Risk-based control-to-range

• US 10,297,350 B2
• Filed: 06/01/2016
• Issued: 05/21/2019
• 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., Ringemann, Christian, Manohar, Chinmay Uday, Greenburg, Alan
• Primary Examiner(s): Riggs, II, Larry D
• Application Number:
  US15/170,468
• Publication Number:
  US 20170348484A1
• Time in Patent Office: 1,084 Days
• Field of Search: None
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61M 2005/14208 with a programmable infusion control system, characterised by the infusion program
• CPC Class Codes: A61M 2205/35 Communication
• CPC Class Codes: A61M 2205/3569 sublocal, e.g. between console and disposable
• CPC Class Codes: A61M 2205/3584 using modem, internet or bluetooth
• 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 2230/201 Glucose concentration
• CPC Class Codes: A61M 5/14244 adapted to be carried by the patient, e.g. portable on the body
• 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 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Methods and systems are disclosed for determining a basal rate adjustment of insulin based on risk associated with a glucose state of a person with diabetes. A method may include detecting a glucose state of the person based on a received glucose measurement signal and determining a current risk metric associated with the detected glucose state. The method may include determining a current risk metric associated with the detected glucose state based on a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around a detected glucose state. The method may include calculating an adjustment to a basal rate of a therapy delivery device based on the current risk metric associated with the detected glucose state and a reference risk metric associated with a reference glucose level.

Claims

1. A method of determining and administering a basal rate adjustment of insulin based on risk associated with a glucose state 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 associated with the detected glucose state based on a target glucose state, the target glucose state being stored in memory accessible by the at least one computing device, the current risk metric indicating a risk of at least one of a hypoglycemic condition and a hyperglycemic condition of the person,

wherein a return path is determined based on a transition from the current glucose state to the target glucose state, the return path comprising at least one intermediate glucose value associated with a return to the target glucose state,

wherein a cumulative hazard value of the return path is determined, 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,

wherein the current risk metric is determined based on a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around the detected glucose state;

identifying, by the at least one computing device, a reference glucose state and a reference risk metric associated with the reference glucose state;

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 associated with the detected glucose state and the reference risk metric associated with the reference glucose level;

transmitting a control signal to instruct the therapy delivery device to adjust the basal rate based on the calculated adjustment; and

administering the adjusted basal rate to the person with diabetes.

2. The method of claim 1, wherein the calculating comprises mapping the current risk metric to a percent reduction of the basal rate based on the reference risk metric.

3. The method of claim 2, wherein the reference glucose state includes a glucose level corresponding to a hypoglycemic condition.

4. 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.

5. The method of claim 1, 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 1, 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 1, further comprising, prior to determining the cumulative hazard value of the return path, identifying, by the at least one computing device, a shifted glucose target for the person to account for a positively shifted hypoglycemic risk in accordance with

ĝ

_t=g_t+max(Δ

g_hypo,0)

where ĝ

_t is the shifted glucose target, g_t is a nominal glucose target, and Δ

g_hypo is a hypoglycemic shift.

8. The method of claim 7, 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

9. The method of claim 7, 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 1, wherein the glucose state distribution is determined by the at least one computing device according to

11. The method of claim 10, wherein k=10 and n=8.

12. The method of claim 1, wherein the current risk metric is determined by the at least one computing device according to

13. The method of claim 2, wherein a basal multiplier value is determined by the by the at least one computing device according to

14. The method of claim 13, wherein r_0% is the risk metric at a glucose state linked to complete basal shutoff.

15. The method of claim 14, wherein a temporary basal rate is determined for transmission to the therapy delivery device is determined by the at least one computing device according to

16. The method of claim 15, wherein a temporary basal rate multiplier limit accounting for insulin sensitivity of the person is determined by the at least one computing device according to

17. The method of claim 16, wherein the TBR_MAX is 250% and the G_brT is 150 mg/dl.

18. The method of claim 6, wherein Δ

g_hyper and Δ

g_hypo are adjusted based on detection of an over-correction bolus.

19. A blood glucose management device configured to determine a basal rate adjustment based on risk associated with a glucose state 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 associated with the detected glucose state based on a target glucose state, the target glucose state being stored in memory accessible by the at least one computing device, the current risk metric indicating a risk of at least one of a hypoglycemic condition and a hyperglycemic condition of the person,

wherein a return path is determined based on a transition from the current glucose state to the target glucose state, the return path comprising at least one intermediate glucose value associated with a return to the target glucose state,

wherein a cumulative hazard value of the return path is determined, 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,

wherein the current risk metric is determined based on a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around the detected glucose state;

identify a reference glucose state and a reference risk metric associated with the reference glucose state;

calculate an adjustment to a basal rate of a therapy delivery device based on the current risk metric associated with the detected glucose state and the reference risk metric associated with the reference glucose level;

transmit a control signal to instruct the therapy delivery device to adjust the basal rate based on the calculated adjustment; and

administer the adjusted basal rate to the person with diabetes.