OVERNIGHT CLOSED-LOOP INSULIN DELIVERY WITH MODEL PREDICTIVE CONTROL AND GLUCOSE MEASUREMENT ERROR MODEL

• US 20100280441A1
• Filed: 03/31/2010
• Published: 11/04/2010
• Est. Priority Date: 03/31/2009
• Status: Active Grant

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated), Cambridge Enterprises Limited
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated), Cambridge Enterprises Limited
• Inventors: Hovorka, Roman, Hayter, Gary A., Taub, Marc B., Wilinska, Malgorzata E., Budiman, Erwin S.
• Granted Patent: US 8,062,249 B2
• Time in Patent Office: Days
• US Class Current: 604/66
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61M 2005/14208 with a programmable infusion control system, characterised by the infusion program
• CPC Class Codes: A61M 2005/14292 Computer-based infusion planning or simulation of spatio-temporal infusate distribution
• CPC Class Codes: A61M 2005/14296 Pharmacokinetic models
• CPC Class Codes: A61M 2205/50 with microprocessors or computers
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2205/70 with testing or calibration facilities
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 5/142 Pressure infusion, e.g. using pumps
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G16H 20/17 delivered via infusion or injection

Abstract

A closed-loop system for insulin infusion overnight uses a model predictive control algorithm (“MPC”). Used with the MPC is a glucose measurement error model which was derived from actual glucose sensor error data. That sensor error data included both a sensor artifacts component, including dropouts, and a persistent error component, including calibration error, all of which was obtained experimentally from living subjects. The MPC algorithm advised on insulin infusion every fifteen minutes. Sensor glucose input to the MPC was obtained by combining model-calculated, noise-free interstitial glucose with experimentally-derived transient and persistent sensor artifacts associated with the FreeStyle Navigator® Continuous Glucose Monitor System (“FSN”). The incidence of severe and significant hypoglycemia reduced 2300- and 200-fold, respectively, during simulated overnight closed-loop control with the MPC algorithm using the glucose measurement error model suggesting that the continuous glucose monitoring technologies facilitate safe closed-loop insulin delivery.

Claims

1. A system for the delivery of insulin to a patient, the system comprising:

a glucose sensor configured to provide a sensor glucose measurement signal representative of sensed glucose;

an insulin delivery device configured to deliver insulin to a patient in response to control signals; and

a controller programmed to receive the sensor glucose measurement signal and to provide a delivery control signal to the delivery device as a function of the received sensor glucose measurement signal in accordance with a control model and a glucose measurement error model;

wherein the glucose measurement error model is derived from actual glucose sensor measurement data.

2. The system for the delivery of insulin of claim 1, wherein the glucose measurement error model is derived solely from actual glucose sensor measurement data.

3. The system for the delivery of insulin of claim 1, wherein the glucose measurement error model is derived solely from actual glucose sensor error data, excluding sensor noise data.

4. The system for the delivery of insulin of claim 1, wherein the glucose measurement error model is derived solely from actual glucose sensor measurement data to the exclusion of randomly-generated variable data.

5. The system for the delivery of insulin of claim 1, wherein the glucose measurement error model is derived solely from a fixed time history of error data from actual use of a glucose sensor of the same type as the sensor of the system.

6. The system for the delivery of insulin of claim 1, wherein the glucose measurement error model is derived from actual glucose sensor measurement data from a glucose sensor of the same type as the sensor of the system.

7. The system for the delivery of insulin of claim 1, wherein the glucose measurement error model is derived solely from a fixed time history of error data from actual use of a glucose sensor of the same type as the sensor of the system, to the exclusion of randomly-generated variable data and to the exclusion of sensor noise data.

8. The system for the delivery of insulin of claim 1, wherein the control model comprises a model predictive controller that is programmed to provide the delivery control signals to the delivery device as a function of a model predictive control.

9. The system for the delivery of insulin of claim 1, wherein the glucose measurement error model is derived from calibration error of the glucose sensor.

10. The system for the delivery of insulin of claim 9, wherein the calibration error in the glucose measurement error model comprises the difference between a plasma glucose level and the sensor glucose level signal of the glucose sensor.

11. The system for delivering insulin of claim 1, wherein the glucose measurement error model is derived from a glucose sensor dropout.

12. The system for the delivery of insulin of claim 10, wherein the controller is further programmed to recalibrate the system when the difference between the received sensor glucose level signal and a plasma glucose level exceeds a predetermined level.

13. The system for delivering insulin of claim 1, wherein the delivery control signal is also a function of weight of a patient, a total daily insulin dose, and a basal insulin profile, and wherein:

the controller is also programmed to calculate from the control model an accepted value;

the controller is also programmed to calculate from the glucose level signal an inferred value;

the controller is also programmed to forecast a future plasma glucose level excursion based on the accepted value and inferred value; and

the controller is also programmed to adjust the delivery control signal in accordance with the forecast future plasma glucose level excursion.

