SAFEGUARDING MEASURES FOR A CLOSED-LOOP INSULIN INFUSION SYSTEM

• US 20140066885A1
• Filed: 04/25/2013
• Published: 03/06/2014
• Est. Priority Date: 08/30/2012
• Status: Active Grant

• Current Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Inventors: Keenan, Desmond Barry, Mastrototaro, John J., Grosman, Benyamin, Parikh, Neha J., Roy, Anirban
• Granted Patent: US 9,526,834 B2
• Time in Patent Office: Days
• US Class Current: 604/504
• CPC Class Codes: A61B 5/14503 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors A61B5/1459, A61B5/1464, A61B5/1473, A61B5/1482, A61B5/14865 take precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61M 2205/18 with alarm
• 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 2205/582 by tactile feedback
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 5/172 electrical or electronic A61M5/16804, A61M5/16831 take precedence
• 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 50/50 for simulation or modelling of medical disorders

Abstract

Processor-implemented methods of controlling an insulin infusion device for a user are provided here. A first method obtains a current insulin on board (IOB) value that estimates active insulin in the user, and compensates a calculated insulin infusion rate in response to the obtained IOB value. A second method supervises the operation of a glucose sensor by obtaining and processing insulin-delivered data and glucose sensor data for the user. An alert is generated if the second method determines that a current glucose sensor value has deviated from a predicted sensor glucose value by at least a threshold amount.

Claims

1. A processor-implemented method of controlling an insulin infusion device for a user, the method comprising:

operating a processor architecture comprising at least one processor device to obtain a current insulin on board (IOB) value that represents an estimate of active insulin in the body of the user;

calculating, by the processor architecture, an IOB rate based at least in part on the obtained current IOB value;

determining, by the processor architecture, an adjusted insulin infusion rate based at least in part on the calculated IOB rate and an uncompensated insulin infusion rate; and

selecting, by the processor architecture, a final insulin infusion rate for the insulin infusion device, wherein the selecting selects either the determined adjusted insulin infusion rate, the uncompensated insulin infusion rate, or a current basal rate as the final insulin infusion rate.

2. The method of claim 1, further comprising:

regulating delivery of insulin by the insulin infusion device in accordance with the selected final insulin infusion rate.

3. The method of claim 2, further comprising:

communicating the selected final insulin infusion rate to the insulin infusion device.

4. The method of claim 1, further comprising:

repeating the operating, the calculating, the determining, and the selecting in accordance with a predetermined schedule to adjust the final insulin infusion rate in an ongoing manner.

5. The method of claim 1, wherein the operating obtains the current IOB value based at least in part on historical bolus delivery data for the user.

6. The method of claim 5, wherein the operating obtains the current IOB value in accordance with a three-compartment insulin pharmacokinetic model.

7. The method of claim 1, wherein calculating the IOB rate comprises:

calculating the IOB rate to be equal to the obtained IOB value multiplied by an IOB decay rate when the obtained IOB value is greater than a minimum IOB value; and

calculating the IOB rate to be equal to zero when the obtained IOB value is less than or equal to the minimum IOB value.

8. The method of claim 1, wherein determining the adjusted insulin infusion rate comprises:

selecting the adjusted insulin infusion rate in accordance with the expression

AdjustedRate(n)=max(0;

PIDRate(n)−

IOBRate(n)), wherein;

AdjustedRate(n) is the selected adjusted insulin infusion rate;

PIDRate(n) is the uncompensated insulin infusion rate; and

IOBRate(n) is the calculated IOB rate.

9. The method of claim 1, wherein selecting the final insulin infusion rate comprises:

selecting the final insulin infusion rate in accordance with the expression

FinalRate(n)=max(Basal;

AdjustedRate(n)) when PIDRate>

Basal, wherein;

FinalRate(n) is the selected final insulin infusion rate;

Basal is the current basal rate;

AdjustedRate(n) is the determined adjusted insulin infusion rate; and

PIDRate(n) is the uncompensated insulin infusion rate.

10. The method of claim 1, wherein selecting the final insulin infusion rate comprises:

selecting the final insulin infusion rate to be equal to the uncompensated insulin infusion rate when the current basal rate is greater than or equal to the uncompensated insulin infusion rate.

11. The method of claim 1, further comprising:

calculating, by the processor architecture, the uncompensated insulin infusion rate in accordance with a proportional-integral-derivative insulin feedback (PID-IFB) control algorithm.

12. A processor-implemented method of controlling an insulin infusion device for a user, the method comprising:

generating, by a processor architecture comprising at least one processor device, a current insulin on board (IOB) value that represents an estimate of active insulin in the body of the user;

calculating, by the processor architecture, an IOB rate based at least in part on the generated current IOB value;

obtaining an uncompensated insulin infusion rate;

determining, by the processor architecture, an adjusted insulin infusion rate in accordance with the expression

AdjustedRate(n)=max(0;

PIDRate(n)−

IOBRate(n)); and

selecting a final insulin infusion rate in accordance with the expression

13. The method of claim 12, further comprising:

communicating the selected final insulin infusion rate to the insulin infusion device to facilitate regulation of insulin delivery by the insulin infusion device.

14. The method of claim 12, further comprising:

repeating the generating, the calculating, the obtaining, the determining, and the selecting in accordance with a predetermined schedule to adjust the final insulin insulin infusion rate in an ongoing manner.

15. The method of claim 12, wherein the generating calculates the current IOB value based at least in part on historical bolus delivery data for the user.

16. The method of claim 12, wherein the generating calculates the current IOB value in accordance with a three-compartment insulin pharmacokinetic model.

