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WO2001072208A2 - Method, system, and computer program product for the evaluation of glycemic control in diabetes from self-monitoring data - Google Patents

Method, system, and computer program product for the evaluation of glycemic control in diabetes from self-monitoring data

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WO2001072208A2
WO2001072208A2 PCT/US2001/009884 US0109884W WO0172208A2 WO 2001072208 A2 WO2001072208 A2 WO 2001072208A2 US 0109884 W US0109884 W US 0109884W WO 0172208 A2 WO0172208 A2 WO 0172208A2
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bg
risk
data
sh
glucose
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PCT/US2001/009884
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French (fr)
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WO2001072208A3 (en )
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Boris P. Kovatchev
Daniel J. Cox
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University Of Virginia Patent Foundation
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/3418Telemedicine, e.g. remote diagnosis, remote control of instruments or remote monitoring of patient carried devices
    • G16H50/50
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/0002Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/145Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • G16H10/40
    • G16H10/60

Abstract

A method, system (710), and computer program predict the long-term risk of hyperglycemia, and the long-term and short-term risks of severe hypoglycemia in diabetics, based on blood glucose readings collected by a self-monitoring blood glucose device (728). Glucose meter (728) obtains data from patien (712) and transfers it to a PC or PDA (740) via modem (732) or other communication link (714). After processing, information may be obtained from the PC (740) by a healthcare provider computer (738) via link (736). The method, system (710), and computer program enhance existing home blood glucose monitoring devices by introducing an intelligent data interpretation component capable of predicting both HbA1c and periods of increased risk of hypoglycemia. The method, and computer program enhance emerging continous monitoring devices by similar features. With these predictions, the diabetic can take steps to prevent the adverse consequences associated with hyperglycemia and hypoglycemia.

Description

METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT

FOR THE EVALUATION OF GLYCEMIC CONTROL IN

DIABETES FROM SELF-MONITORING DATA

CROSS-REFERENCES TO RELATED APPLICATIONS

The present invention claims priority from U.S. Provisional Patent Application Serial No. 60/193,037 filed March 29, 2000, entitled "Algorithm for the Evaluation of Glycemic Control in Diabetes From Self-Monitoring Data" the entire disclosure of which is hereby incoφorated by reference herein.

US GOVERNMENT RIGHTS

This invention was made with United States Government support under Grant Nos. NIH / NIDDK: RO1 DK 28288 and NIH / NIDDK: RO1 DK 51562, both awarded by National Institutes of Health. The United States Government has certain rights in the invention.

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 (HbAιc 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 Conventional Insulin 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 acute 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 Group: 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 Metab 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 integrated glucose control and HbAlc 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 HbAιc, and this curvilinear association yielded a correlation coefficient of 0.98 (See Aaby Svendsen P, Lauritzen T, Soegard TJ, Nerup J (1982). Glycosylated Hemoglobin and Steady-State Mean Blood Glucose Concentration in Type 1 (Insulin-Dependent) Diabetes, Diabetologia. 23, 403-405). In 1993 the DCCT concluded that HbAlc was the "logical nominee" for a gold-standard glycosylated hemoglobin assay, and the DCCT established a linear relationship between the preceding mean BG and HbAιc (See Santiago JV (1993). Lessons from the Diabetes Control and Complications Trial, Diabetes. 42, 1549-1554). Guidelines were developed indicating that an HbAlc of 7% corresponds to a mean BG of 8.3 mM (150 mg/dl), an HbAιc of 9% corresponds to a mean BG of 11.7 mM (210 mg/dl), and a 1% increase in HbAlc corresponds 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 a patient's glycemic control with a single, simple test, namely HbAlc. However, studies clearly demonstrate that HbAlc 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 HbAlc, and hypoglycemia unawareness. One recent review details the current clinical status of this problem, and provides options for preventing SH, that are available to patients and their health care providers (See Bolli, 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 measurements through Self-Monitoring of BG (SMBG). However, the problem with SMBG is that there 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 HbAlc 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 HbAlc 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-term 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 HbAlc 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 transformation ofthe blood glucose measurement scale (See Kovatchev BP, Cox DJ, Gonder-Frederick LA and WL Clarke (1997). Symmetization ofthe 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 ofthe 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 (3.9 to lOmM) is not centered within the scale. The inventors correct this asymmetry by introducing a transformation, f(BG), which is a continuous function defined on the BG range [1.1, 33.3], having the two-parameter analytical form: f(BG, a, β) = [(In (BG ))a -β], a, β > 0 and which satisfies the assumptions:

Al : f(33.3, a, β) = -f(l.l, a, β) anά A2: f(10.0, α, β) = -f(3.9, α, β). Next,f() is multiplied by a third scaling parameter to fix the minimum and maximum values ofthe transformed BG range at - VΪO and VlO respectively. These values are convenient since a random variable with a standard normal distribution has 99.8% of its values within the interval [- Vϊθ" , VΪO ] . If BG is measured in mmol/1, when solved numerically with respect to the assumptions Al and A2, the parameters of the function/CRG, a, β) are a= 1.026, β = 1.861, and the scaling parameter is γ = 1.794. If BG is measured in mg/dl instead, the parameters are computed to be a = 1.084, /?- 5.381, and γ= 1.509.

Thus, when BG is measured in mmol/1, the symmetrizing transformation is f(BG) = 1.794[(ln (BG))1026 - 1.861]. and when BG is measured in mg/dl the symmetrizing transformation is f(BG) = 1.509 [(In (BG))' 084 - 5.381].

On the basis ofthe symmetrizing transformation/^ 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 Self-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, Schlundt D, Clarke WL. Assessment of Risk for Severe Hypoglycemia Among Adults With IDDM: Validation ofthe Low Blood Glucose Index, Diabetes Care 21 :1870-1875, 1998). Given a series of SMBG data the Low BG Index is computed as the average of 10. f(BG)2 taken for values of f(BG) <0 and 0 otherwise. Also suggested was a High BG Index, computed in a symmetrical to the Low BG Index manner, however this index did not find its practical application. Using the Low BG Index in a regression model the inventors were able to account for 40% ofthe 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 Blood Glucose Transitions and its Relationship With Severe Hypoglycemia. Diabetes, 48: Supplement 1, A363, 1999).

In addition, the inventors developed some data regarding HbAlc and SMBG (See Kovatchev BP, Cox DJ, Straume M, Farhy LS. Association of Self-monitoring Blood Glucose Profiles with Glycosylated Hemoglobin. In: Methods in Enzymology, vol. 321 : Numerical Computer Methods. Part C. Michael Johnson and Ludvig Brand, Eds., Academic Press, NY; 2000).

These developments became a part ofthe theoretical background of this invention. In order to bring this 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 HbAlc, long-term and short-term risk for hypoglycemia. The development of these methods was, 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 of HbAlc.

The inventors 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 INVENTION

The invention includes a data analysis method and computer-based system for the simultaneous evaluation, from routinely collected SMBG data, ofthe two most important components of glycemic control in diabetes: HbAlc 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 invention pertains directly to the enhancement of existing home blood glucose monitoring devices by introducing an intelligent data interpretation component capable of predicting both HbAιc and periods of increased risk of hypoglycemia, as well as to enhancement of future continuous monitoring devices by the same features.

One aspect ofthe invention includes a method, system, and computer program product for evaluating HbAlc from a predetermined period of collected SMBG data, for example 4-6 weeks. In one embodiment, the invention provides a computerized method and system for evaluating the HbAlc of a patient based on BG data collected over a predetermined duration. The method includes computing weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on the collected BG data; estimating HbAιc using a predetermined mathematical formula based on the computed WR and Dr; and providing a predetermined confidence interval for classification of said estimated value of HbAlc. Another aspect ofthe invention includes a method, system, and computer program product for estimating the long-term probability for severe hypoglycemia (SH). This method uses SMBG readings from a predetermined period, for example 4- 6 weeks, and predicts the risk of SH within the following 6 months. In 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 includes: computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on the collected BG data; estimating the number of future SH episodes using a predetermined mathematical formula based on the computed WL and DrDn; and defining a probability of incurring a select number of SH episodes respective to said estimated SH episodes.

Still yet another aspect ofthe 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 ofthe 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 includes: computing weighted deviation toward low blood glucose (WL); determining Max(wl) by calculating maximum value oϊwl(BG;2); determining risk value by taking the geometric mean of WL and Max(wl) over the predetermined duration; providing a predetermined threshold risk value; and comparing the determined risk value to the threshold risk value. These three aspects ofthe invention can be integrated together to provide continuous information about the glycemic control of an individual with diabetes, and enhanced monitoring ofthe risk of hypoglycemia.

These and other objects, along with advantages and features ofthe 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 ofthe present invention, as well as the invention itself, will be more fully understood from the following description of preferred embodiments, when read together with the accompanying drawings in which:

FIG. 1 is a flow chart illustrating the method of calculating the estimated HbAιc and predicted HbAlc confidence intervals in accordance with the present invention.

FIG. 2 is a flow chart illustrating the method of calculating the estimated number of future SH episodes and the associated probability thereof in accordance with the present invention.

FIG. 3 is a flow chart illustrating the method of calculating the estimated short term risk of an incurring imminent SH in accordance with the present invention.

FIG. 4 is graphical representation of a typical BG disturbances observed before and after an episode of severe hypoglycemia. FIG. 5 illustrates the action ofthe method for predicting short-term SH by presenting 10 weeks of data for Subject A (upper panel) and Subject B (lower panel). SH episodes are marked by triangle; a black line presents the risk value. When the risk threshold is crossed, the method indicates a subsequent high-risk period (gray bar).

FIG. 6 is a functional block diagram for a computer system for implementation ofthe present invention.

FIGS. 7-9 are schematic block diagrams of alternative variations ofthe present invention related processors, communication links, and systems.

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 ofthe method. The inventive methods for evaluating HbAlc, 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. Stationary measures of BG deviation

According to the inventors' theory of BG symmetrization (See Kovatchev BP,

Straume M, Cox DJ, Farhi LS. Risk Analysis of Blood Glucose Data: A Quantitative

Approach to Optimizing the Control of Insulin Dependent Diabetes. J of Theoretical Medicine, 3ιl-10,.2001) the natural clinical center ofthe BG measurement scale is at

BG level of 112.5 mg/dl (6.25 mmol/1) - a safe euglycemic value for a diabetes patient.

Given this clinical center ofthe BG scale, the weighted deviations to the left

(towards hypoglycemia) or to the right (towards hyperglycemia) are computed. The degree of weighting of these deviations will be represented by parameters a and b respectively as follows: wl(BG;a) = 10.f(BG)a if f(BG)<0 and 0 otherwise, and wr(BG;b) = 10.f(BG)b if f(BG)>0 and 0 otherwise, where f(BG) is the BG symmetrization function presented in the background section. The weighting parameter a and b could be different, or the same for the left and right deviations. The inventors' data analyses demonstrated that the optimal for practical application parameter values are α=2 (which is the parameter value used for computation ofthe Low BG Index) and b-1. Given a series of BG readings xj, x , ... x„, the average weighted deviations to the left and to the right ofthe clinical center of the BG scale are defined as:

1 " 1 "

WL = — V W/(JC, ;2) and WR = — Yw(x, ;1) respectively.

These two measures of BG deviation do not depend on the timing ofthe BG readings, and therefore are stationary. In order to capture the dynamics of BG change, measures ofthe BG rate of change are introduced as provided below.

Computation of BG risk rate of change

Let xj, x2, ... x» be n SMBG readings of a subject recorded at time points ti, t2, ... tn. This data is next transformed by calculating the numbers f(xι), f(x ) ,■■■, f(xn) and draw a cubic spline S(t) passing through the points (tj ,f(xι)), (t2,f(x2,)) ,■■■, (I ,f(xn))- Thus, the function S(t) is a continuous function defined on the whole interval [tj , t„] and such that S(tj)=f(xj), for j=l, ...,n. Also calculated are the set of numbers sk =10.S(k+ tj)2 for k=0,l, ..., t„ - t thus getting interpolated values at one- hour increments.

Next, consider all couples of numbers s* with consecutive indices: Co=(so,sι), and denote by u/,the set of all couples Ck, such that Sk > Sk+i and by Md„ the set of all couples Ch, such that Sk < k+i .

Finally, let DrDn be the average ofthe numbers Sk+i - Sk, provided that C* e Mdn , and Dr be the average ofthe numbers sk+i - S , provided that e Mup+ Md„.

The numbers DrDn and Dr provide a measure for the rate of change of BG in a "risk space," e.g. the rate of change ofthe risk associated with any BG level change. In addition, DrDn measures the rate of BG change only when BG goes down, i.e. DrDn evaluates how quickly the risk could increase when BG falls, while Dr is a measure ofthe overall vulnerability of BG to fluctuations. It is further asserted that DrDn will be associated with risk for hypoglycemia (if someone's blood glucose could fall quickly, his/her risk for hypoglycemia would be higher), while Dr will be associated with the overall stability of BG.

Software code (presented in SPSS control language) The first is for when the BG readings are in mmol/L, and in this case the variable is BGMM. The second is for when the BG readings are in mg/dl, and in this case the variable is BGMG.

