End of life detection for analyte sensors

• US 9,211,092 B2
• Filed: 01/03/2013
• Issued: 12/15/2015
• Est. Priority Date: 01/03/2013
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Bhavaraju, Naresh C., Garcia, Arturo, Hampapuram, Hari, Kamath, Apurv Ullas, Mahalingam, Aarthi, Sokolovskyy, Dmytro, Vanslyke, Stephen J.
• Primary Examiner(s): McCall, Eric S
• Assistant Examiner(s): Megna Fuentes, Anthony W
• Application Number:
  US13/733,742
• Publication Number:
  US 20140182350A1
• Time in Patent Office: 1,076 Days
• Field of Search: 73 101- 189, 600/316, 600/319, 600/347, 600364-365, 600323-340
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0266 for monitoring or limiting apparatus function
• CPC Class Codes: A61B 2560/0276 Determining malfunction
• CPC Class Codes: A61B 2560/028 Arrangements to prevent overuse, e.g. by counting the number of uses
• 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: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: G01M 99/008 by doing functionality tests
• CPC Class Codes: G01N 33/48792 Data management, e.g. communication with processing unit for in vivo diagnostics A61B5/0002; transmission systems for measured values G08C
• CPC Class Codes: G01N 33/49 Blood chemical methods for determining blood cell populations G01N33/5094; chemical analysis of blood groups or blood types G01N33/80
• CPC Class Codes: G01N 33/66 involving blood sugars, e.g. galactose
• CPC Class Codes: G16C 20/80 Data visualisation
• CPC Class Codes: G16C 99/00 Subject matter not provided for in other groups of this subclass

Abstract

Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. The plurality of risk factors may be selected from the list including the number of days the sensor has been in use, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and error between reference BG values and EGV sensor values.

Claims

1. A system for determining an end of life of a continuous analyte sensor, the system comprising sensor electronics configured to be operably connected to a continuous analyte sensor, the sensor electronics configured to:

evaluate a plurality of risk factors associated with end of life symptoms of a sensor;

determine an end of life status of the sensor based on the evaluation of the plurality of risk factors; and

provide an output related to the end of life status of the sensor,

wherein the plurality of risk factors comprise at least two risk factors selected from the group consisting of a number of days the sensor has been in use a rate of change of sensor sensitivity, end of life noise, oxygen concentration, glucose patterns, error between reference values and sensor values in clinical units, wherein one of the at least two risk factors comprises a rate of change of sensor sensitivity, and wherein the sensor electronics are configured to evaluate a rate of change of sensor sensitivity by evaluating at least one of a direction of rate of change of sensor sensitivity, an amplitude of rate of change of sensor sensitivity, a derivative of rate of change of sensor sensitivity, or a comparison of the rate of change of sensor sensitivity to a priori rate of change sensitivity information.

2. The system of claim 1, wherein one of the at least two risk factors comprises a number of days the sensor has been in use, and wherein the sensor electronics are configured to evaluate a number of days the sensor has been in use by at least one of evaluating an amount of time since the sensor has been initialized, evaluating an amount of time since the sensor has been implanted, or evaluating an amount of time since the sensor has been initially calibrated.

3. The system of claim 1, wherein one of the at least two risk factors comprises end of life noise, and wherein the sensor electronics are configured to evaluate end of life noise by evaluating at least one of duration of noise, a magnitude of noise, a history of noise, a spectral content of a signal from the sensor, spikes in the signal from the sensor, skewness of the signal of the sensor, or noise patterns by pattern recognition algorithms.

4. The system of claim 1, wherein one of the at least two risk factors comprises end of life noise, and wherein the sensor electronics are configured to evaluate end of life noise by evaluating at least two of duration of noise, a magnitude of noise, a history of noise, a spectral content of a signal from the sensor, spikes in the signal from the sensor, skewness of the signal of the sensor, or noise patterns by pattern recognition algorithms.

