Prandial Blood Glucose Excursion Optimization Method Via Computation of Time-Varying Optimal Insulin Profiles and System Thereof

• US 20100125241A1
• Filed: 11/17/2008
• Published: 05/20/2010
• Est. Priority Date: 11/17/2008
• Status: Active Grant

• Current Assignee: Roche Diabetes Care Inc. (Roche Holding AG)
• Original Assignee: Disetronic Licensing Ag
• Inventors: Prud'homme, Thierry, Cris, Catalin
• Granted Patent: US 8,527,208 B2
• Time in Patent Office: Days
• US Class Current: 604/65
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method and system for optimizing post prandial blood glucose excursions for persons with diabetes via computation of time-varying optimal insulin profiles are disclosed. The method builds upon a mathematical model based on the physiologic description of the human glucoregulatory system. Model parameter values are specific for each patient and meal type, and are determined from measured data by minimizing differences between both measurements and model predictions, and which are used to provide the optimal insulin profiles that are useful to diabetics in managing their chronic disease.

Claims

1. A method for the computation of an optimal insulin infusion profile useful as a postprandial blood glucose control for a patient/meal combination, said method comprising:

providing a pre-meal measurement of blood glucose concentration;

providing size and composition of a meal to be consumed;

providing pre-meal insulin infusion history,

providing a chosen insulin pattern for the postprandial blood glucose control;

providing a dynamical model having model parameters which are predetermined for the patient/meal combination;

computing automatically the optimal insulin infusion profile by minimizing an objective function which is defined such that if two different insulin profiles are candidates to compensate for a patient/meal combination, the value of the objective function will be lower for the insulin profile which represents the best postprandial blood glucose control for this patient/meal combination, and said minimizing resulting in the optimal insulin infusion profile; and

providing the optimal insulin infusion profile.

2. The method of claim 1, wherein the pre-meal measurement of blood glucose concentration is provided via spot measurements performed during routine therapy.

3. The method of claim 1, wherein the pre-meal measurement of blood glucose concentration is provided via continuous measurements performed during routine therapy.

4. The method of claim 1, further comprises providing information concerning the absorption speed and carbohydrate content of the meal to be consumed.

5. The method of claim 1, further comprises using an insulin pump to provide the pre-meal insulin infusion history.

6. The method of claim 1, further comprises using a blood glucose meter to provide the pre-meal measurement of blood glucose concentration.

7. The method of claim 1, further comprises using a portable electronic device to record data concerning the pre-meal insulin infusion history, the pre-meal measurement of blood glucose concentration, and the size and composition of a meal to be consumed, and providing the data to a computing device.

8. The method of claim 1, further comprises using a portable electronic device to compute automatically the optimized insulin infusion profile by minimizing the objective function.

9. The method of claim 1, wherein the dynamical minimal model is selected from the group of models consisting of a set of linear or nonlinear ordinary differential equations, a set of linear or nonlinear partial differential equations, a set of linear or nonlinear ordinary difference equations, a set of linear or nonlinear partial difference equations, a set of linear or nonlinear stochastic ordinary differential equations, a set of linear or nonlinear stochastic partial differential equations, a set of linear or nonlinear stochastic ordinary difference equations, a set of linear or nonlinear stochastic partial difference equations, an autoregressive moving average model, and a neural network model.

10. The method of claim 1 wherein a meal related glucose absorption is computed from a meal related carbohydrate intake rate for use in the objective function.

11. The method of claim 1, further comprises performing parameter identification to predetermine the model parameters.

12. The method of claim 11, wherein the parameter identification is a parameter identification method selected from the group consisting of maximum likelihood estimation, maximum a posteriori estimation, maximum a posteriori estimation with Bayesian priors, least squares fitting, and nonlinear least squares fitting.

13. The method of claim 1, wherein the objective function is minimized using means selected from the group consisting of global optimization, genetic algorithms, simulated annealing, linear programming, stochastic optimization, and calculus of variations.

14. The method of claim 1, wherein the objective function is defined by:

15. The method of claim 11, wherein said parameter identification is implemented on a electronic device selected from the group consisting of a desktop computer, and a portable electronic device providing a diabetes therapy guidance system.

16. The method of claim 1, wherein the optimal insulin infusion profile is provided to an insulin pump of the patient.

17. The method of claim 1, further comprises modifying insulin dosage and timing sequence of insulin infusion according to the optimal insulin infusion profile when the patient indicates at a subsequent time that the meal is to be consumed.

18. The method of claim 17, wherein the modifying is carried out automatically on an insulin pump of the patient.

19. The method of claim 1, wherein the optimal insulin infusion profile is provided to a display of an electronic device, and said method further comprises permitting acceptance of the displayed optimal insulin profile before use as the postprandial blood glucose control for the patient/meal combination.

20. A system which provides an optimal insulin infusion profile useful as a postprandial blood glucose control for a patient/meal combination, said system comprising:

an input device configured to receive information about a pre-meal measurement of blood glucose concentration, and size and composition of a meal to be consumed;

a memory which contains additional information about a pre-meal insulin infusion history, a chosen insulin pattern for the postprandial blood glucose control, and a dynamical minimal model having model parameters which are predetermined for the patient/meal combination;

a processor programmed to use the received information and the additional information to compute automatically the optimal insulin infusion profile by minimizing an objective function, wherein said minimizing of the objective function results in the optimal insulin infusion profile; and

a display configured to display the optimal insulin infusion profile.