If BG is measured in mmol L, each BG reading is first transformed as follows:

SCALEl=(ln(BGMM))**1.026 - 1.861 RISK1=32.185*SCALE1*SCALE1

If BG is measured in mg/dl, each BG reading is first transformed as follows: SCALE2= (In (BGMG) )**1.08405 - 5.381 RISK2=22.765*SCA E2*SCALE2

Further, the left and right weighted deviations are computed as follows: L=0 L=0

IF (SCALE1 le 0.0) L=RISK1 R=0

IF (SCALE1 gt 0.0) WR=sqrt (RISK1 ) Provided that the BG readings are equally spaced in time, or are interpolated at one-hour increments, the BG rate of change is computed as:

Dr = RISK1(BG)-RISK1<BG-1) DrDn=0 IF (SCALE le 0.0 and Dr gt 0) DrDn=Dr

Finally, an aggregation pass through all BG readings for a subject will produce:

WL = mean ( L ) WR = mean ( H ) Dr = mean ( Dr) , and DrDn = mean ( DrDn)

Method for the Evaluation of HbAi,. A preferred embodiment of HbAlc evaluation method 100 according to the invention is illustrated in FIG. 1. In a first step 102, SMBG data is collected over a predetermined period of time. For example, the SMBG data is collected over 4-6 weeks with a frequency of 3-5 BG measurements per day, of which are transformed by the code or formulas presented in the previous section. Different formulas are to be used if the BG measurements are stored in mg/dl, or in mmol/1. One skilled in the art would appreciate that various levels, durations, and frequencies can be employed. In a step 104, weighted deviation towards high blood glucose (WR) and estimated rate of change of blood glucose (Dr) is computed using the formula / code discussed above. In a step 106, an estimate of HbAlc from self-monitoring data is computed using the linear function: EstHBAlc = 0.9008*WR - 0.8207*DR + 6.7489. It is noted that the coefficients of this function are derived from data for 867 individuals with diabetes, and one would recognize that further data accumulation may update these coefficients. In step 108 HbAlc estimate categories representing a range of values for estimated HbAic are defined according to Table 1.

Table 1 : Defining categories on the basis of EstHBAlc:

In step 110 predicted confidence intervals for corresponding HbAιc are derived according to Table 2.

Table 2: Predicted 95% confidence intervals for classification of HbAιc:

In step 112, the estimated HbAιc from step 106 is assigned in one ofthe categories provided in Table 1 and / or Table 2.

Empirical Validation of Evaluation of HbAic The intervals for HbAlc in Table 2 are based on extensive research. To validate these intervals we analyzed SMBG and HbAlc data from 867 subjects with diabetes. All subjects were instructed to use BG memory meters for six months and to measure their BG two to four times a day. During the same period 5 to 8 HbAlc assays were performed for each subject. The memory meter data were electronically downloaded and stored in a computer for further analysis. This procedure produced a database containing more than 300,000 SMBG readings and 4, 180 HbAlc assays taken over six months. Analysis of variance was conducted to compare HbAjc in the seven categories identified in Table 1. The five categories were highly significantly different, with F=91 and pθ.00001. Moreover, the average HbAιc was significantly different for each pair of categories as demonstrated by Duncan's ranges, with pθ.01. Also, 95% confidence intervals were computed for the mean value of HbAic in each ofthe seven categories. These confidence intervals were used as a basis for computing the HbAιc intervals presented in Table 2. Post-hoc analysis ofthe classification power of this method demonstrated that the method was well protected against extreme errors such as incorrectly classifying HbAιc in category 1, 2 or 3 on the basis of SMBG while the acmal HbAιc was greater than 9.5%, or classifying HbAιc in category 5, 6 or 7 while the actual HbAlc was below 9.0%.

In summary, after an initial 4-6 weeks of SMBG readings the computerized method computes an interval estimate for the value of HbAlc that can be used to track patients' changes in glycemic control in the high BG range. Method for Evaluation ofthe Long-Term Probability for Severe Hypoglycemia fSH) A preferred embodiment of long-term probability for SH evaluation method 200 according to the invention is illustrated in FIG. 2. In a first step 202, SMBG data is collected over a predetermined period of time. For example, the SMBG data is collected over 4-6 weeks with a frequency of 3-5 BG measurements per day, of which are transformed by the code or formulas presented immediately above. Different formulas are to be used if the BG measurements are stored in mg/dl, or in mmol/1; One skilled in the art would appreciate that various levels, durations, and frequencies can be employed. In a step 204, WL and DrDn are computed using the formula / code as discussed above. In step 206, an estimate ofthe number of future SH episodes is computed using the linear function:

EstNSH = 3.3613*WL - 4.3427*DrDn - 1.2716. It is noted that the coefficients of this function are derived from data for 181 individuals with diabetes, and one would appreciate that further data accumulation may update these coefficients. It is further noted that this formula provides a single value estimate for the number of future SH episodes and that through additional methodologies, as discussed below, categories are provided with ranges and confidence levels for enhanced clinical applications. In step 208, estimated number of SH episodes (estNSH) categories representing a range of values for estNSH are defined according to Table 3.

Table 3: Classification of EstNSH:

In step 210, respective to the estNSH categories, the probability of incurring 0, 1-2, or more than 2 SH episodes in the following six months is derived, as represented in table 4.

In step 212, the EstNSH from step 206 is assigned in one of the categories provided in Table 3 and / or Table 4.

Empirical Validation of Evaluation ofthe Long-Term Probability for SH One-hundred-eighty-one adults with Type 1 diabetes (mean age 37 years, duration of diabetes 18 years) used memory meters to collect more than 34,000 SMBG over a month. Then for the next six months they recorded in diaries any occurrence of SH. The SMBG data were mathematically transformed and an a linear regression model was used to predict future severe hypoglycemia resulting in a highly significant model (F=36.3, pO.OOOl) and multiple R of 55%.

All subjects were classified into 4 categories using the present long-term SH method. The average number of future SH episodes in categories 1, 2, 3, and 4 was 0.3, 2.0, 5.0, and 9.75 respectively. Analysis of variance demonstrated highly significant differences between these categories, F=19.0, pO.OOOl. In summary, a linear combination ofthe Low BG Index and the rate of drop of

BG as measured in "risk space" provide an accurate assessment ofthe long-term risk of SH. Because it is based on SMBG records that are automatically stored by many reflectance meters, this is an effective and clinically useful indicator of patients' glycemic control in the low BG range.

Method for the Evaluation ofthe Short-term (within 24 hours) Risk of Hypoglycemia

A preferred embodiment of short term risk of SH evaluation method 300 according to the invention is illustrated in FIG. 3. In a first step 302, SMBG is data is collected over a predetermined short term period. For example, the SMBG data is collected over a 24 hour period, with a frequency of 3-5 BG measurements per day - 4 or more readings, as a nominal level according to data analyses. One skilled in the art would appreciate that various levels, periods (durations), and frequencies can be employed. In a step 304 WL(24) and Max(wl) is computed from all readings collected within the preceding 24 hours, wherein the maximum value of wl(BG;2) is Max(wl). In step 306, the risk value is by taking the geometric mean of WL and Max(wl) over the 24 hour period, wherein said risk value is mathematically defined as:

Risk(24) = ^WL(24) * Max(wl) ; In step 308 a threshold risk value is determined. In step 310 the estimated risk value is compared to the threshold risk value. For example, if the threshold risk value is set at 17, then if Risk(24)> 17, then-based on the SMBG data collected over the previous 24 hours— the resultant indication is a high risk ofthe patient incurring imminent hypoglycemia. In other words, this is a decision-making rule that considers a 24-hour period of SMBG data and judges whether this period is likely to precede an imminent hypoglycemia episode. The threshold value of 17 is derived from an extensive data set, however, it is recognized that it is possible that this value maybe adjusted with further accumulation of data or for additional objectives.

Empirical Validation of Evaluation ofthe Short-term Risk of Hypoglycemia: Eighty-five individuals were recruited through advertisement in newsletters, diabetes clinics, and through direct referrals. The inclusion criteria were: 1) age of 21- 60 years; 2) type I diabetes with at least two years duration, and insulin use since the time of diagnosis; 3) at least 2 documented SH episodes in the past year; and 4) routine use of SMBG devices for diabetes monitoring. The participants were instructed to use the meter 3-5 times a day, and to record in monthly diaries any SH episodes, including the exact dates and times of their occurrences. SH was defined as severe neuroglycopenia that results in stupor or unconsciousness and precludes self-treatment. For each subject the study continued 6-8 months and each month the subject's meter was downloaded and the SH diary was collected. The memory capacity ofthe meters was sufficient, and the downloading was often enough, so that no BG data were lost. No changes were made in the participants' diabetes management routine, nor were any additional treatments administered during the study. During the study a total of 75,495 SMBG readings (on average 4.0±1.5 per subject per day) were downloaded from the participants' memory meters, and 399 (4.7±6.0 per subject) SH episodes were recorded in their diaries. An important finding, among other things, was that episodes of moderate or severe hypoglycemia are preceded and followed by measurable BG disturbances. In the 24-hour period before an SH episode the Low BG Index (e.g. WL) rose (pO.OOl), the average BG was lower (p=0.001), and the BG variance increased (p=0.001). In the 24 hours following the SH episode, the Low BG Index and BG variance remained elevated (p<0.001), but the average BG returned to its baseline. To this end, FIG. 4 is graphical representation of a typical BG disturbance observed before and after an episode of severe hypoglycemia. In the period 48 to 24 hours before the SH episode, the average BG level decreased and the variance of BG increased. In the 24-hour period immediately preceding the SH episode, the average BG level dropped further and the variance of BG continued to increase. In the 24-hour period following the SH episode, the average BG level normalized, but the BG variance remained greatly increased. Both the average BG and its variance returned to their baseline levels within 48 hours after the SH episode.

As such, as part ofthe invention, the disturbances presented in FIG. 4 are quantified from SMBG data to enable the evaluation ofthe short-term risk of hypoglycemia. The cutoff value of Risk(24)=l 7 is derived from an optimization along the following restrictions: 1) the method had to predict a maximum percentage of SH episodes, i.e. to identify as risky a maximum percentage of 24-hour periods preceding SH, and 2) to prevent overestimation ofthe risk, the method had to identify as risky no more that 15% ofthe total time ofthe study (one day a week on average). The cutoff risk value of 17 was held constant for all subjects. The reason for choosing the value of 15% was to prevent the patients from becoming irritated with an overabundance of "false alarms" and then ignoring "true alarms." In practice, a patient's physician can select an alternate value depending on the severity ofthe patient's diabetes and particular objectives.

The following example illustrates the action ofthe algorithm on the SMBG data of two participants in the study. FIG. 5 presents ten weeks of data for Subject A (upper panel) and Subject B (lower panel). SH episodes are marked by triangles; a black curve presents the risk value. When the risk threshold (the horizontal line at Risk=l 7) is crossed, the algorithm indicates a subsequent high-risk period (gray bar). For Subject A, 7 out of 9 SH episodes are predicted and there are 5 false alarms, e.g. high-risk periods that did not result in SH; for Subject B there are 3 false alarms and the only SH episode is predicted. It is obvious that Subject B's risk values when compared to Subject A's risk values, include more and higher deviations. For both subjects, all SH episodes were accompanied by supercritical risk values, and about half of all large deviations were accompanied by one or more SH episode.

Across all participants in the study, 44% of all recorded SH episodes were preceded, within 24 hours, by a high-risk period, and 50% were preceded, within 48 hours, by a high-risk period. If only periods with either at least 3, or at least 4 SMBG measurements were considered, the accuracy ofthe latter prediction increased to 53% and 57%, respectively. Post-hoc analysis of BG levels occurring during, or immediately after, high-risk periods that were not followed by an SH episode, i.e. during, or immediately after false alarms, demonstrated that the average per subject minimum of such BG levels was 2.3±0.2 mmol/1 versus 5.9±1.7 mmol/1 (t=19.5, p<0.0001) for all non-risk periods, including all SH episodes that remained unaccounted for. This indicates that, although symptomatic SH did not occur, BG levels following high-risk periods were notably low.

In summary, the inventors simulated the action ofthe short-term risk method on a 6-month series of SMBG readings for 85 individuals with Type I diabetes. With four or more SMBG readings per day, at least 50% of all episodes of SH could be anticipated. Even when symptomatic SH did not occur, the algorithm predicted episodes of moderate hypoglycemia.

Integration ofthe Three Methods The three methods of this invention, as discussed above and illustrated in FIGS. 1-3, utilize the same series of SMBG data. Therefore, from an SMBG-device point of view, a unified display or matrix ofthe results of these three methods could be made similar to the grid output presented below:

Thus, for example, the output for subject 1 (Ss 1) shown in the above grid indicates that this person is likely to have HbAιc between 9 and 9.5%, and has a 90% chance not to experience severe hypoglycemia in the subsequent 6 months. The output for subject 2 (Ss 2) indicates that this person is likely to have HbAlc below 8%, and has a greater than 80% chance to experience at least 3 SH episodes in the subsequent 6 months.

In addition to this grid-output, the short term risk method provides a continuous tracking ofthe risk of imminent hypoglycemia and can be used to sound an alarm when this risk becomes high.

The method ofthe 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 personal digit assistants (PDAs). In an example embodiment, the invention was implemented in software running 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 communication infrastructure 606 (e.g., a communications bus, cross-over bar, or network). Computer system 600 includes a display interface 602 that forwards graphics, text, and other data from 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 include a secondary memory 610. The secondary memory 610 may include, for example, a hard disk drive 612 and/or a removable storage drive 614, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, 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, a PCMCIA slot and card, etc. Software and data transferred via communications interface 624 are in the form of signals 628 which may be electronic, electromagnetic, optical or other signals capable of being received by communications interface 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 and other communications channels.

