(x,) RL02(1)=0
LBGIG)=(a-l)/j) * LBGIO-l) + (1/j) * RLO (x,) RLO2(j)=0-l)/J)*RLO2a-l) +(l/j) * (RLO (x’LBGIO))'.
99. The system as claimed in claim 98, wherein: computing SBGI, said computed
SBGI is mathematically defined as:
SBGI(n)= yj{RL02{n)).
100. The system as claimed in claim 99, comprising qualifying or providing a warning
of upcoming short term SH wherein if:
(LBGI (150) 2. 5 and LBGI (50) (1.5*LBGI (150) and SBGI (5) SBGI (150)) then said issue of warning is qualified or provided, or RLO (LBGI (150) +1.5*SBGI (150)) then said issue of warning is qualified or provided;
otherwise:
a warning is not necessarily qualified or provided.
101. The system as claimed in claim 99, comprising qualifying or providing a warning
of upcoming short term SH wherein if:

(LBGI (n) : a and SBGI(n) ge(P)) then said issue of warning is qualified or provided, and/or
(RLO (n) (LBGI (n) + y * SBGI (n))) then said issue of warning is qualified or provided;
otherwise:
a warning is not necessarily qualified or provided, wherein a, P and y are threshold parameters.
102. The system as claimed in claim 101, wherein said threshold parameters a, p and y are defined as a = about 5, P = about 7.5, y = about 1.5.
103. The system as claimed in claim 101, wherein said threshold parameters a, p and y are defined as any combination in a, b, and/or c, or as any intermediate combination of values of said parameters between the values of said parameters in a, b, and/or c below:
a), a = 6.4, p = 8. 2, y = 1.5, a = 5.0, p = 7.5, y = 1.3 ;
b). a = 6.0, p - 7.5, y =1.5, a = 4.9, p =t 7.0, y = 1.2 ; and/or
c). a - 5. 5, p =7.5, y = 1.5, a = 48, p = 7.0, y = 1.2.
104. The system as claimed in claim 101, wherein said threshold parameters a, p and y
are defined as any combination in a, b, and/or c, or as any intermediate combination of
values of said parameters between the values of said parameters in a, b, and/or c
below:
a), a about 6.4, p about 8. 2, y about 1.5, a about 5.0, p about 7.5, y about 1.3 ; b). a about 6.0, p about 7.5, y aboutl.5, a about 4.9, P about 7.0, y about 1.2 ; and/or
c). a about 5. 5, P about 7.5, y about 1.5, a about 48, p about 7.0, y about 1.2.
105. A system for evaluating the short term probability for severe hypoglycemia (SH)
of a patient based on BG data collected over a predetermined duration, said system
comprising:

a BG acquisition mechanism, said acquisition mechanism configured to acquire BG data from the patient;
a database component operative to maintain a database identifying said BG data;, and a processor programmed to:
compute scale values based on said collected BG data; and
compute the low BG risk value (RLO) for each BG data.
106. The system as claimed in claim 11, wherein the validation of sample estimate
using sample selection criteria of HbA(c estimate only if the first predetermined
duration sample meets at least one of the following four criteria:
a) a test frequency criterion wherein if the first predetermined duration sample contains an average of at least about 1.5; and
b) a randomness of data criterion-1 wherein the HbAic estimate is validated or displayed only if the ratio (RL01/RHI1>= about 0.005), wherein
RLOl is the Low BG Index as claimed in claim 6 RHIl is the High BG Index as claimed in claim 6; or
c) a randomness of data criterion-2 wherein HbAic estimate is validated
or displayed only if the ratio (N06 > = about 3%),
wherein
N06 is the percentage of readings during the night as claimed in claim 6.
107. The method as claimed in claim 106, wherein said third predetermined duration
is at least about 35 days.
108. The system as claimed in claim 29, wherein the validation of sample estimate using selection criteria of HbAic estimate only if the first predetermined duration sample meets at least one of the following criteria:
a) a test frequency criterion wherein if the first predetermined duration sample contains an average of at least about 1.5; and

b) a randomness of data criterion-1 wherein the HbAjc estimate is validated or
displayed only if the ratio (RL01/RHIl>=about 0.005),
wherein
RLOl is the Low BG index as claimed in claim 24 RHIl is the High BG index as claimed in claim 24; or
c) a randomness of data criterion-2 wherein HbAjc estimate is validated or
displayed only if the ratio (N06>= about 3%).
wherein
N06 is the percentage of readings during the night as claimed in claim 24.
109. The system as claimed in claim 108, wherein said third predetermined duration
is at least about 35 days.
110. The method as claimed in claim 1, wherein said method for evaluating the
HbAic of a patient based on said BG data collected is accomplished without the need
for prior HbAic information.
111. The system as claimed in claim 19 or 37, wherein said system for evaluating
the HbAic
of a patient based on said BG data collected is accomplished without the need for prior HbAic information.

Documents:

0370-chenp-2005 abstract.pdf

0370-chenp-2005 assignment.pdf

0370-chenp-2005 claims-duplicate.pdf

0370-chenp-2005 claims.pdf

0370-chenp-2005 correspondence-others.pdf

0370-chenp-2005 correspondence-po.pdf

0370-chenp-2005 descripotion (complete)-duplicate.pdf

0370-chenp-2005 description (complete).pdf

0370-chenp-2005 drawings.pdf

0370-chenp-2005 form-1.pdf

0370-chenp-2005 form-18.pdf

0370-chenp-2005 form-26.pdf

0370-chenp-2005 form-3.pdf

0370-chenp-2005 form-5.pdf

0370-chenp-2005 pct search report.pdf

0370-chenp-2005 pct.pdf

0370-chenp-2005 petition.pdf

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Patent Number

218931

Indian Patent Application Number

370/CHENP/2005

PG Journal Number

23/2008

Publication Date

06-Jun-2008

Grant Date

16-Apr-2008

Date of Filing

11-Mar-2005

Name of Patentee

UNIVERSITY OF VIRGINIA PATENT FOUNDATION

Applicant Address

Inventors:

#	Inventor's Name	Inventor's Address
1	KOVATCHEV, Boris, P
2	COX, Daniel, J

PCT International Classification Number

G0F1/00

PCT International Application Number

PCT/US2003/025053

PCT International Filing date

2003-08-08

PCT Conventions:

#	PCT Application Number	Date of Convention	Priority Country
1	60/478,377	2003-06-13	U.S.A.
2	60/402,976	2002-08-13	U.S.A.