Systems and methods for providing sensitive and specific alarms

• US 9,119,528 B2
• Filed: 01/16/2013
• Issued: 09/01/2015
• Est. Priority Date: 10/30/2012
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Hampapuram, Hari, Rack-Gomer, Anna Leigh, Bhavaraju, Naresh C., Kamath, Apurv Ullas, Cobelli, Claudio, Sparacino, Giovanni, Facchinetti, Andrea, Zecchin, Chiara
• Primary Examiner(s): MCNALLY, KERRI L
• Application Number:
  US13/742,694
• Publication Number:
  US 20140118138A1
• Time in Patent Office: 958 Days
• Field of Search: 340/539.12, 340/573.1
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• 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 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• 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: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Systems and methods for providing sensitive and specific alarms indicative of glycemic condition are provided herein. In an embodiment, a method of processing sensor data by a continuous analyte sensor includes: evaluating sensor data using a first function to determine whether a real time glucose value meets a first threshold; evaluating sensor data using a second function to determine whether a predicted glucose value meets a second threshold; activating a hypoglycemic indicator if either the first threshold is met or if the second threshold is predicted to be met; and providing an output based on the activated hypoglycemic indicator.

Claims

1. A method of activating a hypoglycemic indicator based on continuous glucose sensor data, the method comprising:

evaluating sensor data using a first function to determine whether a real time glucose value meets one or more user settable first criteria;

evaluating sensor data using a second function to determine whether a predicted glucose value meets one or more non user settable second criteria;

activating a hypoglycemic indicator if either the one or more user settable first criteria or the one or more non user settable second criteria are met; and

providing an output based on the activated hypoglycemic indicator.

2. The method of claim 1, wherein the evaluating sensor data using a first function to determine whether a real time glucose value meets one or more user settable first criteria comprises determining whether the real-time glucose value passes a glucose threshold.

3. The method of claim 2, wherein the evaluating sensor data using a first function to determine whether a real time glucose value meets one or more user settable first criteria further comprises determining whether an amplitude of rate of change or direction of rate of change meets a rate of change criterion.

4. The method of claim 1, wherein the evaluating sensor data using a first function to determine whether the real time glucose value meets one or more user settable first criteria comprises evaluating a static risk of a substantially real time glucose value.

5. The method of claim 1, wherein the evaluating sensor data using a second function to determine whether the predicted glucose value meets one or more second criteria comprises evaluating a dynamic risk of the predicted glucose value.

6. The method of claim 1, wherein the second function comprises an artificial neural network model that uses at least one of exercise, stress, illness or surgery to determine the predicted glucose value.

7. The method of claim 1, wherein the second function utilizes a first order autoregressive model to determine the predicted glucose value.

8. The method of claim 7, wherein the first order autoregressive model comprises a parameter alpha, and wherein alpha is estimated recursively each time a sensor data points is received.

9. The method of claim 1, wherein evaluating the sensor data using the first function and the second function allows for increased warning time of hypoglycemic alerts without substantially increasing the number of alerts as compared to evaluating the sensor data using the first function without evaluating the sensor data using the second function.

10. The method of claim 1, wherein the second function comprises a Kalman Filter to determine the predicted glucose value using as an input an estimate of the rate of change of the blood glucose.

11. The method of claim 1, wherein the one or more user settable first criteria comprises a first threshold that is configured to be user settable.

12. The method of claim 11, wherein the one or more non user settable second criteria comprises a second threshold that is not user settable.

13. The method of claim 1, wherein the one or more non user settable second criteria comprises a prediction horizon.

14. The method of claim 1, wherein the one or more non user settable second criteria comprises a second threshold that is adaptively set by a processor module based on the one or more user settable first criteria.

15. The method of claim 1, wherein the one or more non user settable second criteria comprises a prediction horizon that is adaptively set by a processor module based on the first user settable criteria.

16. The method of claim 1, wherein the hypoglycemic indicator comprises a flag that has a particular set of instructions associated with it depending on whether the hypoglycemic indicator was activated based on the first function meeting the one or more user settable first criteria or whether the hypoglycemic indicator was activated based on the second function meeting the one or more non user settable second criteria.

17. The method of claim 1, wherein the output comprises at least one of an audible, tactile or visual output, and wherein the output is differentiated and/or provides information selectively based on whether the hypoglycemic indicator was activated based on the first function meeting the one or more user settable first criteria or whether the hypoglycemic indicator was activated based on the second function meeting the one or more non user settable second criteria.

18. The method of claim 1, wherein providing an output comprises transmitting a message to an insulin delivery device including instructions associated with at least one of:

a) suspending insulin delivery, b) initiating a hypoglycemia and/or hyperglycemia minimizer algorithm, c) controlling insulin delivery or d) information associated with the hypoglycemia indicator.

19. The method of claim 1, wherein the one or more non user settable second criteria are programmed at the factory in such a way that no user is able to modify the second criteria.

20. The method of claim 1, wherein the user is a host or a healthcare provider.

21. The method of claim 1, wherein the second function provides no more than one additional alarm per week based on a retrospective analysis comparing the use of the first function and the second function together as compared to the first function alone.

22. The method of claim 1, wherein evaluating the sensor data using the first function and the second function provides increased warning time of hypoglycemic alerts without substantially increasing the number of alerts as compared to evaluating the sensor data using the first function without evaluating the sensor data using the second function.

