SYSTEM AND METHOD FOR GENERATING A PERSONALIZED DIABETES MANAGEMENT TOOL FOR DIABETES MELLITUS

• US 20100138453A1
• Filed: 02/12/2008
• Published: 06/03/2010
• Est. Priority Date: 02/12/2008
• Status: Abandoned Application

• Current Assignee: Clifton A. Alferness, Gust H. Bardy
• Original Assignee: Clifton A. Alferness, Gust H. Bardy
• Inventors: Bardy, Gust H., Alferness, Clifton A.
• Application Number:
  US12/030,104
• Publication Number:
  US 20100138453A1
• Time in Patent Office: Days
• US Class Current: 707/803
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 70/40 relating to drugs, e.g. their side effects or intended usage

Abstract

A system and method for generating a personalized diabetes management tool for diabetes mellitus is provided. An insulin activity curve for a patient population for an insulin preparation for diabetes mellitus treatment is identified. A personal insulin activity model for the patient is generated. An insulin sensitivity is determined by taking a derivative of the rate of change of blood glucose over time for the insulin preparation. An insulin sensitivity coefficient for the insulin preparation for a patient of diabetes mellitus is established. The insulin sensitivity coefficient is applied to the patient population insulin activity curve over a duration of action of the insulin preparation.

Claims

1. A system for generating a personalized diabetes management tool for diabetes mellitus, comprising:

a database, comprising an insulin activity curve for a patient population for an insulin preparation for diabetes mellitus treatment; and

a modeler module configured to generate a personal insulin activity model for the patient, comprising;

an analysis module configured to determine an insulin sensitivity by taking a derivative of the rate of change of blood glucose over time for the insulin preparation, and to establish an insulin sensitivity coefficient for the insulin preparation for a patient of diabetes mellitus; and

an application module configured to apply the insulin sensitivity coefficient to the patient population insulin activity curve over a duration of action of the insulin preparation.

2. A system according to claim 1, wherein the database further comprises a carbohydrate sensitivity coefficient for the patient, which is included in the personal insulin activity model through a digestive response curve running contemporaneous to the insulin activity curve.

3. A system according to claim 1, wherein the database further comprises a medication other than an insulin preparation, and a hematological effect of the medication oil blood glucose is included in the personal insulin activity model.

4. A system according to claim 3, wherein the medication is one of an antidiabetic medication and an oral medication selected from the group comprising exenatide, pramlintide acetate, sulfonylurea, meglinitinide, nateglinitide, biguanides, thiazolidinediones, and alpha-glucose inhibitor.

5. A system according to claim 1, further comprising:

an approximation module configured to model the patient population insulin activity curve by determining a filter length representing time to peak activity and an exponential decay following therefrom, wherein the filter length and the exponential decay are proportioned by the insulin sensitivity coefficient to form the personal insulin activity model.

6. A system according to claim 1, wherein the database further comprises a history of empirically measured blood glucose levels for the patient, which were each recorded over a dosage of the insulin preparation, the system further comprising:

a comparison module configured to compare expected blood glucose levels from the personal insulin activity model to the measured blood glucose levels for each dosage; and

a re-evaluation module configured to re-evaluate the insulin sensitivity coefficient against observed differences between the expected blood glucose levels and the measured blood glucose levels.

7. A system according to claim 1, wherein the database further comprises clinical monitoring data aggregated for the patient selected from the set comprising glycated hemoglobin, fructosamine, urinary glucose, urinary ketone, and interstitial glucose, the system further comprising:

a re-evaluation module configured to re-evaluate the insulin sensitivity coefficient by applying the clinical monitoring data to the personal insulin activity model.

8. A system according to claim 1, wherein the database further comprises a history of event data aggregated for the patient, which were each recorded over a dosage of the insulin preparation, the system further comprising;

a re-evaluation module configured to re-evaluate the insulin sensitivity coefficient by applying the event data to the personal insulin activity model.

9. A system according to claim 8, wherein the event data comprises one or more of insulin basal dose, insulin bolus dose, insulin bolus timing, insulin resistance level, period of day, time of day, patient activity level, and patient physical condition.

10. A system according to claim 1, wherein the database further comprises a library of insulin activity curves for the patient population for other insulin preparations for diabetes mellitus treatment, the system further comprising:

an evaluation module configured to generate personal insulin activity models for the patient for each of the other insulin preparations.

