CONTROL-TO-RANGE AGGRESSIVENESS

• US 20170348483A1
• Filed: 06/01/2016
• Published: 12/07/2017
• Est. Priority Date: 06/01/2016
• Status: Active Grant

• Current Assignee: Roche Diabetes Care Inc. (Roche Holding AG)
• Original Assignee: Roche Diabetes Care Inc. (Roche Holding AG)
• Inventors: Duke, David L., Greenburg, Alan, Ringemann, Christian, Manohar, Chinmay Uday
• Granted Patent: US 10,332,633 B2
• Time in Patent Office: Days
• CPC Class Codes: A61M 2005/14208 with a programmable infusion control system, characterised by the infusion program
• CPC Class Codes: A61M 2205/50 with microprocessors or computers
• CPC Class Codes: A61M 2205/502 User interfaces, e.g. screens or keyboards
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2205/8212 with means or measures taken for minimising energy consumption
• 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 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method and system of determining a basal rate adjustment of insulin in a continuous glucose monitoring system of a person with diabetes is provided. The method includes receiving, by at least one computing device, a signal representative of at least one glucose measurement; detecting, by the at least one computing device, a glucose state of the person based on the signal, the detected glucose state including a glucose level of the person and a rate of change of the glucose level; determining, by the at least one computing device, a current risk metric, the current risk metric indicating a risk of at least one of a hypoglycemic condition and a hyperglycemic condition of the person; and calculating, by the at least one computing device, an adjustment to a basal rate of a therapy delivery device based on the current risk metric and a control-to-range algorithm comprising at least one aggressiveness parameter.

Claims

1. A method of determining a basal rate adjustment of insulin in a continuous glucose monitoring system of a person with diabetes, the method comprising:

receiving, by at least one computing device, a signal representative of at least one glucose measurement;

detecting, by the at least one computing device, a glucose state of the person based on the signal, the detected glucose state including a glucose level of the person and a rate of change of the glucose level;

determining, by the at least one computing device, a current risk metric, the current risk metric indicating a risk of at least one of a hypoglycemic condition and a hyperglycemic condition of the person; and

calculating, by the at least one computing device, an adjustment to a basal rate of a therapy delivery device based on the current risk metric and a control-to-range algorithm comprising at least one aggressiveness parameter.

2. The method of claim 1, wherein determining the current risk metric comprises

determining, by the at least one computing device, a return path based on a transition from the detected glucose state to a target glucose state, the return path comprising at least one intermediate glucose value associated with a return to the target glucose state;

determining, by the at least one computing device, a cumulative hazard value of the return path, the cumulative hazard value including a sum of the hazard values of the at least one glucose value on the return path, each hazard value being indicative of a hazard associated with the corresponding intermediate glucose value; and

determining, by the at least one computing device, a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around the detected glucose state

3. The method of claim 1, further comprising displaying to a user, on a graphical user interface, graphical data representative of the calculated adjustment to the basal rate.

4. The method of claim 1, further comprising transmitting a control signal to instruct the therapy delivery device to adjust the basal rate based on the calculated adjustment.

5. The method of claim 4, wherein the therapy delivery device includes an insulin pump for delivering insulin to the person with diabetes, and the therapy delivery device is in communication with the at least one computing device for receiving the calculated adjustment of the basal rate.

6. The method of claim 2, wherein the hazard value for each of the hazard values of the at least one glucose value on the return path is determined by the at least one computing device in accordance with

7. The method of claim 2, further comprising generating a basal multiplier plot based on the current risk metric.

8. The method of claim 2, wherein the aggressiveness parameter is scaling of a risk surface generated based on the risk metric with a scaling factor.

9. The method of claim 3, wherein

if T^±

=t₁^±

+t₂^±

is a real number, the cumulative hazard value of the return path is determined by the at least one computing device according to

10. The method of claim 2, wherein the aggressiveness parameter is shifting of the risk surface.

11. The method of claim 10, wherein only the risk surface representing hyperglycemic risk is shifted.

12. The method of claim 11, wherein the shift in the hyperglycemic risk surface corrects for insulin on board following a meal or correction bolus.

13. The method of claim 2, wherein the aggressiveness parameter is adjustment to a maximum allowed glucose acceleration.

14. The method of claim 13, wherein the maximum allowed glucose acceleration is increased.

15. The method of claim 13, wherein the maximum allowed glucose acceleration is decreased.

16. The method of claim 2, wherein the aggressiveness parameter is adjustment to a glucose state uncertainty.

17. The method of claim 16, wherein the glucose state uncertainty is produced by a Kalman filter.

18. The method of claim 2, wherein the glucose state distribution is determined by the at least one computing device according to

19. The method of claim 2, wherein the current risk metric is determined by the at least one computing device according to

21. The device of claim 19, wherein determining the current risk metric comprises

determining, by the at least one computing device, a return path based on a transition from the detected glucose state to a target glucose state, the return path comprising at least one intermediate glucose value associated with a return to the target glucose state;

determining, by the at least one computing device, a cumulative hazard value of the return path, the cumulative hazard value including a sum of the hazard values of the at least one glucose value on the return path, each hazard value being indicative of a hazard associated with the corresponding intermediate glucose value; and

determining, by the at least one computing device, a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around the detected glucose state

20. A blood glucose management device configured to determine a basal rate adjustment in a continuous glucose monitoring system of a person with diabetes, the device comprising:

a non-transitory computer-readable medium storing executable instructions; and

at least one processing device configured to execute the executable instructions such that, when executed by the at least one processing device, the executable instructions cause the at least one processing device to;

receive a signal representative of at least one glucose measurement;

detect a glucose state of the person based on the signal, the detected glucose state including a glucose level of the person and a rate of change of the glucose level;

determine a current risk metric, the current risk metric indicating a risk of at least one of a hypoglycemic condition and a hyperglycemic condition of a person and

calculate an adjustment to a basal rate of a therapy delivery based on the current risk metric and a control-to-range algorithm comprising at least one aggressiveness parameter.