17. The method of claim 12, wherein the calculating calculates the IOB rate in accordance with the expression

18. The method of claim 12, wherein the obtaining comprises:

calculating, by the processor architecture, PIDRate(n) in accordance with a proportional-integral-derivative insulin feedback (PID-IFB) control algorithm.

19. A processor-implemented method of controlling an insulin infusion device for a user, the method comprising:

operating the insulin infusion device in a closed-loop mode to deliver insulin to the body of the user;

obtaining current insulin-delivered data that indicates an amount of insulin delivered by the insulin infusion device during a most recent sampling period;

obtaining current sensor data that indicates a current sensor glucose value for the user corresponding to the most recent sampling period;

processing historical insulin-delivered data and historical sensor data, for a plurality of historical sampling periods prior to the most recent sampling period, to obtain predicted sensor glucose values for a historical time period;

calculating a difference between the current sensor glucose value and a predicted current sensor glucose value for the most recent sampling period, wherein the predicted sensor glucose values for the historical time period include the predicted current sensor glucose value; and

generating an alert when the difference exceeds a threshold error amount.

20. The method of claim 19, further comprising:

switching from the closed-loop mode to an open-loop mode in response to generating the alert.

21. The method of claim 19, wherein:

a recent history period corresponds to a period of time from a begin-prediction sampling period to the most recent sampling period, inclusive;

a distant history period corresponds to a period of time from a begin-training sampling period to an end-training sampling period, inclusive;

the distant history period occurred before the recent history period; and

the processing obtains the model-predicted sensor glucose values as a function of a bounded initial condition that is influenced by a baseline sensor glucose value obtained during the distant history period.

22. The method of claim 21, wherein the begin-prediction sampling period corresponds to the end-training sampling period.

23. The method of claim 21, wherein the baseline sensor glucose value is obtained during the begin-training sampling period.

24. The method of claim 19, wherein the processing comprises:

estimating plasma insulin for the user based on the current insulin-delivered data, the historical insulin-delivered data, and an insulin basal rate for the user; and

obtaining the model-predicted sensor glucose values based at least in part on the estimated plasma insulin.

25. The method of claim 19, wherein the processing comprises:

calculating a plurality of candidate solutions to a sensor glucose prediction model, wherein each of the plurality of candidate solutions is calculated as a function of a bounded initial condition, and wherein the bounded initial condition is influenced by a baseline sensor glucose value, wherein the historical sensor data includes the baseline sensor glucose value; and

selecting a best-matched solution from the calculated plurality of candidate solutions for use as the model-predicted sensor glucose values.

26. A processor-implemented method of controlling an insulin infusion device for a user, the method comprising:

operating the insulin infusion device in a closed-loop mode to deliver insulin to the body of the user;

identifying, from historical sensor glucose values for the user, a baseline historical sensor glucose value obtained during a begin-training sampling period;

calculating a plurality of candidate solutions to a sensor glucose prediction model, wherein each of the plurality of candidate solutions is calculated as a function of a bounded initial condition and historical insulin-delivered data for the user, and wherein the bounded initial condition is influenced by the baseline sensor glucose value;

selecting a best-matched solution from the calculated plurality of candidate solutions, based on a comparison of predicted sensor glucose values from the calculated plurality of candidate solutions to a first portion of the historical sensor glucose values;

comparing at least one predicted sensor glucose value from the best-matched solution to a second portion of the historical sensor glucose values, wherein the first portion of the historical sensor glucose values corresponds to a distant history period, the second portion of the historical sensor glucose values corresponds to a recent history period, and the distant history period occurred before the recent history period that data samples; and

generating an alert, in response to the comparing, when the second portion of the historical sensor glucose values deviates from the best-matched solution by at least a threshold error amount.

27. The method of claim 26, further comprising:

switching from the closed-loop mode to an open-loop mode in response to generating the alert.

28. The method of claim 26, wherein the calculating comprises:

estimating plasma insulin for the user based on at least some of the historical insulin-delivered data for the user, and based on an insulin basal rate for the user, wherein each of the plurality of candidate solutions is calculated based at least in part on the estimated plasma insulin.

29. A processor-implemented method of controlling an insulin infusion device for a user, the method comprising:

operating the insulin infusion device in a closed-loop mode to deliver insulin to the body of the user;

defining a model training period and a model prediction period for a historical period of time;

finding a best-matched solution to a sensor glucose prediction model, relative to historical sensor glucose values obtained during the model training period, wherein the best-matched solution is a function of a baseline sensor glucose value obtained during the model training period, and is a function of historical insulin-delivered data for the user obtained during the historical period of time;

comparing at least one predicted sensor glucose value from the best-matched solution to at least one historical sensor glucose value corresponding only to the model prediction period; and

generating an alert, in response to the comparing, when the at least one historical sensor glucose value deviates from the at least one predicted sensor glucose value by at least a threshold error amount.

30. The method of claim 29, further comprising:

switching from the closed-loop mode to an open-loop mode in response to generating the alert.

31. The method of claim 29, wherein the comparing compares a most recent historical sensor glucose value to a most recent predicted sensor glucose value from the best-matched solution.

32. The method of claim 29, wherein the model prediction period immediately follows the model training period.

33. The method of claim 29, wherein finding the best-matched solution comprises:

calculating a plurality of candidate solutions to the sensor glucose prediction model;

comparing predicted sensor glucose values from the calculated plurality of candidate solutions to corresponding historical sensor glucose values obtained only during the model training period, to obtain a respective training error for each of the calculated plurality of candidate solutions; and

selecting the best-matched solution from the calculated plurality of candidate solutions, based on the training error.