5. The system of claim 1, wherein one of the at least two risk factors comprises glucose patterns, and wherein the sensor electronics are configured to evaluate glucose patterns by evaluating at least one of mean glucose, glucose variability, peak-to-peak glucose excursions, or expected versus unexpected glucose trends based on timing.

6. The system of claim 1, wherein one of the at least two risk factors comprises error between reference values and sensor values in clinical units, and wherein the sensor electronics are configured to evaluate error between reference values and sensor values in clinical units by evaluating at least one of a direction of error between reference values and sensor values in clinical units, or a linearity of the sensor and an error at calibration.

7. The system of claim 1, wherein the sensor electronics are configured to evaluate a plurality of risk factors by translating outputs of the plurality of risk factor evaluations to end of life risk factor values.

8. The system of claim 7, wherein the sensor electronics are configured to translate an output of end of life risk factor values by determining a likelihood of recovery.

9. The system of claim 7, wherein the sensor electronics are configured to determine an end of life status by combining the end of life risk factor values into a combined end of life score.

11. The system of claim 9, wherein the sensor electronics are configured to combine the end of life risk factor values into a combined end of life score by weighting one or more of the plurality of risk factors.

12. The system of claim 1, wherein each of the plurality of risk factors is partially indicative of the end of life of the sensor based on a comparison of the risk factor to one or more criteria.

14. The system of claim 1, wherein the sensor electronics are configured to determine the end of life status by using a probability analysis, a decision fusion, linear discriminant analysis, or fuzzy logic.

15. The system of claim 1, wherein the output related to the end of life status is displayed on a user interface.

16. The system of claim 1, wherein the output related to the end of life status comprises instructions to change the sensor.

17. The system of claim 1, wherein the output related to the end of life status comprises a data transmission.

10. A system for determining an end of life of a continuous analyte sensor, the system comprising sensor electronics configured to be operably connected to a continuous analyte sensor, the sensor electronics configured to:

evaluate a plurality of risk factors associated with end of life symptoms of a sensor;

determine an end of life status of the sensor based on the evaluation of the plurality of risk factors; and

provide an output related to the end of life status of the sensor,

wherein the plurality of risk factors comprise at least two risk factors selected from the group consisting of a number of days the sensor has been in use a rate of change of sensor sensitivity, end of life noise, oxygen concentration, glucose patterns, error between reference values and sensor values in clinical units, wherein the sensor electronics are configured to evaluate a plurality of risk factors by translating outputs of the plurality of risk factor evaluations to end of life risk factor values, wherein the sensor electronics are configured to determine an end of life status by combining the end of life risk factor values into a combined end of life score, and wherein the sensor electronics are configured to determine an end of life status based on the combined end of life score.

13. A system for determining an end of life of a continuous analyte sensor, the system comprising sensor electronics configured to be operably connected to a continuous analyte sensor, the sensor electronics configured to:

evaluate a plurality of risk factors associated with end of life symptoms of a sensor;

determine an end of life status of the sensor based on the evaluation of the plurality of risk factors; and

provide an output related to the end of life status of the sensor,

wherein the plurality of risk factors comprise at least two risk factors selected from the group consisting of a number of da s the sensor has been in use a rate of change of sensor sensitivity, end of life noise, oxygen concentration, glucose patterns, error between reference values and sensor values in clinical units, wherein each of the plurality of risk factors is partially indicative of the end of life of the sensor based on a comparison of the risk factor to one or more criteria, and wherein if at least two of the plurality of risk factors are determined to meet the one or more criteria, respectively, then a combination of the at least two variables is indicative of the end of life of the sensor.