In this document, the terms "computer program medium" and "computer usable medium" are used to generally refer to media such as removable storage drive 914, 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 ofthe present invention as discussed herein. In particular, the computer programs, when executed, enable processor 604 to perform the functions ofthe present invention. Accordingly, such computer programs represent controllers of computer system 600.

In an embodiment where the invention is implemented using software, the 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 functions ofthe 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 ofthe hardware state machine to perform the functions described herein will be apparent to persons skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using a combination of both hardware and software. In an example software embodiment ofthe invention, 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.

FIGS. 7 - 9 show block diagrammatic representation of alternative embodiments ofthe invention. Referring FIG. 7, there is shown a block diagrammatic representation ofthe system 710 essentially comprises the glucose meter 728 used by a patient 712 for recording, inter alia, 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. For instance, data stored may be stored within the glucose meter 728 and may be directly downloaded into the personal computer 740 through an appropriate interface cable. 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 functions as a BG acquisition mechanism. The BG meter or acquisition mechanism, device, tool, or system includes various conventional methods directed toward drawing a blood sample (e.g. by fingerprick) for each test, and a determination ofthe glucose level using an instrument that reads glucose concentrations by electromechanical or claorimetric methods. Recently, various methods 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. describes a noninvasive technique of measuring blood glucose concentration using near-IR radiation diffuse-reflection laser spectroscopy. Similar near-IR spectrometric devices are also described in U.S. Pat. No. 5,086,229 to Rosenthal et al. and U.S. Pat. No. 4,975,581 to Robinson et al.

U.S. Pat. No. 5,139,023 to Stanley 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 describes a passive glucose monitor that collects perspiration through a skin patch, where a cholinergic agent is used to stimulate perspiration secretion from 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.

In addition, U.S. Pat. No. 5,279,543 to Glikfeld 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 describes an iontophoretic apparatus 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 Berner describes a sampling system for measuring the concentration of an analyte present..

Further yet, the BG meter or acquisition mechanism may include indwelling catheters and subcutaneous tissue fluid sampling.

The computer or PDA 740 includes the software and hardware necessary to process, analyze and interpret 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 ofthe 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 ofthe 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 housing 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 22 is designed to execute a computer program stored in memory 824 to perform the various calculations and control functions 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 interface, 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 microprocessor 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 ofthe patient 812, the insulin dose values, the insulin types, and the parameter values used by microprocessor 822 to calculate 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 connected 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 ofthe type which produces digital values which are output directly to microprocessor 822. Alternatively, blood glucose meter 828 may be ofthe type which produces analog values. In this alternative 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 establishing a communication link between apparatus 810 and a personal computer 840 or a healthcare provider computer 838 through a communication network 836. Specific techniques for connecting electronic devices through connection cords are well known in the art. Another alternative example is "bluetooth" technology communication. Alternatively, FIG. 9 shows a block diagrammatic representation of an alternative embodiment having a diabetes management system that is a patient- operated apparatus 910, similar as shown in FIG. 8, having a housing preferably sufficiently compact to enable the apparatus 910 to be hand-held and carried by a patient. However, the present embodiment includes a separate or detachable glucose meter or BG acquisition mechanism 928.

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 summary, the invention proposes a data analysis computerized method and system for the simultaneous evaluation of the two most important components of glycemic control in individuals with diabetes: HbAιc and the risk of hypoglycemia. The method, while using only routine SMBG data, provides, among other things, three sets of output.

The potential implementations ofthe method, system, and computer program product ofthe 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 HbAlc, 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 a second 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 ofthe invention can be directly 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 HbAιc 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, the invention evaluates the effectiveness of various treatments for diabetes.

Further still, as patients with diabetes face a life-long optimization problem of maintaining strict glycemic control without increasing their risk of hypoglycemia, the present invention alleviates 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 their everyday environments.

Additionally, the invention provides the missing link by proposing three distinct, but compatible, algorithms for evaluating HbAlc 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-term risk of hyperglycemia.

Finally, another advantage, the invention evaluates the effectiveness of new insulin or insulin delivery devices. Any manufacturer or researcher of insulin or insulin delivery devices can utilize the embodiments ofthe invention to test the relative success of proposed or tested insulin types or device delivery designs.

The invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The foregoing embodiments are therefore to be considered in all respects illustrative rather than limiting of the invention described herein. Scope of the invention is thus indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