11. A system according to claim 1, wherein the database further comprises quantitative characteristics of the patient, the system further comprising:

a selection module configured to choose the patient population insulin activity curve most appropriately corresponding to the quantitative characteristics of the patient.

12. A system according to claim 1, further comprising:

an interstitial glucose testing module to identify an empirically observed decrease in interstitial glucose level recorded for a dosage of the insulin preparation, wherein the interstitial glucose level decrease is adapted to a decrease in blood glucose level as the insulin sensitivity.

13. A system according to claim 1, wherein at least one of the personal insulin activity model and the patient population insulin activity curve are characterized by timing of onset, peak of action, and duration of action of the insulin preparation.

14. A system according to claim 1, wherein the insulin preparation comprises one of rapid-acting insulin, short-acting insulin, intermediate-acting insulin, long-acting insulin, insulin glargine, insulin detemir, and an insulin preparation mix.

15. A method for generating a personalized diabetes management tool for diabetes mellitus, comprising:

identifying an insulin activity curve for a patient population for an insulin preparation for diabetes mellitus treatment; and

generating a personal insulin activity model for the patient, comprising;

determining an insulin sensitivity by taking a derivative of the rate of change of blood glucose over time for the insulin preparation;

establishing an insulin sensitivity coefficient for the insulin preparation for a patient of diabetes mellitus; and

applying the insulin sensitivity coefficient to the patient population insulin activity curve over a duration of action of the insulin preparation.

16. A method according to claim 15, further comprising:

establishing a carbohydrate sensitivity coefficient for the patient; and

including the carbohydrate sensitivity coefficient in the personal insulin activity model by generating a digestive response curve running contemporaneous to the insulin activity curve.

17. A method according to claim 15, further comprising:

identifying a medication other than an insulin preparation; and

including a hematological effect of the medication on blood glucose in the personal insulin activity model.

18. A method according to claim 17, wherein the medication is one of an antidiabetic medication and an oral medication selected from the group comprising exenatide, pramlintide acetate, sulfonylurea, meglinitinide, nateglinitide, biguanides, thiazolidinediones, and alpha-glucose inhibitor.

19. A method according to claim 15, further comprising:

modeling the patient population insulin activity curve by determining a filter length representing time to peak activity and an exponential decay following therefrom; and

proportioning the filter length and the exponential decay by the insulin sensitivity coefficient to form the personal insulin activity model.

20. A method according to claim 15, further comprising:

maintaining a history of empirically measured blood glucose levels for the patient, which were each recorded over a dosage of the insulin preparation;

comparing expected blood glucose levels from the personal insulin activity model to the measured blood glucose levels for each dosage; and

re-evaluating the insulin sensitivity coefficient against observed differences between the expected blood glucose levels and the measured blood glucose levels.

21. A method according to claim 15, further comprising:

aggregating clinical monitoring data for the patient selected from the set comprising glycated hemoglobin, fructosamine, urinary glucose, urinary ketone, and interstitial glucose; and

re-evaluating the insulin sensitivity coefficient by applying the clinical monitoring data to the personal insulin activity model.

22. A method according to claim 15, further comprising:

aggregating a history of event data for the patient, which were each recorded over a dosage of the insulin preparation; and

re-evaluating the insulin sensitivity coefficient by applying the event data to the personal insulin activity model.

23. A method according to claim 22, wherein the event data comprises one or more of insulin basal dose, insulin bolus dose, insulin bolus timing, insulin resistance level, period of day, time of day, patient activity level, and patient physical condition.

24. A method according to claim 15, further comprising:

assembling a library of insulin activity curves for the patient population for other insulin preparations for diabetes mellitus treatment; and

generating personal insulin activity models for the patient for each of the other insulin preparations.

25. A method according to claim 15, further comprising:

obtaining quantitative characteristics of the patient; and

choosing the patient population insulin activity curve most appropriately corresponding to the quantitative characteristics of the patient.

26. A method according to claim 15, further comprising:

identifying an empirically observed decrease in interstitial glucose level recorded for a dosage of the insulin preparation; and

adapting the interstitial glucose level decrease to a decrease in blood glucose level as the insulin sensitivity.

27. A method according to claim 15, wherein at least one of the personal insulin activity model and the patient population insulin activity curve are characterized by timing of onset, peak of action, and duration of action of the insulin preparation.

28. A method according to claim 15, wherein the insulin preparation comprises one of rapid-acting insulin, short-acting insulin, intermediate-acting insulin, long-acting insulin, insulin glargine, insulin detemir, and an insulin preparation mix.