14. The system for delivering insulin of claim 13, wherein the accepted value comprises an insulin sensitivity of the patient, a glucose distribution volume, and an insulin distribution volume.

15. The system for delivering insulin of claim 13, wherein the inferred value comprises glucose flux and a carbohydrate bioavailability.

16. The system for delivering insulin of claim 1, wherein the controller is also programmed to adjust a value of the delivery control signal in accordance with a safety check.

17. The system for delivering insulin of claim 16, wherein the controller is also programmed to adjust a value of the delivery control signal in accordance with the safety check to include at least one of:

imposing a maximum infusion rate related to a basal rate depending on a current sensor glucose level, time since a previous meal, and carbohydrate content of a meal;

shutting off insulin delivery at a predetermined low sensor glucose value;

reducing insulin delivery when sensor glucose is decreasing rapidly; and

capping the insulin infusion to a pre-programmed basal rate if an insulin delivery pump occlusion is inferred.

18. The system for delivering insulin of claim 1, wherein the glucose sensor, the insulin delivery device, and the controller are virtual devices, each being programmed for in silico testing of a system for delivery of insulin to a virtual patient.

19. A method for delivering insulin to a patient, the method comprising:

sensing a glucose level and providing a glucose measurement signal representative of the sensed glucose;

providing a control signal as a function of the glucose measurement signal in accordance with a control model and a glucose measurement error model, wherein the glucose measurement error model is derived from actual glucose sensor measurement data; and

delivering insulin in response to the control signal.

20. The method for delivering insulin of claim 19, wherein the glucose measurement error model is derived solely from actual glucose sensor measurement data

21. The method for delivering insulin of claim 19, wherein the glucose measurement error model is derived solely from actual glucose sensor error data, excluding sensor noise data.

22. The method for delivering insulin of claim 19, wherein the glucose measurement error model is derived solely from actual glucose sensor measurement data to the exclusion of randomly-generated variable data.

23. The method for delivering insulin of claim 19, wherein the glucose measurement error model is derived solely from a fixed time history of error data from actual use of a glucose sensor of the same type as the sensor of the system.

24. The method for delivering insulin of claim 19, wherein the glucose measurement error model is derived from actual glucose sensor measurement data from a glucose sensor of the same type as the sensor of the system.

25. The method for delivering insulin of claim 19, wherein the glucose measurement error model is derived solely from a fixed time history of error data from actual use of a glucose sensor of the same type as the sensor of the system, to the exclusion of randomly-generated variable data and to the exclusion of sensor noise data.

26. The method for delivering insulin of claim 19, wherein providing the control signal further comprises producing the control signal in accordance with a model predictive control.

27. The method for delivering insulin of claim 19, further comprising determining a calibration error of a glucose sensor from actual sensor data and deriving the glucose measurement error model therefrom.

28. The method for delivering insulin of claim 27, wherein determining a calibration error comprises determining calibration error based on the difference between a plasma glucose level and the glucose level signal.

29. The method for delivering insulin of claim 19, further comprising determining a glucose sensor dropout reading from actual sensor data and deriving the glucose measurement error model therefrom.

30. The method for delivering insulin of claim 19, wherein providing the control signal is also a function of weight of a patient, a total daily insulin dose, and a basal insulin profile, the method further comprising:

determining, based on the control model, at least one accepted value;

calculating from the glucose level signal at least one inferred value;

adjusting the control model in accordance with the accepted value and inferred value; and

forecasting a future plasma glucose level excursion based on the control model.

31. The method for delivering insulin of claim 30, wherein determining the accepted value comprises basing the determination on an insulin sensitivity of the patient, a glucose distribution volume, and an insulin distribution volume.

32. The method for delivering insulin of claim 30, wherein calculating the inferred value comprises calculating the inferred value also from glucose flux and a carbohydrate bioavailability.

33. The method for delivering insulin of claim 19, further comprising adjusting a value of the control signal in accordance with a safety check.

34. The method for delivering insulin of claim 33 wherein adjusting a value of the control signal comprises at least one of:

imposing a maximum infusion rate related to a basal rate depending on a current sensor glucose level, time since a previous meal, and carbohydrate content of a meal;

shutting off insulin delivery at a predetermined low sensor glucose value;

reducing insulin delivery when sensor glucose is decreasing rapidly; and

capping the insulin infusion to a pre-programmed basal rate if an insulin delivery pump occlusion is inferred.

35. The method for delivering insulin of claim 19, wherein sensing, providing a control signal, and delivering insulin are performed virtually, each occurring for in silico testing of a method for delivery of insulin to a virtual patient.