Claims

CLAIMS We claim: A computerized method for evaluating the HbAlc of a patient based on BG data collected over a predetermined duration, said method comprising: computing weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on said collected BG data; and estimating HbAlc using a predetermined mathematical formula based on said computed WR and Dr.
2. The method of claim 1, wherein: said computed WR is mathematically defined from a series of BG readings xj, x2, ... xn taken at time points ty, t2, ..., t„ as:
WR = -Y wr(x, ;l) n TXx where: wr(BG;b) = 10f(BG)b if f(BG)>0 and 0 otherwise, b =1 , representing a weighting parameter, and
said computed DR is mathematically defined as: Dr = average of Sk+i - Sk, where: sk =10.S(k+ tif for k=0,l, ..., t„ - tj,
3. The method of claim 1 , wherein said the estimate of HbA)C from said BG monitoring data is mathematically defined as : Estimated HbAic = 0.9008(WR) - 0.8207(DR) + 6.7489.
4. The method of claim 1, further comprising: defining predetermined categories for the estimate of HbAιc , each of said HbAlc estimate categories representing a range of values for estimated HbAlc; and assigning said estimated HbAlc to at least one of said HbAic estimate categories.
5. The method of claim 4, wherein said HbAιc estimate categories are defined as follows: classified category 1, wherein said estimated HbAιc is less than about 7.8; classified category 2, wherein said estimated HbAιc is between about 7.8 and about 8.5; classified category 3, wherein said estimated HbAιc is between about 8.5 and about 9.0; classified category 4, wherein said estimated HbAιc is between about 9.0 and about 9.6; classified category 5, wherein said estimated HbAιc is between about 9.6 and about 10.3; classified category 6, wherein said estimated HbAιc is between about 10.3 and about 11.0; and classified category 7, wherein said estimated HbAιc is above about 11.0.
6. The method of claim 5, further comprising: defining predicted confidence intervals for corresponding said HbAιc estimate categories, wherein said predicted confidence intervals are defined as follows: said classified category 1 corresponds with a predicted HbA]C less than about 8.0; said classified category 2 corresponds with a predicted HbAlc between about 8.0 and about 8.5; said classified category 3 corresponds with a predicted HbAιc between about 8.5 and about 9.0; said classified category 4 corresponds with a predicted HbA]c between about 9.0 and about 9.5; said classified category 5 corresponds with a predicted HbAιe between about 9.5 and about 10.1; said classified category 6 corresponds with a predicted HbAlc between about 10.1 and about 11.0; and said classified category 7 corresponds with a predicted HbAlc above about 11.0.
7. The method of claim 4, further comprising: defining predicted confidence intervals for corresponding said HbAlc , each of said predicted confidence intervals representing a range of values for HbAlc.
8. The method of claim 7, wherein said predicted HbAlc confidence intervals have about a 95% confidence level.
9. A computerized method for evaluating the HbAlc of a patient based on BG data collected over a predetermined duration, said method comprising: computing weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on said collected BG data; estimating HbAlc using a predetermined mathematical formula based on said computed WR and Dr; and providing a predetermined confidence interval for classification of said estimated value of HbAlc.
10. The method of claim 9, wherein: said confidence interval is between about 85% to about 95%.
11. A system for evaluating HbAιc 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; a processor programmed to: compute weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on said collected BG data; and estimate HbA]C using a predetermined mathematical formula based on said computed WR and Dr.
12. The system of claim 11, wherein: said computed WR is mathematically defined from a series of BG readings X , χ2, ... x„ taken at time points tj, t2, .... t„ as:
where: wr(BG;b) = 10.f(BG)b if f(BG)>0 and 0 otherwise, b =1 , representing a weighting parameter, and
said computed DR is mathematically defined as: Dr = average of k+i - S , where: sk =10.S(k+ ti)2 for k=0, l, ..., tn - t S(tj)=f(xj), for j=l n.
13. The system of claim 11 , wherein said the estimate of HbAιc from said BG monitoring data is mathematically defined as : Estimated HbAιc = 0.9008(WR) - 0.8207(DR) + 6.7489.
14. The system of claim 11, wherein said processor being further programmed to: define predetermined categories for the estimate of HbAic , each of said HbAιc estimate categories representing a range of values for estimated HbAιc; and assign said estimated HbAιc to at least one of said HbAic estimate categories.
15. The system of claim 14, wherein said HbAιc estimate categories are defined as follows: classified category 1, wherein said estimated HbAlc is less than about 7.8; classified category 2, wherein said estimated HbAlc is between about 7.8 and about 8.5; classified category 3, wherein said estimated HbAjc is between about 8.5 and about 9.0; classified category 4, wherein said estimated HbAιc is between about 9.0 and about 9.6; classified category 5, wherein said estimated HbAιc is between about 9.6 and about 10.3; classified category 6, wherein said estimated HbAιc is between about 10.3 and about 11.0; and classified category 7, wherein said estimated HbAιc is above about 11.0.
16. The system of claim 15, wherein said processor being further programmed to: define predicted confidence intervals for corresponding said HbAιc estimate categories, wherein said predicted confidence intervals are defined as follows: said classified category 1 corresponds with a predicted HbAιc less than about 8.0; said classified category 2 corresponds with a predicted HbAιc between about 8.0 and about 8.5; said classified category 3 corresponds with a predicted HbAιc between about 8.5 and about 9.0; said classified category 4 corresponds with a predicted HbAιc between about 9.0 and about 9.5; said classified category 5 corresponds with a predicted HbAιc between about 9.5 and about 10.1; said classified category 6 corresponds with a predicted HbAιc between about 10.1 and about 11.0; and said classified category 7 corresponds with a predicted HbAιc above about 11.0.
17. The system of claim 14, wherein said processor being further programmed to: define predicted confidence intervals for corresponding said HbAιc , each of said predicted confidence intervals representing a range of values for HbAιc.
18. The system of claim 17, wherein said predicted HbAιc confidence intervals have about a 95% confidence level.
19. A glycemic control system for evaluating HbAιc of a patient, 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; a processor programmed to: compute weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on said collected BG data; and estimate HbAιc using a predetermined mathematical formula based on said computed WR and Dr.
20. A computer program product comprising a computer useable medium having computer program logic for enabling at least one processor in a computer system to evaluate HbAic of a patient based on BG data, said computer program logic comprising: computing weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on said collected BG data; and estimating HbAlc using a predetermined mathematical formula based on said computed WR and Dr.
21. The computer program product of claim 20, wherein said computer program logic further comprises: providing a predetermined confidence interval for classification of said estimated value of HbAic, wherein said confidence interval is a single value or a range of values.
22. A computerized method for evaluating the long term probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said method comprising: computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on said collected BG data; and estimating the number of future SH episodes using a predetermined mathematical formula based on said computed WL and DrDn.
23. The method of claim 22, wherein: said computed WL is mathematically defined from a series of BG readings x/, x2, ... x„ taken at time points tj, t2, ..., t„ as:
where: wl(BG;a) = 10.f(BG)a if f(BG)>0 and 0 otherwise, a =2, representing a weighting parameter, and said computed DR is mathematically defined as: DrDn = average of Sk+i - Sk, provided that Sk< Sk+i, where: sk =10.S(k+ tj)2 for k=0, 1, ..., t„ - 1,,
24. The method of claim 22, wherein said estimated number of future SH episodes (EstNSH) is mathematically defined as : EstNSH = 3.3613(WL) - 4.3427(DrDn) - 1.2716.
25. The method of claim 22, further comprising: defining predetermined EstNSH categories, each of said EstNSH categories representing a range of values for EstNSH; and assigning said EstNSH to at least one of said EstNSH categories.
26. The method of claim 25, wherein said EstNSH categories are defined as follows: category 1, wherein said EstNSH category is less than about 0.775; category 2, wherein said EstNSH category is between about 0.775 and about 3.750 ; category 3, wherein said EstNSH category is between about 3.750 and about 7.000; and category 4, wherein said EstNSH category is above about 7.0.
27. The method of claim 26, further comprising: defining a probability of incurring a select number of SH episodes respectively for each of said assigned EstNSH categories; wherein said probability and said respective select number of SH are defined as: said classified category 1 corresponds with about a 90% probability of incurring about 0 SH episodes and about a 10% probability of incurring about 1 or more SH episodes over the predetermined duration; said classified category 2 corresponds with about a 50% probability of incurring about 0 SH episodes, 25 % probability of incurring about 1 to about 2 SH episodes, and 25 % probability of incurring more than 2 SH episodes over the predetermined duration; said classified category 3 corresponds with about a 25% probability of incurring about 0 SH episodes, 25 % probability of incurring about 1 to about 2 SH episodes, and 50 % probability of incurring more than 2 SH episodes over the predetermined duration; and said classified category 4 corresponds with about a 20% probability of incurring about 0 to about 2 SH episodes and about a 80% probability of incurring more than 2 SH episodes over the predetermined duration.
28. The method of claim 25, further comprising: defining a probability of incurring a select number of SH episodes respectively for each of said assigned EstNSH categories, and providing at least one probability of incurring a select number of SH episodes according to said EstNSH category to which said EstNSH is assigned.
29. A computerized method for evaluating the long term probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said method comprising: computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on said collected BG data; estimating the number of future SH episodes using a predetermined mathematical formula based on said computed WL and DrDn; and defining a probability of incurring a select number of SH episodes respective to said estimated SH episodes.
30. A system for evaluating the long 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; a processor programmed to: computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on said collected BG data; and estimating the number of future SH episodes using a predetermined mathematical formula based on said computed WL and DrDn.
31. The system of claim 30, wherein: said computed WL is mathematically defined from a series of BG readings xj, x2, ... x„ taken at time points t/, t2, ..., t„ as:
WL =-∑wl(x, ;2) n lX where: wl(BG;a) = 10f(BG)a if f(BG)>0 and 0 otherwise, a =2, representing a weighting parameter, and said computed DR is mathematically defined as: DrDn = average of Sk+ι - Sk, provided that sk< Sk+i, where: sk =10.S(k+ tι)2 for k=0,l, ..., t„ - t
32. The system of claim 30, wherein said estimated number of future SH episodes (EstNSH) is mathematically defined as : EstNSH = 3.3613(WL) - 4.3427(DrDn) - 1.2716.
33. The system of claim 30, wherein said processor being further programmed to: define predetermined EstNSH categories, each of said EstNSH categories representing a range of values for EstNSH; and assign said EstNSH to at least one of said EstNSH categories.
34. The system of claim 33, wherein said EstNSH categories are defined as follows: category 1, wherein said EstNSH category is less than about 0.775; category 2, wherein said EstNSH category is between about 0.775 and about 3.750 ; category 3, wherein said EstNSH category is between about 3.750 and about 7.000; and category 4, wherein said EstNSH category is above about 7.0.
35. The method of claim 34, wherein said processor being further programmed to: define a probability of incurring a select number of SH episodes respectively for each of said assigned EstNSH categories, wherein said probability and said respective select number of SH are defined as: said classified category 1 corresponds with about a 90% probability of incurring about 0 SH episodes and about a 10% probability of incurring about 1 or more SH episodes over the predetermined duration; said classified category 2 corresponds with about a 50% probability of incurring about 0 SH episodes, 25 % probability of incurring about 1 to about 2 SH episodes, and 25 % probability of incurring more than 2 SH episodes over the predetermined duration; said classified category 3 corresponds with about a 25% probability of incurring about 0 SH episodes, 25 % probability of incurring about 1 to about 2 SH episodes, and 50 % probability of incurring more than 2 SH episodes over the predetermined duration; and said classified category 4 corresponds with about a 20% probability of incurring about 0 to about 2 SH episodes and about a 80% probability of incurring more than 2 SH episodes over the predetermined duration.
36. The system of claim 33, wherein said processor being further programmed to: define a probability of incurring a select number of SH episodes respectively for each of said assigned EstNSH categories; and provide at least one probability of incurring a select number of SH episodes according to said EstNSH category to which said EstNSH is assigned.
37. A glycemic control system for evaluating the long term probability for severe hypoglycemia (SH) of a patient, 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; a processor programmed to: computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on said collected BG data; and estimating the number of future SH episodes using a predetermined mathematical formula based on said computed WL and DrDn.
38. A computer program product comprising a computer useable medium having computer program logic for enabling at least one processor in a computer system to evaluate long term probability for severe hypoglycemia (SH) of a patient based on BG data, said computer program logic comprising: computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on said collected BG data; and estimating the number of future SH episodes using a predetermined mathematical formula based on said computed WL and DrDn.
39. The computer program product of claim 38, wherein said computer program logic further comprises: defining a probability of incurring a select number of SH episodes respective to said estimated SH episodes.
40. A computerized method for evaluating the short term risk for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said method comprising: computing weighted deviation toward low blood glucose (WL); determining Max(wl) by calculating maximum value of wl(BG;2); and determining risk value by taking the geometric mean of WL and Max(wl) over said predetermined duration, said risk value is mathematically defined as: risk value = ^WL • Max(wl) .
41. The method of claim 40, wherein: said computed WL is mathematically defined from a series of BG readings xj, x ) ... xn taken over the predetermined duration as:
WL =-Y wl(x, ;2) n Xx where: wl(BG;a) = 10f(BG)a if f(BG)>0 and 0 otherwise, a =2, representing a weighting parameter.
42. The method of claim 40, further comprising: providing a predetermined threshold risk value; and comparing said determined risk value to said threshold risk value.
43. The method of claim 42, wherein: if said determined risk value is greater than said threshold value then short term risk of incurring a hypoglycemic episode is high; and if said determined risk value is less than said threshold value then short term risk of incurring a hypoglycemic episode is low.
44. The method of claim 43, wherein said short term is approximately a 24 hour period.
45. The method of claim 43, wherein said short term ranges from about 12 to about 72 hour period.
46. The method of claim 43, wherein said threshold value is approximately 17.
47. The method of claim 43, wherein said threshold value is between about 12 to 25.
48. A system for evaluating the short term risk 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; a processor programmed to: compute weighted deviation toward low blood glucose (WL); determine Max(wl) by calculating maximum value of wl(BG; 2); and determine risk value by taking the geometric mean of WL and Max(wl) over said predetermined duration, said risk value is mathematically defined as: risk value = -JWL • Max(wl) .
49. The system of claim 48, wherein: said computed WL is mathematically defined from a series of BG readings xj, x2, ... x„ taken over the predetermined duration as:
where: wl(BG;a) = 10.f(BG)a if f(BG)>0 and 0 otherwise, a =2, representing a weighting parameter.
50. The system of claim 48, wherein said processor being further programmed to: provide a predetermined threshold risk value; and compare said determined risk value to said threshold risk value.
51. The system of claim 50, wherein: if said determined risk value is greater than said threshold value then short term risk of incurring a hypoglycemic episode is high; and if said determined risk value is less than said threshold value then short term risk of incurring a hypoglycemic episode is low.
52. The system of claim 51 , wherein said short term is approximately a 24 hour period.
53. The method of claim 51, wherein said short term ranges from about 12 to about 72 hour period.
54. The system of claim 51 , wherein said threshold value is approximately 17.
55. The method of claim 51 , wherein said threshold value is between about 12 to 25.
56. A glycemic control system for evaluating the short term risk for severe hypoglycemia (SH) of a patient, 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; a processor programmed to: compute weighted deviation toward low blood glucose (WL); determine Max(wl) by calculating maximum value of wl(BG;2); and determine risk value by taking the geometric mean of WL and Max(wl) over said predetermined duration, said risk value is mathematically defined as: risk value =
57. A computer program product comprising a computer useable medium having computer program logic for enabling at least one processor in a computer system to evaluate the short term risk for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said computer program logic comprising: computing weighted deviation toward low blood glucose (WL); determining Max(wl) by calculating maximum value ofwl(BG;2); and determining risk value by taking the geometric mean of WL and Max(wl) over said predetermined duration, said risk value is mathematically defined as: risk value = WL • Max(wl) .
58. The computer program product of claim 57, wherein said computer program logic further comprises: providing a predetermined threshold risk value; and comparing said determined risk value to said threshold risk value.
5
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Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003065033A2 (en) * 2002-01-28 2003-08-07 Control Diabetes, Inc. Methods and systems for assessing glycemic control using predetermined pattern label analysis of blood glucose readings
WO2004015539A2 (en) 2002-08-13 2004-02-19 University Of Virginia Patent Foundation Managing and processing self-monitoring blood glucose
EP1416417A2 (en) * 2002-10-08 2004-05-06 Bayer Healthcare, LLC Mehtod and systems for data management in patient diagnoses and treatment
WO2006050980A2 (en) * 2004-11-15 2006-05-18 Novo Nordisk A/S Method and apparatus for monitoring long term and short term effects of a treatment
US7266400B2 (en) 2003-05-06 2007-09-04 Orsense Ltd. Glucose level control method and system
EP1956371A2 (en) 2006-12-21 2008-08-13 Lifescan, Inc. Systems, methods and computer program codes for recognition of patterns of hyperglycemia and hypoglycemia, increased glucose variability, and ineffective self-monitoring in diabetes
EP1988821A2 (en) * 2006-01-05 2008-11-12 University Of Virginia Patent Foundation Method, system and computer program product for evaluation of blood glucose variability in diabetes from self-monitoring data
WO2010077330A1 (en) * 2008-12-31 2010-07-08 Medtronic Minimed, Inc. Method and/or system for estimating glycation of hemoglobin
EP2218394A1 (en) * 2009-02-17 2010-08-18 Clifton A. Alferness System and method for providing a personalized tool for estimating glycated hemoglobin
US7815569B2 (en) 2004-04-21 2010-10-19 University Of Virginia Patent Foundation Method, system and computer program product for evaluating the accuracy of blood glucose monitoring sensors/devices
US7824333B2 (en) 2006-03-31 2010-11-02 Lifescan, Inc. Diabetes management methods and systems
US7914449B2 (en) 2004-03-17 2011-03-29 Sysmex Corporation Diagnostic support system for diabetes and storage medium
WO2013073983A1 (en) * 2011-11-16 2013-05-23 Vengerov Yury Yuzefovitch Device for reading results of analyses performed with the aid of test strips
US8744828B2 (en) 2012-07-26 2014-06-03 Rimidi Diabetes, Inc. Computer-implemented system and method for improving glucose management through modeling of circadian profiles
US8756043B2 (en) 2012-07-26 2014-06-17 Rimidi Diabetes, Inc. Blood glucose meter and computer-implemented method for improving glucose management through modeling of circadian profiles
US8768673B2 (en) 2012-07-26 2014-07-01 Rimidi Diabetes, Inc. Computer-implemented system and method for improving glucose management through cloud-based modeling of circadian profiles
WO2016093684A1 (en) * 2014-12-09 2016-06-16 Instituto Superior Autónomo De Occidente A.C. Portable electronic device that emits suggestions about activities or events interfering in a state of interest for an individual
US9501949B2 (en) 2004-10-07 2016-11-22 Novo Nordisk A/S Method and system for self-management of a disease

