SYSTEM AND METHOD FOR MANAGING TYPE 2 DIABETES MELLITUS THROUGH A PERSONAL PREDICTIVE MANAGEMENT TOOL

• US 20100145670A1
• Filed: 02/12/2008
• Published: 06/10/2010
• Est. Priority Date: 02/12/2008
• Status: Abandoned Application

• Current Assignee: Clifton A. Alferness, Gust H. Hardy
• Original Assignee: Clifton A. Alferness, Gust H. Hardy
• Inventors: Hardy, Gust H., Alferness, Clifton A.
• Application Number:
  US12/030,130
• Publication Number:
  US 20100145670A1
• Time in Patent Office: Days
• US Class Current: 703/11
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 70/60 relating to pathologies
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A system and method for managing Type 2 diabetes mellitus through a personal predictive management tool is provided. An insulin resistance for a Type 2 diabetes patient is identified. A time course curve is maintained for a patient population including expected blood glucose levels for a type of human-consumable food. The blood glucose levels following consumption of the food is estimated by adjusting the time course curve as a function of the patient-specific insulin resistance that has been manifested.

Claims

1. A system for managing Type 2 diabetes mellitus through a personal predictive management tool, comprising:

a database comprising;

an insulin resistance for a Type 2 diabetes patient; and

a time course curve for a patient population comprising expected blood glucose levels for a type of human-consumable food; and

an analysis module configured to estimate the blood glucose levels following consumption of the food by adjusting the time course curve as a function of the patient-specific insulin resistance that has been manifested.

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

an analysis module configured to determine an effect on the insulin resistance for a physical activity, wherein the time course curve is revised based on the effect of the physical activity.

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

an analysis module configured to determine an effect on the insulin resistance for an antidiabetic medication, wherein the time course curve is revised based on the effect of the antidiabetic medication.

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

a personal insulin response profile for the patient for a type of insulin preparation further comprised in the database, wherein the blood glucose levels following consumption of the food are estimated by evaluating an interaction between the personal insulin response profile and the time course curve over a duration of action of the insulin preparation.

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

a library of time course curves for a multiplicity of types of human-consumable foods for a patient population, wherein the time course curves are aggregated from a common reference time, and the estimated blood glucose levels are revised as a function of the aggregated time course curves.

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

a library of insulin response profiles for the patient for other types of insulin preparations for Type 2 diabetes mellitus treatment, wherein the personal insulin response profiles for each of the other types of insulin preparation are referenced.

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

a blood glucose level threshold; and

a forecasting module configured to identify a point at which an expected blood glucose level from the personal insulin response profile is expected to either exceed or fall below the blood glucose level threshold.

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

a carbohydrate sensitivity for the patient;

a blood glucose estimator module configured to generate a personal time course curve for the patient, comprising;

a modeling module configured to determine a carbohydrate sensitivity coefficient by interpolating the carbohydrate sensitivity over the patient population time course curve; and

an application module configured to apply the carbohydrate sensitivity coefficient to the patient population time course curve.

9. A system according to claim 1, wherein the time course curve is provided as a projection of one of a glycemic index and glycemic load for the type of food.

10. A system according to claim 1, wherein an empirically observed increase in blood glucose level is identified for a fixed-sized serving of the type of food as the carbohydrate sensitivity.

11. A system according to claim 10, wherein the empirically observed rise during discrete time intervals is determined throughout a 24-hour period.

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

an insulin estimator configured to determine parameters for a dosage of the insulin preparation, and to re-evaluate the insulin sensitivity coefficient by applying the parameters to the personal insulin response profile.

13. A system according to claim 12, wherein the dosage parameters comprises one or more of insulin basal dose, insulin bolus dose, insulin bolus timing, period of day, and time of day.

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

an evaluation module configured to determine factors affecting the patient, and to re-estimate the blood glucose levels applying the patient factors to the time course curve.

15. A system according to claim 14, wherein the patient factors comprises one or more of timing of consumption, amount of food, food composition, patient activity level, patient activity timing, and patient physical condition.

16. A system according to claim 1, wherein the personal insulin response profile is characterized by timing of onset, peak of action, and duration of action of the insulin preparation.

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

18. A method for managing Type 2 diabetes mellitus through a personal predictive management tool, comprising:

identifying an insulin resistance for a Type 2 diabetes patient;

maintaining a time course curve for a patient population comprising expected blood glucose levels for a type of human-consumable food; and

estimating the blood glucose levels following consumption of the food by adjusting the time course curve as a function of the patient-specific insulin resistance that has been manifested.

19. A method according to claim 18, further comprising:

determining an effect on the insulin resistance for a physical activity; and

revising the time course curve based on the affect of the physical activity.

20. A method according to claim 18, further comprising:

determining, an effect on the insulin resistance for an antidiabetic medication; and

revising the time course curve based on the effect of the antidiabetic medication.

21. A method according to claim 18, further comprising:

referencing a personal insulin response profile for the patient for a type of insulin preparation; and

estimating the blood glucose levels following consumption of the food by evaluating an interaction between the personal insulin response profile and the time course curve over a duration of action of the insulin preparation.

22. A method according to claim 18, further comprising:

assembling a library of time course curves for a multiplicity of types of human-consumable foods for a patient population;

aggregating the time course curves from a common reference time; and

revising the estimated blood glucose levels as a function of the aggregated time course curves.

23. A method according to claim 18, further comprising:

assembling a library of insulin response profiles for the patient for other types of insulin preparations for Type 2 diabetes mellitus treatment; and

referencing the personal insulin response profiles for each of the other types of insulin preparation.

24. A method according to claim 18, further comprising:

defining a blood glucose level threshold; and

identifying a point at which an expected blood glucose level from the personal insulin response profile is expected to either exceed or fall below the blood glucose level threshold.

25. A method according to claim 18, further comprising:

obtaining a carbohydrate sensitivity for the patient;

generating a personal time course curve for the patient, comprising;

determining a carbohydrate sensitivity coefficient by interpolating the carbohydrate sensitivity over the patient population time course curve; and

applying the carbohydrate sensitivity coefficient to the patient population time course curve.

26. A method according to claim 18, further comprising:

providing the time course curve as a projection of one of a glycemic index and glycemic load for the type of food.

27. A method according to claim 18, further comprising:

identifying an empirically observed increase in blood glucose level for a fixed-sized serving of the type of food as the carbohydrate sensitivity.

28. A method according to claim 27, further comprising:

determining the empirically observed rise during discrete time intervals throughout a 24-hour period.

29. A method according to claim 18, further comprising:

determining parameters for a dosage of the insulin preparation; and

re-evaluating the insulin sensitivity coefficient by applying the parameters to the personal insulin response profile.

30. A method according to claim 29, wherein the dosage parameters comprises one or more of insulin basal dose, insulin bolus dose, insulin bolus timing, period of day, and time of day.

31. A method according to claim 18, further comprising:

determining factors affecting the patient; and

re-estimating the blood glucose levels applying the patient factors to the time course curve.

32. A method according to claim 31, wherein the patient factors comprises one or more of timing of consumption, amount of food, food composition, patient activity level, patient activity timing, and patient physical condition.

33. A method according to claim 18, wherein the personal insulin response profile is characterized by timing of onset, peak of action, and duration of action of the insulin preparation.

34. A method according to claim 18, wherein the insulin preparation type comprises one of rapid-acting insulin, short-acting insulin, intermediate-acting insulin, long-acting insulin, insulin glargine, insulin detemir, and an insulin preparation combination.