23. A method of activating a hypoglycemic indicator based on continuous glucose sensor data, the method comprising:

evaluating sensor data using a first function to determine whether a real time glucose value meets one or more user settable first criteria;

evaluating sensor data using a second function to determine whether a predicted glucose value meets one or more non user settable second criteria, wherein the second function utilizes a first order autoregressive model to determine the predicted glucose value, wherein the first order autoregressive model comprises a forgetting factor, a prediction horizon and a prediction threshold tuned to provide no more than one additional alarm per week based on a retrospective analysis comparing the use of the first function and the second function together as compared to the first function alone;

activating a hypoglycemic indicator if either the one or more user settable first criteria or the one or more non user settable second criteria are met; and

providing an output based on the activated hypoglycemic indicator.

24. A system for processing data, the system comprising:

a continuous analyte sensor configured to be implanted within a body; and

sensor electronics configured to receive and process sensor data output by the sensor, the sensor electronics including a processor configured to;

evaluate sensor data using a first function to determine whether a real time glucose value meets one or more user settable first criteria;

evaluate sensor data using a second function to determine whether a predicted glucose value meets one or more non-user settable second criteria;

activate a hypoglycemic indicator if either the one or more user settable first criteria or the one or more non user settable second criteria are met; and

provide an output based on the activated hypoglycemic indicator.

25. The system of claim 24, wherein the evaluating sensor data using a first function to determine whether a real time glucose value meets one or more user settable first criteria comprises determining whether the real-time glucose value passes a glucose threshold.

26. The system of claim 25, wherein the evaluating sensor data using a first function to determine whether a real time glucose value meets one or more user settable first criteria further comprises determining whether an amplitude of rate of change or direction of rate of change meets a rate of change criterion.

27. The system of claim 24, wherein the evaluating sensor data using a first function to determine whether the real time glucose value meets one or more user settable first criteria comprises evaluating a static risk of a substantially real time glucose value.

28. The system of claim 24, wherein the evaluating sensor data using a second function to determine whether the predicted glucose value meets one or more non user settable second criteria comprises evaluating a dynamic risk of the predicted glucose value.

29. The system of claim 24, wherein the second function comprises an artificial neural network model that uses at least one of exercise, stress, illness or surgery to determine the predicted glucose value.

30. The system of claim 24, wherein the second function utilizes a first order autoregressive model to determine the predicted glucose value.

31. The system of claim 30, wherein the first order autoregressive model comprises a parameter alpha, and wherein alpha is estimated recursively each time a sensor data points is received.

32. The system of claim 24, wherein evaluating the sensor data using the first function and the second function allows for increased warning time of hypoglycemic alerts without substantially increasing the number of alerts as compared to evaluating the sensor data using the first function without evaluating the sensor data using the second function.

33. The system of claim 24, wherein the second function comprises a Kalman Filter to determine the predicted glucose value using as an input an estimate of the rate of change of the blood glucose.

34. The system of claim 24, wherein the one or more user settable first criteria comprises a first threshold that is configured to be user settable.

35. The system of claim 34, wherein the one or more non user settable second criteria comprises a second threshold that is not user settable.

36. The system of claim 24, wherein the one or more non user settable second criteria comprises a prediction horizon that is not user settable.

37. The system of claim 24, wherein the one or non user settable second criteria comprises a second threshold that is adaptively set by a processor module based on the one or more user settable first criteria.

38. The system of claim 24, wherein the one or more non user settable second criteria comprises a prediction horizon that is adaptively set by the processor module based on the one or more user settable first criteria.

39. The system of claim 24, wherein the hypoglycemic indicator comprises a flag that has a particular set of instructions associated with it depending on whether the hypoglycemic indicator was activated based on the first function meeting the one or more user settable first criteria or whether the hypoglycemic indicator was activated based on the second function meeting the one or more non user settable second criteria.

40. The system of claim 24, wherein the output comprises at least one of an audible, tactile or visual output, and wherein the output is differentiated and/or provides information selectively based on whether the hypoglycemic indicator was activated based on the first function meeting the one or more user settable first criteria or whether the hypoglycemic indicator was activated based on the second function meeting the one or more non user settable second criteria.

41. The system of claim 24, wherein providing output comprises transmitting a message to an insulin delivery device including instructions associated with at least one of:

a) suspending insulin delivery, b) initiating a hypoglycemia and/or hyperglycemia minimizer algorithm, c) controlling insulin delivery or d) information associated with the hypoglycemia indicator.

42. The system of claim 24, wherein the one or more non user settable second criteria are programmed at the factory in such a way that no user is able to modify the non user settable second criteria.

43. The system of claim 24, wherein the user is a host or a healthcare provider.

45. The system of claim 24, wherein the evaluation of the sensor data using the first function and the second function provides increased warning time of hypoglycemic alerts without substantially increasing the number of alerts as compared to evaluation of the sensor data using the first function without evaluation of the sensor data using the second function.

44. The system of 24, wherein the second function provides no more than one additional alarm per week based on a retrospective analysis comparing the use of the first function and the second function together as compared to the first function alone.

46. A system for processing data, the system comprising:

a continuous analyte sensor configured to be implanted within a body; and

sensor electronics configured to receive and process sensor data output by the sensor, the sensor electronics including a processor configured to;

evaluate sensor data using a first function to determine whether a real time glucose value meets one or more user settable first criteria;

evaluate sensor data using a second function to determine whether a predicted glucose value meets one or more non-user settable second criteria, wherein the second function utilizes a first order autoregressive model to determine the predicted glucose value, wherein the first order autoregressive model comprises a forgetting factor, a prediction horizon and a prediction threshold tuned to provide no more than one additional alarm per week based on a retrospective analysis comparing the use of the first function and the second function together as compared to the first function alone;

activate a hypoglycemic indicator if either the one or more user settable first criteria or the one or more non user settable second criteria are met; and

provide an output based on the activated hypoglycemic indicator.