Families Citing this family (261)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6036924A (en) 1997-12-04 2000-03-14 Hewlett-Packard Company Cassette of lancet cartridges for sampling blood
US6391005B1 (en) 1998-03-30 2002-05-21 Agilent Technologies, Inc. Apparatus and method for penetration with shaft having a sensor for sensing penetration depth
US8974386B2 (en) 1998-04-30 2015-03-10 Abbott Diabetes Care Inc. Analyte monitoring device and methods of use
US8465425B2 (en) 1998-04-30 2013-06-18 Abbott Diabetes Care Inc. Analyte monitoring device and methods of use
US8480580B2 (en) 1998-04-30 2013-07-09 Abbott Diabetes Care Inc. Analyte monitoring device and methods of use
US9066695B2 (en) 1998-04-30 2015-06-30 Abbott Diabetes Care Inc. Analyte monitoring device and methods of use
US8346337B2 (en) 1998-04-30 2013-01-01 Abbott Diabetes Care Inc. Analyte monitoring device and methods of use
US6175752B1 (en) 1998-04-30 2001-01-16 Therasense, Inc. Analyte monitoring device and methods of use
US8688188B2 (en) 1998-04-30 2014-04-01 Abbott Diabetes Care Inc. Analyte monitoring device and methods of use
DK1144028T3 (en) * 1998-11-30 2004-10-18 Novo Nordisk As System to help a user during medical self treatment, said self treatment comprising a plurality of actions
US8641644B2 (en) 2000-11-21 2014-02-04 Sanofi-Aventis Deutschland Gmbh Blood testing apparatus having a rotatable cartridge with multiple lancing elements and testing means
US6560471B1 (en) 2001-01-02 2003-05-06 Therasense, Inc. Analyte monitoring device and methods of use
US7892183B2 (en) 2002-04-19 2011-02-22 Pelikan Technologies, Inc. Method and apparatus for body fluid sampling and analyte sensing
US8221334B2 (en) 2002-04-19 2012-07-17 Sanofi-Aventis Deutschland Gmbh Method and apparatus for penetrating tissue
US7909778B2 (en) 2002-04-19 2011-03-22 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US7041068B2 (en) 2001-06-12 2006-05-09 Pelikan Technologies, Inc. Sampling module device and method
US7226461B2 (en) 2002-04-19 2007-06-05 Pelikan Technologies, Inc. Method and apparatus for a multi-use body fluid sampling device with sterility barrier release
US8579831B2 (en) 2002-04-19 2013-11-12 Sanofi-Aventis Deutschland Gmbh Method and apparatus for penetrating tissue
US7547287B2 (en) 2002-04-19 2009-06-16 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US7976476B2 (en) 2002-04-19 2011-07-12 Pelikan Technologies, Inc. Device and method for variable speed lancet
EP1404235A4 (en) 2001-06-12 2008-08-20 Pelikan Technologies Inc Method and apparatus for lancet launching device integrated onto a blood-sampling cartridge
US7344507B2 (en) 2002-04-19 2008-03-18 Pelikan Technologies, Inc. Method and apparatus for lancet actuation
US7316700B2 (en) 2001-06-12 2008-01-08 Pelikan Technologies, Inc. Self optimizing lancing device with adaptation means to temporal variations in cutaneous properties
US8337419B2 (en) 2002-04-19 2012-12-25 Sanofi-Aventis Deutschland Gmbh Tissue penetration device
WO2002100460A3 (en) 2001-06-12 2003-05-08 Don Alden Electric lancet actuator
US7291117B2 (en) 2002-04-19 2007-11-06 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US7371247B2 (en) 2002-04-19 2008-05-13 Pelikan Technologies, Inc Method and apparatus for penetrating tissue
US9795334B2 (en) 2002-04-19 2017-10-24 Sanofi-Aventis Deutschland Gmbh Method and apparatus for penetrating tissue
US7981056B2 (en) 2002-04-19 2011-07-19 Pelikan Technologies, Inc. Methods and apparatus for lancet actuation
US7229458B2 (en) 2002-04-19 2007-06-12 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US7674232B2 (en) 2002-04-19 2010-03-09 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US7699791B2 (en) 2001-06-12 2010-04-20 Pelikan Technologies, Inc. Method and apparatus for improving success rate of blood yield from a fingerstick
US7491178B2 (en) 2002-04-19 2009-02-17 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US9427532B2 (en) 2001-06-12 2016-08-30 Sanofi-Aventis Deutschland Gmbh Tissue penetration device
US7331931B2 (en) 2002-04-19 2008-02-19 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US7717863B2 (en) 2002-04-19 2010-05-18 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US9226699B2 (en) 2002-04-19 2016-01-05 Sanofi-Aventis Deutschland Gmbh Body fluid sampling module with a continuous compression tissue interface surface
US9248267B2 (en) 2002-04-19 2016-02-02 Sanofi-Aventis Deustchland Gmbh Tissue penetration device
US7648468B2 (en) 2002-04-19 2010-01-19 Pelikon Technologies, Inc. Method and apparatus for penetrating tissue
US9314194B2 (en) 2002-04-19 2016-04-19 Sanofi-Aventis Deutschland Gmbh Tissue penetration device
US7232451B2 (en) 2002-04-19 2007-06-19 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US7297122B2 (en) 2002-04-19 2007-11-20 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US8267870B2 (en) 2002-04-19 2012-09-18 Sanofi-Aventis Deutschland Gmbh Method and apparatus for body fluid sampling with hybrid actuation
US8784335B2 (en) 2002-04-19 2014-07-22 Sanofi-Aventis Deutschland Gmbh Body fluid sampling device with a capacitive sensor
US7682318B2 (en) 2001-06-12 2010-03-23 Pelikan Technologies, Inc. Blood sampling apparatus and method
US7901362B2 (en) 2002-04-19 2011-03-08 Pelikan Technologies, Inc. Method and apparatus for penetrating tissue
US20040054263A1 (en) * 2001-08-20 2004-03-18 Piet Moerman Wireless diabetes management devices and methods for using the same
US9282925B2 (en) 2002-02-12 2016-03-15 Dexcom, Inc. Systems and methods for replacing signal artifacts in a glucose sensor data stream
US8702624B2 (en) 2006-09-29 2014-04-22 Sanofi-Aventis Deutschland Gmbh Analyte measurement device with a single shot actuator
US8423113B2 (en) 2003-07-25 2013-04-16 Dexcom, Inc. Systems and methods for processing sensor data
US8260393B2 (en) 2003-07-25 2012-09-04 Dexcom, Inc. Systems and methods for replacing signal data artifacts in a glucose sensor data stream
JP4289869B2 (en) * 2002-11-06 2009-07-01 シスメックス株式会社 Diabetes diagnosis support system
US8574895B2 (en) 2002-12-30 2013-11-05 Sanofi-Aventis Deutschland Gmbh Method and apparatus using optical techniques to measure analyte levels
US6949816B2 (en) 2003-04-21 2005-09-27 Motorola, Inc. Semiconductor component having first surface area for electrically coupling to a semiconductor chip and second surface area for electrically coupling to a substrate, and method of manufacturing same
US7850621B2 (en) 2003-06-06 2010-12-14 Pelikan Technologies, Inc. Method and apparatus for body fluid sampling and analyte sensing
US8066639B2 (en) 2003-06-10 2011-11-29 Abbott Diabetes Care Inc. Glucose measuring device for use in personal area network
US8275437B2 (en) 2003-08-01 2012-09-25 Dexcom, Inc. Transcutaneous analyte sensor
US8886272B2 (en) 2004-07-13 2014-11-11 Dexcom, Inc. Analyte sensor
US20100168542A1 (en) 2003-08-01 2010-07-01 Dexcom, Inc. System and methods for processing analyte sensor data
US8369919B2 (en) 2003-08-01 2013-02-05 Dexcom, Inc. Systems and methods for processing sensor data
US8845536B2 (en) 2003-08-01 2014-09-30 Dexcom, Inc. Transcutaneous analyte sensor
US7933639B2 (en) 2003-08-01 2011-04-26 Dexcom, Inc. System and methods for processing analyte sensor data
US8761856B2 (en) 2003-08-01 2014-06-24 Dexcom, Inc. System and methods for processing analyte sensor data
US8160669B2 (en) 2003-08-01 2012-04-17 Dexcom, Inc. Transcutaneous analyte sensor
US7774145B2 (en) 2003-08-01 2010-08-10 Dexcom, Inc. Transcutaneous analyte sensor
US20070208245A1 (en) * 2003-08-01 2007-09-06 Brauker James H Transcutaneous analyte sensor
US7778680B2 (en) 2003-08-01 2010-08-17 Dexcom, Inc. System and methods for processing analyte sensor data
US8233959B2 (en) 2003-08-22 2012-07-31 Dexcom, Inc. Systems and methods for processing analyte sensor data
US8010174B2 (en) 2003-08-22 2011-08-30 Dexcom, Inc. Systems and methods for replacing signal artifacts in a glucose sensor data stream
US9247901B2 (en) 2003-08-22 2016-02-02 Dexcom, Inc. Systems and methods for replacing signal artifacts in a glucose sensor data stream
WO2005033659A3 (en) 2003-09-29 2007-01-18 Pelikan Technologies Inc Method and apparatus for an improved sample capture device
WO2005037095A1 (en) 2003-10-14 2005-04-28 Pelikan Technologies, Inc. Method and apparatus for a variable user interface
JP2007512588A (en) * 2003-10-29 2007-05-17 ノボ・ノルデイスク・エー/エス Medical advisory system
US7299082B2 (en) 2003-10-31 2007-11-20 Abbott Diabetes Care, Inc. Method of calibrating an analyte-measurement device, and associated methods, devices and systems
US7519408B2 (en) 2003-11-19 2009-04-14 Dexcom, Inc. Integrated receiver for continuous analyte sensor
EP2239567B1 (en) 2003-12-05 2015-09-02 DexCom, Inc. Calibration techniques for a continuous analyte sensor
US8287453B2 (en) 2003-12-05 2012-10-16 Dexcom, Inc. Analyte sensor
EP1711791B1 (en) * 2003-12-09 2014-10-15 DexCom, Inc. Signal processing for continuous analyte sensor
WO2005065414A3 (en) 2003-12-31 2005-12-29 Pelikan Technologies Inc Method and apparatus for improving fluidic flow and sample capture
US8771183B2 (en) 2004-02-17 2014-07-08 Abbott Diabetes Care Inc. Method and system for providing data communication in continuous glucose monitoring and management system
US7591801B2 (en) 2004-02-26 2009-09-22 Dexcom, Inc. Integrated delivery device for continuous glucose sensor
CN100446719C (en) * 2004-02-26 2008-12-31 糖尿病工具瑞典股份公司 Metabolic monitoring, a method and apparatus for indicating a health-related condition of a subject
EP1751546A2 (en) 2004-05-20 2007-02-14 Albatros Technologies GmbH &amp; Co. KG Printable hydrogel for biosensors
EP1765194A4 (en) 2004-06-03 2010-09-29 Pelikan Technologies Inc Method and apparatus for a fluid sampling device
WO2006001797A1 (en) 2004-06-14 2006-01-05 Pelikan Technologies, Inc. Low pain penetrating
US7497827B2 (en) 2004-07-13 2009-03-03 Dexcom, Inc. Transcutaneous analyte sensor
US20060016700A1 (en) 2004-07-13 2006-01-26 Dexcom, Inc. Transcutaneous analyte sensor
US8565848B2 (en) * 2004-07-13 2013-10-22 Dexcom, Inc. Transcutaneous analyte sensor
US8452368B2 (en) 2004-07-13 2013-05-28 Dexcom, Inc. Transcutaneous analyte sensor
US8652831B2 (en) 2004-12-30 2014-02-18 Sanofi-Aventis Deutschland Gmbh Method and apparatus for analyte measurement test time
US7822454B1 (en) 2005-01-03 2010-10-26 Pelikan Technologies, Inc. Fluid sampling device with improved analyte detecting member configuration
EP1677226A1 (en) * 2005-01-04 2006-07-05 Giacomo Vespasiani Method and system for the management of data for a patient-controlled insulin therapy
US7798961B1 (en) 2005-01-11 2010-09-21 BeWell Mobile Technology Inc. Acquisition and management of time dependent health information
US7920906B2 (en) 2005-03-10 2011-04-05 Dexcom, Inc. System and methods for processing analyte sensor data for sensor calibration
US20090312620A1 (en) 2005-04-27 2009-12-17 Hou-Mei Henry Chang Diabetes monitor
US8112240B2 (en) 2005-04-29 2012-02-07 Abbott Diabetes Care Inc. Method and apparatus for providing leak detection in data monitoring and management systems
CA2607437A1 (en) * 2005-06-08 2006-12-14 Agamatrix, Inc. Data collection system and interface
US8251904B2 (en) 2005-06-09 2012-08-28 Roche Diagnostics Operations, Inc. Device and method for insulin dosing
US20090227855A1 (en) 2005-08-16 2009-09-10 Medtronic Minimed, Inc. Controller device for an infusion pump
US7737581B2 (en) * 2005-08-16 2010-06-15 Medtronic Minimed, Inc. Method and apparatus for predicting end of battery life
US9521968B2 (en) 2005-09-30 2016-12-20 Abbott Diabetes Care Inc. Analyte sensor retention mechanism and methods of use
US8880138B2 (en) 2005-09-30 2014-11-04 Abbott Diabetes Care Inc. Device for channeling fluid and methods of use
US7766829B2 (en) 2005-11-04 2010-08-03 Abbott Diabetes Care Inc. Method and system for providing basal profile modification in analyte monitoring and management systems
EP1955240B8 (en) * 2005-11-08 2016-03-30 Bigfoot Biomedical, Inc. Method for manual and autonomous control of an infusion pump
US7885698B2 (en) 2006-02-28 2011-02-08 Abbott Diabetes Care Inc. Method and system for providing continuous calibration of implantable analyte sensors
US7826879B2 (en) 2006-02-28 2010-11-02 Abbott Diabetes Care Inc. Analyte sensors and methods of use
US8029441B2 (en) 2006-02-28 2011-10-04 Abbott Diabetes Care Inc. Analyte sensor transmitter unit configuration for a data monitoring and management system
EP1839566A1 (en) * 2006-03-29 2007-10-03 F. Hoffmann-La Roche AG Method and assembly for the observation of a medical instrument.
US7620438B2 (en) 2006-03-31 2009-11-17 Abbott Diabetes Care Inc. Method and system for powering an electronic device
US8226891B2 (en) 2006-03-31 2012-07-24 Abbott Diabetes Care Inc. Analyte monitoring devices and methods therefor
JP5121822B2 (en) * 2006-05-08 2013-01-16 バイエル・ヘルスケア・エルエルシー Abnormal output detection system for a biosensor
US20080071157A1 (en) 2006-06-07 2008-03-20 Abbott Diabetes Care, Inc. Analyte monitoring system and method
CA2656074C (en) * 2006-07-19 2018-02-20 Cross Technology Solutions Ab Mobile apparatus, method and system for processing blood sugar affecting factors
US7653425B2 (en) 2006-08-09 2010-01-26 Abbott Diabetes Care Inc. Method and system for providing calibration of an analyte sensor in an analyte monitoring system
US7618369B2 (en) * 2006-10-02 2009-11-17 Abbott Diabetes Care Inc. Method and system for dynamically updating calibration parameters for an analyte sensor
US8364231B2 (en) 2006-10-04 2013-01-29 Dexcom, Inc. Analyte sensor
US7630748B2 (en) 2006-10-25 2009-12-08 Abbott Diabetes Care Inc. Method and system for providing analyte monitoring
JP2010508091A (en) 2006-10-26 2010-03-18 アボット ダイアベティス ケア インコーポレイテッドAbbott Diabetes Care Inc. The method for detecting a decrease sensitivity of the analyte sensor in real time systems, and computer program products
US8439837B2 (en) * 2006-10-31 2013-05-14 Lifescan, Inc. Systems and methods for detecting hypoglycemic events having a reduced incidence of false alarms
US20080306353A1 (en) * 2006-11-03 2008-12-11 Douglas Joel S Calculation device for metabolic control of critically ill and/or diabetic patients
US8079955B2 (en) * 2006-11-28 2011-12-20 Isense Corporation Method and apparatus for managing glucose control
US8121857B2 (en) 2007-02-15 2012-02-21 Abbott Diabetes Care Inc. Device and method for automatic data acquisition and/or detection
US8930203B2 (en) 2007-02-18 2015-01-06 Abbott Diabetes Care Inc. Multi-function analyte test device and methods therefor
US8732188B2 (en) 2007-02-18 2014-05-20 Abbott Diabetes Care Inc. Method and system for providing contextual based medication dosage determination
US9636450B2 (en) 2007-02-19 2017-05-02 Udo Hoss Pump system modular components for delivering medication and analyte sensing at seperate insertion sites
US8123686B2 (en) 2007-03-01 2012-02-28 Abbott Diabetes Care Inc. Method and apparatus for providing rolling data in communication systems
EP2137637A4 (en) 2007-04-14 2012-06-20 Abbott Diabetes Care Inc Method and apparatus for providing data processing and control in medical communication system
US7768387B2 (en) 2007-04-14 2010-08-03 Abbott Diabetes Care Inc. Method and apparatus for providing dynamic multi-stage signal amplification in a medical device
CA2683959C (en) 2007-04-14 2017-08-29 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in medical communication system
US9008743B2 (en) 2007-04-14 2015-04-14 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in medical communication system
US9204827B2 (en) 2007-04-14 2015-12-08 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in medical communication system
US7928850B2 (en) 2007-05-08 2011-04-19 Abbott Diabetes Care Inc. Analyte monitoring system and methods
US8461985B2 (en) 2007-05-08 2013-06-11 Abbott Diabetes Care Inc. Analyte monitoring system and methods
US8665091B2 (en) 2007-05-08 2014-03-04 Abbott Diabetes Care Inc. Method and device for determining elapsed sensor life
US8456301B2 (en) 2007-05-08 2013-06-04 Abbott Diabetes Care Inc. Analyte monitoring system and methods
US8103471B2 (en) 2007-05-14 2012-01-24 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in a medical communication system
US8239166B2 (en) 2007-05-14 2012-08-07 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in a medical communication system
US8560038B2 (en) 2007-05-14 2013-10-15 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in a medical communication system
US8600681B2 (en) 2007-05-14 2013-12-03 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in a medical communication system
US8444560B2 (en) 2007-05-14 2013-05-21 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in a medical communication system
US8140312B2 (en) 2007-05-14 2012-03-20 Abbott Diabetes Care Inc. Method and system for determining analyte levels
US8260558B2 (en) 2007-05-14 2012-09-04 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in a medical communication system
US7996158B2 (en) 2007-05-14 2011-08-09 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in a medical communication system
US9125548B2 (en) 2007-05-14 2015-09-08 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in a medical communication system
WO2008144616A1 (en) * 2007-05-18 2008-11-27 Heidi Kay Lipid raft, caveolin protein, and caveolar function modulation compounds and associated synthetic and therapeutic methods
US8597190B2 (en) 2007-05-18 2013-12-03 Optiscan Biomedical Corporation Monitoring systems and methods with fast initialization
EP2152350A4 (en) 2007-06-08 2013-03-27 Dexcom Inc Integrated medicament delivery device for use with continuous analyte sensor
US20080311968A1 (en) * 2007-06-13 2008-12-18 Hunter Thomas C Method for improving self-management of a disease
EP2006786A1 (en) * 2007-06-18 2008-12-24 Boehringer Mannheim Gmbh Method and glucose monitoring system for monitoring individual metabolic response and for generating nutritional feedback
CA2690870C (en) 2007-06-21 2017-07-11 Abbott Diabetes Care Inc. Health monitor
US8597188B2 (en) 2007-06-21 2013-12-03 Abbott Diabetes Care Inc. Health management devices and methods
EP2170181B1 (en) 2007-06-22 2014-04-16 Ekos Corporation Method and apparatus for treatment of intracranial hemorrhages
US8834366B2 (en) 2007-07-31 2014-09-16 Abbott Diabetes Care Inc. Method and apparatus for providing analyte sensor calibration
US7768386B2 (en) 2007-07-31 2010-08-03 Abbott Diabetes Care Inc. Method and apparatus for providing data processing and control in a medical communication system
US20090143725A1 (en) * 2007-08-31 2009-06-04 Abbott Diabetes Care, Inc. Method of Optimizing Efficacy of Therapeutic Agent
US7731659B2 (en) * 2007-10-18 2010-06-08 Lifescan Scotland Limited Method for predicting a user's future glycemic state
US7695434B2 (en) * 2007-10-19 2010-04-13 Lifescan Scotland, Ltd. Medical device for predicting a user's future glycemic state
US8374668B1 (en) 2007-10-23 2013-02-12 Abbott Diabetes Care Inc. Analyte sensor with lag compensation
US8377031B2 (en) 2007-10-23 2013-02-19 Abbott Diabetes Care Inc. Closed loop control system with safety parameters and methods
US8409093B2 (en) 2007-10-23 2013-04-02 Abbott Diabetes Care Inc. Assessing measures of glycemic variability
US8216138B1 (en) 2007-10-23 2012-07-10 Abbott Diabetes Care Inc. Correlation of alternative site blood and interstitial fluid glucose concentrations to venous glucose concentration
US8417312B2 (en) 2007-10-25 2013-04-09 Dexcom, Inc. Systems and methods for processing sensor data
US9135402B2 (en) * 2007-12-17 2015-09-15 Dexcom, Inc. Systems and methods for processing sensor data
US9839395B2 (en) 2007-12-17 2017-12-12 Dexcom, Inc. Systems and methods for processing sensor data
US8473022B2 (en) 2008-01-31 2013-06-25 Abbott Diabetes Care Inc. Analyte sensor with time lag compensation
US20100145173A1 (en) * 2008-02-12 2010-06-10 Alferness Clifton A System and method for creating a personalized tool predicting a time course of blood glucose affect in diabetes mellitus
US20100138453A1 (en) * 2008-02-12 2010-06-03 Alferness Clifton A System and method for generating a personalized diabetes management tool for diabetes mellitus
US20100145670A1 (en) * 2008-02-12 2010-06-10 Alferness Clifton A System and method for managing type 2 diabetes mellitus through a personal predictive management tool
US20100138203A1 (en) * 2008-02-12 2010-06-03 Alferness Clifton A System and method for actively managing type 2 diabetes mellitus on a personalized basis
US20100198020A1 (en) * 2008-02-12 2010-08-05 Alferness Clifton A System And Method For Computer-Implemented Method For Actively Managing Increased Insulin Resistance In Type 2 Diabetes Mellitus
US20100198021A1 (en) * 2008-02-12 2010-08-05 Alferness Clifton A Computer-implemented method for providing a tunable personalized tool for estimating glycated hemoglobin
US20100137786A1 (en) * 2008-02-12 2010-06-03 Alferness Clifton A System and method for actively managing type 1 diabetes mellitus on a personalized basis
US20110077930A1 (en) * 2008-02-12 2011-03-31 Alferness Clifton A Computer-implemented method for providing a personalized tool for estimating 1,5-anhydroglucitol
US20100145725A1 (en) * 2008-02-12 2010-06-10 Alferness Clifton A System and method for managing type 1 diabetes mellitus through a personal predictive management tool
CA2715628A1 (en) 2008-02-21 2009-08-27 Dexcom, Inc. Systems and methods for processing, transmitting and displaying sensor data
US8396528B2 (en) 2008-03-25 2013-03-12 Dexcom, Inc. Analyte sensor
US8346335B2 (en) 2008-03-28 2013-01-01 Abbott Diabetes Care Inc. Analyte sensor calibration management
US8583205B2 (en) 2008-03-28 2013-11-12 Abbott Diabetes Care Inc. Analyte sensor calibration management
US8457901B2 (en) * 2008-04-04 2013-06-04 Hygieia, Inc. System for optimizing a patient's insulin dosage regimen
US9220456B2 (en) 2008-04-04 2015-12-29 Hygieia, Inc. Systems, methods and devices for achieving glycemic balance
WO2009126900A1 (en) 2008-04-11 2009-10-15 Pelikan Technologies, Inc. Method and apparatus for analyte detecting device
CN101261276B (en) 2008-04-17 2012-04-18 北京软测科技有限公司 Diabetes monitoring and diagnosis device
US7826382B2 (en) 2008-05-30 2010-11-02 Abbott Diabetes Care Inc. Close proximity communication device and methods
WO2010019919A1 (en) * 2008-08-14 2010-02-18 University Of Toledo Multifunctional neural network system and uses thereof for glycemic forecasting
US9392969B2 (en) 2008-08-31 2016-07-19 Abbott Diabetes Care Inc. Closed loop control and signal attenuation detection
US20100057040A1 (en) 2008-08-31 2010-03-04 Abbott Diabetes Care, Inc. Robust Closed Loop Control And Methods
US8622988B2 (en) * 2008-08-31 2014-01-07 Abbott Diabetes Care Inc. Variable rate closed loop control and methods
US8734422B2 (en) 2008-08-31 2014-05-27 Abbott Diabetes Care Inc. Closed loop control with improved alarm functions
US8219173B2 (en) 2008-09-30 2012-07-10 Abbott Diabetes Care Inc. Optimizing analyte sensor calibration
US8986208B2 (en) 2008-09-30 2015-03-24 Abbott Diabetes Care Inc. Analyte sensor sensitivity attenuation mitigation
US9326707B2 (en) 2008-11-10 2016-05-03 Abbott Diabetes Care Inc. Alarm characterization for analyte monitoring devices and systems
US8992464B2 (en) 2008-11-11 2015-03-31 Hygieia, Inc. Apparatus and system for diabetes management
US8103456B2 (en) 2009-01-29 2012-01-24 Abbott Diabetes Care Inc. Method and device for early signal attenuation detection using blood glucose measurements
US8224415B2 (en) 2009-01-29 2012-07-17 Abbott Diabetes Care Inc. Method and device for providing offset model based calibration for analyte sensor
US9375169B2 (en) 2009-01-30 2016-06-28 Sanofi-Aventis Deutschland Gmbh Cam drive for managing disposable penetrating member actions with a single motor and motor and control system
EP2399205A4 (en) 2009-02-25 2013-10-16 Univ Virginia Patent Found Cgm-based prevention of hypoglycemia via hypoglycemia risk assessment and smooth reduction insulin delivery
US8992493B2 (en) * 2009-03-13 2015-03-31 Atrium Medical Corporation Chest drainage systems and methods
WO2010111660A1 (en) 2009-03-27 2010-09-30 Dexcom, Inc. Methods and systems for promoting glucose management
WO2010121084A1 (en) 2009-04-15 2010-10-21 Abbott Diabetes Care Inc. Analyte monitoring system having an alert
WO2010127050A1 (en) 2009-04-28 2010-11-04 Abbott Diabetes Care Inc. Error detection in critical repeating data in a wireless sensor system
EP2425209A4 (en) 2009-04-29 2013-01-09 Abbott Diabetes Care Inc Method and system for providing real time analyte sensor calibration with retrospective backfill
US8368556B2 (en) 2009-04-29 2013-02-05 Abbott Diabetes Care Inc. Method and system for providing data communication in continuous glucose monitoring and management system
US8168396B2 (en) * 2009-05-11 2012-05-01 Diabetomics, Llc Methods for detecting pre-diabetes and diabetes using differential protein glycosylation
US20100330598A1 (en) * 2009-06-26 2010-12-30 Roche Diagnostics Operations, Inc. METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR PROVIDING BOTH AN ESTIMATED TRUE MEAN BLOOD GLUCOSE VALUE AND ESTIMATED GLYCATED HEMOGLOBIN (HbA1C) VALUE FROM STRUCTURED SPOT MEASUREMENTS OF BLOOD GLUCOSE
RU2552312C2 (en) * 2009-06-30 2015-06-10 Лайфскэн Скотлэнд Лимитед Systems and methods for diabetes control
EP2456351B1 (en) 2009-07-23 2016-10-12 Abbott Diabetes Care, Inc. Real time management of data relating to physiological control of glucose levels
CN104799866A (en) 2009-07-23 2015-07-29 雅培糖尿病护理公司 The analyte monitoring device
US8478557B2 (en) 2009-07-31 2013-07-02 Abbott Diabetes Care Inc. Method and apparatus for providing analyte monitoring system calibration accuracy
JP2013501558A (en) 2009-08-10 2013-01-17 ディアベテス トールス スウェーデン アーべーDiabetes Tools Sweden Ab Apparatus and method for processing a set of data values
EP2473099A4 (en) 2009-08-31 2015-01-14 Abbott Diabetes Care Inc Analyte monitoring system and methods for managing power and noise
EP2473098A4 (en) 2009-08-31 2014-04-09 Abbott Diabetes Care Inc Analyte signal processing device and methods
EP2473422A4 (en) 2009-08-31 2014-09-17 Abbott Diabetes Care Inc Displays for a medical device
EP2482720A4 (en) 2009-09-29 2014-04-23 Abbott Diabetes Care Inc Method and apparatus for providing notification function in analyte monitoring systems
US20110092788A1 (en) * 2009-10-15 2011-04-21 Roche Diagnostics Operations, Inc. Systems And Methods For Providing Guidance In Administration Of A Medicine
WO2011053881A1 (en) 2009-10-30 2011-05-05 Abbott Diabetes Care Inc. Method and apparatus for detecting false hypoglycemic conditions
US8803688B2 (en) * 2010-01-07 2014-08-12 Lisa Halff System and method responsive to an event detected at a glucose monitoring device
US20110163880A1 (en) * 2010-01-07 2011-07-07 Lisa Halff System and method responsive to an alarm event detected at an insulin delivery device
US20110184265A1 (en) * 2010-01-22 2011-07-28 Abbott Diabetes Care Inc. Method and Apparatus for Providing Notification in Analyte Monitoring Systems
US9326709B2 (en) * 2010-03-10 2016-05-03 Abbott Diabetes Care Inc. Systems, devices and methods for managing glucose levels
CA2792758A1 (en) * 2010-03-11 2011-09-15 University Of Virginia Patent Foundation Method and system for the safety, analysis and supervision of insulin pump action and other modes of insulin delivery in diabetes
KR101100987B1 (en) * 2010-03-23 2011-12-30 삼성모바일디스플레이주식회사 Touch Screen Panel
US9398869B2 (en) * 2010-03-26 2016-07-26 University Of Virginia Patent Foundation Method, system, and computer program product for improving the accuracy of glucose sensors using insulin delivery observation in diabetes
US8965476B2 (en) 2010-04-16 2015-02-24 Sanofi-Aventis Deutschland Gmbh Tissue penetration device
US9795747B2 (en) 2010-06-02 2017-10-24 Sanofi-Aventis Deutschland Gmbh Methods and apparatus for lancet actuation
US8635046B2 (en) 2010-06-23 2014-01-21 Abbott Diabetes Care Inc. Method and system for evaluating analyte sensor response characteristics
EP2491859A1 (en) * 2011-02-23 2012-08-29 F. Hoffmann-La Roche AG Method and system for determining blood glucose characteristics from a discontinuous mode of measurement and computer program product
CN107019515A (en) 2011-02-28 2017-08-08 雅培糖尿病护理公司 Devices, systems, and methods associated with analyte monitoring devices and devices incorporating the same
KR20120116581A (en) * 2011-04-13 2012-10-23 주식회사 필로시스 Method of controlling momentum by using blood glucose test meter having walking counter function
EP2700031A2 (en) * 2011-04-20 2014-02-26 Novo Nordisk A/S Glucose predictor based on regularization networks with adaptively chosen kernels and regularization parameters
JP5997453B2 (en) 2011-04-25 2016-09-28 アークレイ株式会社 The information processing apparatus and a user terminal
US20150018633A1 (en) * 2011-06-23 2015-01-15 University Of Virginia Patent Foundation Unified Platform for Monitoring and Control of Blood Glucose Levels in Diabetic Patients
US20130035575A1 (en) * 2011-08-05 2013-02-07 Dexcom, Inc. Systems and methods for detecting glucose level data patterns
CN106326651A (en) * 2011-08-26 2017-01-11 弗吉尼亚大学专利基金会 Method and system for adaptive advisory control of diabetes
WO2013066873A1 (en) 2011-10-31 2013-05-10 Abbott Diabetes Care Inc. Electronic devices having integrated reset systems and methods thereof
WO2013066849A1 (en) 2011-10-31 2013-05-10 Abbott Diabetes Care Inc. Model based variable risk false glucose threshold alarm prevention mechanism
US8710993B2 (en) 2011-11-23 2014-04-29 Abbott Diabetes Care Inc. Mitigating single point failure of devices in an analyte monitoring system and methods thereof
US9317656B2 (en) 2011-11-23 2016-04-19 Abbott Diabetes Care Inc. Compatibility mechanisms for devices in a continuous analyte monitoring system and methods thereof
US9339217B2 (en) 2011-11-25 2016-05-17 Abbott Diabetes Care Inc. Analyte monitoring system and methods of use
US20140030748A1 (en) * 2012-07-27 2014-01-30 Lifescan, Inc. Method and system to manage diabetes using multiple risk indicators for a person with diabetes
USD683151S1 (en) 2012-09-20 2013-05-28 Steelcase Inc. Chair
USD699061S1 (en) 2012-09-20 2014-02-11 Steelcase Inc. Arm assembly
USD694538S1 (en) 2012-09-20 2013-12-03 Steelcase Inc. Chair
US8998339B2 (en) 2012-09-20 2015-04-07 Steelcase Inc. Chair assembly with upholstery covering
USD697726S1 (en) 2012-09-20 2014-01-21 Steelcase Inc. Chair
USD694537S1 (en) 2012-09-20 2013-12-03 Steelcase Inc. Chair
USD697729S1 (en) 2012-09-20 2014-01-21 Steelcase Inc. Chair
USD694539S1 (en) 2012-09-20 2013-12-03 Steelcase Inc. Chair
USD688907S1 (en) 2012-09-20 2013-09-03 Steelcase Inc. Arm assembly
USD697727S1 (en) 2012-09-20 2014-01-21 Steeelcase Inc. Chair
US9907492B2 (en) 2012-09-26 2018-03-06 Abbott Diabetes Care Inc. Method and apparatus for improving lag correction during in vivo measurement of analyte concentration with analyte concentration variability and range data
US20140100435A1 (en) * 2012-10-04 2014-04-10 Roche Diagnostics Operations, Inc. System and method for assessing risk associated with a glucose state
US9675290B2 (en) 2012-10-30 2017-06-13 Abbott Diabetes Care Inc. Sensitivity calibration of in vivo sensors used to measure analyte concentration
JP5511033B1 (en) * 2012-12-04 2014-06-04 Necシステムテクノロジー株式会社 Blood glucose level predicting device, measuring device, the blood glucose level predicting method, and program
US20160004813A1 (en) * 2013-02-21 2016-01-07 University Of Virginia Patent Foundation Method and system for model-based tracking of changes in average glycemia in diabetes
US20160030725A1 (en) 2013-03-14 2016-02-04 Ekos Corporation Method and apparatus for treatment of intracranial hemorrhages
US9474475B1 (en) 2013-03-15 2016-10-25 Abbott Diabetes Care Inc. Multi-rate analyte sensor data collection with sample rate configurable signal processing
US20150095042A1 (en) * 2013-09-27 2015-04-02 Roche Diagnostics Operations, Inc. High/low blood glucose risk assessment systems and methods
US9233204B2 (en) 2014-01-31 2016-01-12 Aseko, Inc. Insulin management
US9486580B2 (en) 2014-01-31 2016-11-08 Aseko, Inc. Insulin management
US9892234B2 (en) 2014-10-27 2018-02-13 Aseko, Inc. Subcutaneous outpatient management
US9886556B2 (en) 2015-08-20 2018-02-06 Aseko, Inc. Diabetes management therapy advisor

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5971922A (en) 1998-04-07 1999-10-26 Meidensha Electric Mfg Co Ltd System and method for predicting blood glucose level

Family Cites Families (107)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB8413830D0 (en) * 1984-05-31 1984-07-04 Seltronix Ltd Blood glucose monitor
JPS6125525A (en) * 1984-07-13 1986-02-04 Sumitomo Electric Industries Patient monitor apparatus
US4695954A (en) * 1984-10-31 1987-09-22 Rose Robert J Modular medication dispensing system and apparatus utilizing portable memory device
US5206144A (en) * 1985-03-29 1993-04-27 Novo Industri A/S Determination of glycated (glycosylated) hemoglobin in blood
US4731726A (en) 1986-05-19 1988-03-15 Healthware Corporation Patient-operated glucose monitor and diabetes management system
EP0290683A3 (en) 1987-05-01 1988-12-14 Diva Medical Systems B.V. Diabetes management system and apparatus
US5216597A (en) * 1987-05-01 1993-06-01 Diva Medical Systems Bv Diabetes therapy management system, apparatus and method
US4817044A (en) * 1987-06-01 1989-03-28 Ogren David A Collection and reporting system for medical appliances
CA1338348C (en) 1987-11-30 1996-05-28 Kazutoshi Yamazaki Eliminating agent for glycosylated hemoglobin
US5025374A (en) * 1987-12-09 1991-06-18 Arch Development Corp. Portable system for choosing pre-operative patient test
JP2907342B2 (en) 1988-01-29 1999-06-21 ザ リージェンツ オブ ザ ユニバーシティー オブ カリフォルニア Ion Infiltration noninvasive sampling or delivery device
US5128015A (en) 1988-03-15 1992-07-07 Tall Oak Ventures Method and apparatus for amperometric diagnostic analysis
US5108564A (en) 1988-03-15 1992-04-28 Tall Oak Ventures Method and apparatus for amperometric diagnostic analysis
US5076273A (en) 1988-09-08 1991-12-31 Sudor Partners Method and apparatus for determination of chemical species in body fluid
US5086229A (en) 1989-01-19 1992-02-04 Futrex, Inc. Non-invasive measurement of blood glucose
US5139023A (en) 1989-06-02 1992-08-18 Theratech Inc. Apparatus and method for noninvasive blood glucose monitoring
US4975581A (en) 1989-06-21 1990-12-04 University Of New Mexico Method of and apparatus for determining the similarity of a biological analyte from a model constructed from known biological fluids
CA2028261C (en) 1989-10-28 1995-01-17 Won Suck Yang Non-invasive method and apparatus for measuring blood glucose concentration
US5140985A (en) 1989-12-11 1992-08-25 Schroeder Jon M Noninvasive blood glucose measuring device
US5036861A (en) 1990-01-11 1991-08-06 Sembrowich Walter L Method and apparatus for non-invasively monitoring plasma glucose levels
CA2043198C (en) 1990-06-20 1999-09-21 Stephen Bussman Portable electronic logbook and method of storing and displaying data
JPH06501858A (en) * 1990-08-31 1994-03-03
US5251126A (en) * 1990-10-29 1993-10-05 Miles Inc. Diabetes data analysis and interpretation method
US5376070A (en) * 1992-09-29 1994-12-27 Minimed Inc. Data transfer system for an infusion pump
US5307263A (en) * 1992-11-17 1994-04-26 Raya Systems, Inc. Modular microprocessor-based health monitoring system
US5960403A (en) * 1992-11-17 1999-09-28 Health Hero Network Health management process control system
US5590648A (en) * 1992-11-30 1997-01-07 Tremont Medical Personal health care system
US5261126A (en) * 1992-12-02 1993-11-16 Japanic Corporation Raw sewage disposal apparatus
FI95427C (en) * 1992-12-23 1996-01-25 Instrumentarium Oy A data transmission system
US5558638A (en) 1993-04-30 1996-09-24 Healthdyne, Inc. Patient monitor and support system
US6022315A (en) * 1993-12-29 2000-02-08 First Opinion Corporation Computerized medical diagnostic and treatment advice system including network access
US6206829B1 (en) * 1996-07-12 2001-03-27 First Opinion Corporation Computerized medical diagnostic and treatment advice system including network access
KR0142483B1 (en) 1994-02-28 1998-08-17 고지마 게이지 Non-linear time sequential data predicting device
US5536249A (en) * 1994-03-09 1996-07-16 Visionary Medical Products, Inc. Pen-type injector with a microprocessor and blood characteristic monitor
US5704366A (en) * 1994-05-23 1998-01-06 Enact Health Management Systems System for monitoring and reporting medical measurements
EP1016433A1 (en) 1994-06-24 2000-07-05 Cygnus, Inc. Iontophoteric sampling device and method
US5431793A (en) 1994-07-29 1995-07-11 Beckman Instruments, Inc. Quantitative analysis of glycosylated hemoglobin by immunocappillary electrophoresis
JP3150857B2 (en) * 1994-10-19 2001-03-26 富士写真フイルム株式会社 Analytical element and analytical method for glycated hemoglobin content ratio measurement
US5946659A (en) * 1995-02-28 1999-08-31 Clinicomp International, Inc. System and method for notification and access of patient care information being simultaneously entered
US5713856A (en) * 1995-03-13 1998-02-03 Alaris Medical Systems, Inc. Modular patient care system
EP0846296A4 (en) 1995-03-31 1998-06-17
US5695949A (en) 1995-04-07 1997-12-09 Lxn Corp. Combined assay for current glucose level and intermediate or long-term glycemic control
US6671563B1 (en) * 1995-05-15 2003-12-30 Alaris Medical Systems, Inc. System and method for collecting data and managing patient care
US5989409A (en) 1995-09-11 1999-11-23 Cygnus, Inc. Method for glucose sensing
US5741211A (en) 1995-10-26 1998-04-21 Medtronic, Inc. System and method for continuous monitoring of diabetes-related blood constituents
FI118509B (en) 1996-02-12 2007-12-14 Nokia Oyj A method and apparatus for predicting a patient's blood glucose concentration
FI960636A (en) * 1996-02-12 1997-08-13 Nokia Mobile Phones Ltd A method for monitoring the patient's state of health
US5974389A (en) * 1996-03-01 1999-10-26 Clark; Melanie Ann Medical record management system and process with improved workflow features
US5801057A (en) 1996-03-22 1998-09-01 Smart; Wilson H. Microsampling device and method of construction
US5878384A (en) * 1996-03-29 1999-03-02 At&T Corp System and method for monitoring information flow and performing data collection
US5822935A (en) 1996-12-19 1998-10-20 Steelcase Inc. Solid-core wall system
US5956501A (en) 1997-01-10 1999-09-21 Health Hero Network, Inc. Disease simulation system and method
US5959529A (en) * 1997-03-07 1999-09-28 Kail, Iv; Karl A. Reprogrammable remote sensor monitoring system
WO1998040835A1 (en) * 1997-03-13 1998-09-17 First Opinion Corporation Disease management system
US6270455B1 (en) * 1997-03-28 2001-08-07 Health Hero Network, Inc. Networked system for interactive communications and remote monitoring of drug delivery
US5997476A (en) * 1997-03-28 1999-12-07 Health Hero Network, Inc. Networked system for interactive communication and remote monitoring of individuals
FI112545B (en) * 1997-05-30 2003-12-15 Nokia Corp The method and system of the patient's blood to predict the glycosylated hemoglobin component level
US5997475A (en) * 1997-08-18 1999-12-07 Solefound, Inc. Device for diabetes management
US6054039A (en) 1997-08-18 2000-04-25 Shieh; Paul Determination of glycoprotein and glycosylated hemoglobin in blood
EP0910023A2 (en) 1997-10-17 1999-04-21 Siemens Aktiengesellschaft Method and device for the neuronal modelling of a dynamic system with non-linear stochastic behavior
US6144922A (en) * 1997-10-31 2000-11-07 Mercury Diagnostics, Incorporated Analyte concentration information collection and communication system
JP2001521804A (en) * 1997-10-31 2001-11-13 アミラ メディカル Acquisition and communication system of analyte concentration information
US6049764A (en) * 1997-11-12 2000-04-11 City Of Hope Method and system for real-time control of analytical and diagnostic instruments
US6579690B1 (en) 1997-12-05 2003-06-17 Therasense, Inc. Blood analyte monitoring through subcutaneous measurement
US6024699A (en) * 1998-03-13 2000-02-15 Healthware Corporation Systems, methods and computer program products for monitoring, diagnosing and treating medical conditions of remotely located patients
EP1171823A4 (en) * 1999-03-03 2006-10-04 Cyrano Sciences Inc Apparatus, systems and methods for detecting and transmitting sensory data over a computer network
US6579231B1 (en) * 1998-03-27 2003-06-17 Mci Communications Corporation Personal medical monitoring unit and system
CA2326579C (en) 1998-04-03 2011-01-18 Triangle Pharmaceuticals, Inc. Systems, methods and computer program products for guiding the selection of therapeutic treatment regimens
US6175752B1 (en) 1998-04-30 2001-01-16 Therasense, Inc. Analyte monitoring device and methods of use
US8974386B2 (en) * 1998-04-30 2015-03-10 Abbott Diabetes Care Inc. Analyte monitoring device and methods of use
DE69910003D1 (en) 1998-05-13 2003-09-04 Cygnus Therapeutic Systems Monitoring physiological analytes
DE69914319D1 (en) 1998-05-13 2004-02-26 Cygnus Therapeutic Systems Signal processing for measurement of physiological analytes
GB9812432D0 (en) * 1998-06-09 1998-08-05 Queen Mary & Westfield College Predictive test
US7384396B2 (en) * 1998-07-21 2008-06-10 Spectrx Inc. System and method for continuous analyte monitoring
US6554798B1 (en) * 1998-08-18 2003-04-29 Medtronic Minimed, Inc. External infusion device with remote programming, bolus estimator and/or vibration alarm capabilities
US6338713B1 (en) * 1998-08-18 2002-01-15 Aspect Medical Systems, Inc. System and method for facilitating clinical decision making
JP2000060803A (en) * 1998-08-21 2000-02-29 Terumo Corp Blood sugar level information processing system
EP1102559B1 (en) 1998-09-30 2003-06-04 Cygnus, Inc. Method and device for predicting physiological values
US6201980B1 (en) 1998-10-05 2001-03-13 The Regents Of The University Of California Implantable medical sensor system
DK1144028T3 (en) * 1998-11-30 2004-10-18 Novo Nordisk As System to help a user during medical self treatment, said self treatment comprising a plurality of actions
US6540672B1 (en) * 1998-12-09 2003-04-01 Novo Nordisk A/S Medical system and a method of controlling the system for use by a patient for medical self treatment
EP1237463B1 (en) * 1999-03-29 2008-05-14 Beckman Coulter, Inc. Meter with integrated database and simplified telemedicine capability
US6336900B1 (en) * 1999-04-12 2002-01-08 Agilent Technologies, Inc. Home hub for reporting patient health parameters
US6558351B1 (en) * 1999-06-03 2003-05-06 Medtronic Minimed, Inc. Closed loop system for controlling insulin infusion
US6277071B1 (en) * 1999-06-25 2001-08-21 Delphi Health Systems, Inc. Chronic disease monitor
US6804558B2 (en) * 1999-07-07 2004-10-12 Medtronic, Inc. System and method of communicating between an implantable medical device and a remote computer system or health care provider
US6611846B1 (en) * 1999-10-30 2003-08-26 Medtamic Holdings Method and system for medical patient data analysis
US6406426B1 (en) * 1999-11-03 2002-06-18 Criticare Systems Medical monitoring and alert system for use with therapeutic devices
US6418346B1 (en) * 1999-12-14 2002-07-09 Medtronic, Inc. Apparatus and method for remote therapy and diagnosis in medical devices via interface systems
US6692436B1 (en) * 2000-04-14 2004-02-17 Computerized Screening, Inc. Health care information system
DE60144299D1 (en) * 2000-05-19 2011-05-05 Welch Allyn Protocol Inc A device for monitoring of patients
CA2349021C (en) * 2000-06-16 2010-03-30 Bayer Corporation System, method and biosensor apparatus for data communications with a personal data assistant
US6635016B2 (en) * 2000-08-21 2003-10-21 Joseph Finkelstein Method and system for collecting and processing of biomedical information
US6450956B1 (en) * 2000-11-06 2002-09-17 Siemens Corporate Research, Inc. System and method for treatment and outcome measurement analysis
US6524240B1 (en) * 2000-11-22 2003-02-25 Medwave, Inc. Docking station for portable medical devices
US6645142B2 (en) * 2000-12-01 2003-11-11 Optiscan Biomedical Corporation Glucose monitoring instrument having network connectivity
US6799149B2 (en) * 2000-12-29 2004-09-28 Medtronic, Inc. Therapy management techniques for an implantable medical device
US6551243B2 (en) * 2001-01-24 2003-04-22 Siemens Medical Solutions Health Services Corporation System and user interface for use in providing medical information and health care delivery support
US20060106644A1 (en) * 2001-05-30 2006-05-18 Koo Charles C Patient referral and physician-to-physician marketing method and system
US6544212B2 (en) * 2001-07-31 2003-04-08 Roche Diagnostics Corporation Diabetes management system
US6781522B2 (en) * 2001-08-22 2004-08-24 Kivalo, Inc. Portable storage case for housing a medical monitoring device and an associated method for communicating therewith
US20030216628A1 (en) * 2002-01-28 2003-11-20 Bortz Jonathan David Methods and systems for assessing glycemic control using predetermined pattern label analysis of blood glucose readings
US20050187789A1 (en) * 2004-02-25 2005-08-25 Cardiac Pacemakers, Inc. Advanced patient and medication therapy management system and method
WO2005119555A2 (en) * 2004-06-01 2005-12-15 Lifescan, Inc. Methods and systems of automating medical device data management
WO2005119524A3 (en) * 2004-06-04 2007-04-19 Therasense Inc Diabetes care host-client architecture and data management system
US20060173260A1 (en) * 2005-01-31 2006-08-03 Gmms Ltd System, device and method for diabetes treatment and monitoring
US20070033074A1 (en) * 2005-06-03 2007-02-08 Medtronic Minimed, Inc. Therapy management system

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5971922A (en) 1998-04-07 1999-10-26 Meidensha Electric Mfg Co Ltd System and method for predicting blood glucose level

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP1267708A4

Cited By (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003065033A3 (en) * 2002-01-28 2004-02-05 Control Diabetes Inc Methods and systems for assessing glycemic control using predetermined pattern label analysis of blood glucose readings
WO2003065033A2 (en) * 2002-01-28 2003-08-07 Control Diabetes, Inc. Methods and systems for assessing glycemic control using predetermined pattern label analysis of blood glucose readings
EP2322092A1 (en) 2002-08-13 2011-05-18 University Of Virginia Patent Foundation Method, system, and computer program product for processing of self-monitoring blood glucose (smbg) data to enhance diabetic self-management
WO2004015539A2 (en) 2002-08-13 2004-02-19 University Of Virginia Patent Foundation Managing and processing self-monitoring blood glucose
EP1534121A4 (en) * 2002-08-13 2008-12-10 Univ Virginia Method, system, and computer program product for the processing of self-monitoring blood glucose(smbg)data to enhance diabetic self-management
EP1534121A2 (en) * 2002-08-13 2005-06-01 University Of Virginia Patent Foundation Method, system, and computer program product for the processing of self-monitoring blood glucose(smbg)data to enhance diabetic self-management
JP2005535885A (en) * 2002-08-13 2005-11-24 ユニヴァースティ オブ ヴァージニア パテント ファウンデイションUniversity Of Virginia Patent Foundation The method for processing a self-monitoring blood glucose (SMBG) data to promote diabetes self-management, system and computer program product
US8538703B2 (en) 2002-08-13 2013-09-17 University Of Virginia Patent Foundation Method, system, and computer program product for the processing of self-monitoring blood glucose(SMBG)data to enhance diabetic self-management
EP2327359A1 (en) 2002-08-13 2011-06-01 University Of Virginia Patent Foundation Method, system, and computer program product for processing of self-monitoring blood glucose (smbg) data to enhance diabetic self-management
EP1416417A3 (en) * 2002-10-08 2007-03-07 Bayer HealthCare LLC Mehtod and systems for data management in patient diagnoses and treatment
EP1416417A2 (en) * 2002-10-08 2004-05-06 Bayer Healthcare, LLC Mehtod and systems for data management in patient diagnoses and treatment
US7266400B2 (en) 2003-05-06 2007-09-04 Orsense Ltd. Glucose level control method and system
US7914449B2 (en) 2004-03-17 2011-03-29 Sysmex Corporation Diagnostic support system for diabetes and storage medium
US7815569B2 (en) 2004-04-21 2010-10-19 University Of Virginia Patent Foundation Method, system and computer program product for evaluating the accuracy of blood glucose monitoring sensors/devices
US9501949B2 (en) 2004-10-07 2016-11-22 Novo Nordisk A/S Method and system for self-management of a disease
WO2006050980A2 (en) * 2004-11-15 2006-05-18 Novo Nordisk A/S Method and apparatus for monitoring long term and short term effects of a treatment
WO2006050980A3 (en) * 2004-11-15 2006-08-31 Jon Ulrich Hansen Method and apparatus for monitoring long term and short term effects of a treatment
EP1988821A4 (en) * 2006-01-05 2010-08-25 Univ Virginia Method, system and computer program product for evaluation of blood glucose variability in diabetes from self-monitoring data
JP2009523230A (en) * 2006-01-05 2009-06-18 ユニバーシティ オブ バージニア パテント ファウンデーション Methods for assessing glycemic variability in diabetes from self-monitoring data, device and computer program product
EP1988821A2 (en) * 2006-01-05 2008-11-12 University Of Virginia Patent Foundation Method, system and computer program product for evaluation of blood glucose variability in diabetes from self-monitoring data
US7824333B2 (en) 2006-03-31 2010-11-02 Lifescan, Inc. Diabetes management methods and systems
EP2535831A1 (en) 2006-03-31 2012-12-19 Lifescan, Inc. Diabetes management methods and systems
EP1956371A2 (en) 2006-12-21 2008-08-13 Lifescan, Inc. Systems, methods and computer program codes for recognition of patterns of hyperglycemia and hypoglycemia, increased glucose variability, and ineffective self-monitoring in diabetes
WO2010077330A1 (en) * 2008-12-31 2010-07-08 Medtronic Minimed, Inc. Method and/or system for estimating glycation of hemoglobin
EP2218394A1 (en) * 2009-02-17 2010-08-18 Clifton A. Alferness System and method for providing a personalized tool for estimating glycated hemoglobin
WO2013073983A1 (en) * 2011-11-16 2013-05-23 Vengerov Yury Yuzefovitch Device for reading results of analyses performed with the aid of test strips
US8744828B2 (en) 2012-07-26 2014-06-03 Rimidi Diabetes, Inc. Computer-implemented system and method for improving glucose management through modeling of circadian profiles
US8756043B2 (en) 2012-07-26 2014-06-17 Rimidi Diabetes, Inc. Blood glucose meter and computer-implemented method for improving glucose management through modeling of circadian profiles
US8768673B2 (en) 2012-07-26 2014-07-01 Rimidi Diabetes, Inc. Computer-implemented system and method for improving glucose management through cloud-based modeling of circadian profiles
WO2016093684A1 (en) * 2014-12-09 2016-06-16 Instituto Superior Autónomo De Occidente A.C. Portable electronic device that emits suggestions about activities or events interfering in a state of interest for an individual

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US20120004512A1 (en) 2012-01-05 application
RU2283495C2 (en) 2006-09-10 grant
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US20030212317A1 (en) 2003-11-13 application
US20060094947A1 (en) 2006-05-04 application
WO2001072208A3 (en) 2002-03-07 application
US7025425B2 (en) 2006-04-11 grant
CN1422136A (en) 2003-06-04 application
CN100448392C (en) 2009-01-07 grant
JP4891511B2 (en) 2012-03-07 grant
US7874985B2 (en) 2011-01-25 grant
CA2404262C (en) 2009-03-24 grant
CA2404262A1 (en) 2001-10-04 application
EP1267708A4 (en) 2006-04-12 application

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