18. A method for determining an end of life of a continuous analyte sensor, comprising:

evaluating a plurality of risk factors associated with end of life symptoms of a sensor;

determining an end of life status of the sensor based on the evaluation of the plurality of risk factors; and

providing an output related to the end of life status of the sensor,

wherein the plurality of risk factors comprise at least two risk factors selected from the group consisting of a number of days the sensor has been in use, a rate of change of sensor sensitivity, end of life noise, oxygen concentration, glucose patterns, error between reference values, and sensor values in clinical units, wherein one of the at least two risk factors comprises a rate of change of sensor sensitivity and wherein sensor electronics are configured to evaluate a rate of change of sensor sensitivity by evaluating at least one of a direction of rate of change of sensor sensitivity, an amplitude of rate of change of sensor sensitivity, a derivative of rate of change of sensor sensitivity, or a comparison of the rate of change of sensor sensitivity to a priori rate of change sensitivity information.

19. The method of claim 18, wherein one of the at least two risk factors comprises a number of days the sensor has been in use, and wherein evaluating a number of days the sensor has been in use comprises at least one of evaluating an amount of time since the sensor has been initialized, evaluating an amount of time since the sensor has been implanted, or evaluating an amount of time since the sensor has been initially calibrated.

20. The method of claim 18, wherein one of the at least two risk factors comprises end of life noise, and wherein evaluating end of life noise comprises evaluating at least one of duration of noise, a magnitude of noise, a history of noise, a spectral content of a signal from the sensor, spikes in the signal from the sensor, skewness of the signal of the sensor or noise patterns by pattern recognition algorithms.

21. The method of claim 18, wherein one of the at least two risk factors comprises end of life noise, and wherein evaluating end of life noise comprises evaluating at least two of duration of noise, a magnitude of noise, a history of noise, a spectral content of a signal from the sensor, spikes in the signal from the sensor, skewness of the signal of the sensor or noise patterns by pattern recognition algorithms.

22. The method of claim 18, wherein one of the at least two risk factors comprises glucose patterns, and wherein evaluating glucose patterns comprises evaluating at least one of mean glucose, glucose variability, peak-to-peak glucose excursions, or expected versus unexpected glucose trends based on timing.

23. The method of claim 18, wherein one of the at least two risk factors comprises error between reference values and sensor values in clinical units, and wherein evaluating error between reference values and sensor values in clinical units comprises evaluating at least one of a direction of error between reference values and sensor values in clinical units, a linearity of the sensor, or an error at calibration.

24. The method of claim 18, wherein the evaluating a plurality of risk factors comprises translating outputs of the plurality of risk factor evaluations to end of life risk factor values.

25. The method of claim 24, wherein translating an output of end of life risk factor values comprises determining a likelihood of recovery.

26. The method of claim 24, wherein the determining an end of life status comprises combining the end of life risk factor values into a combined end of life score.

27. The method of claim 26, wherein the determining an end of life status is based on the combined end of life score.

28. The method of claim 26, wherein combining the end of life risk factor values into a combined end of life score comprises weighting one or more of the plurality of risk factors.

29. The method of claim 18, wherein each of the plurality of risk factors is partially indicative of the end of life of the sensor based on a comparison of the risk factor to one or more criteria.

30. The method of claim 29, wherein if at least two of the plurality of risk factors are determined to meet the one or more criteria, respectively, then the combination of the at least two variables is indicative of the end of life of the sensor.

31. The method of claim 18, wherein determining the end of life status comprises using a probability analysis, a decision fusion, linear discriminant analysis or fuzzy logic.

32. The method of claim 18, wherein the output related to the end of life status is displayed on a user interface.

33. The method of claim 18, wherein the output related to the end of life status comprises instructions to change the sensor.

34. The method of claim 18, wherein the output related to the end of life status comprises a data transmission.

35. The method of claim 18, wherein the method is implemented on a computer having a processor and a memory coupled to said processor, wherein at least one of the evaluating, the determining, and the providing is performed using the processor.

36. A method for determining an end of life of a continuous analyte sensor, comprising:

evaluating a plurality of risk factors associated with end of life symptoms of a sensor;

determining an end of life status of the sensor based on the evaluation of the plurality of risk factors; and

providing an output related to the end of life status of the sensor,

wherein the plurality of risk factors comprise at least two risk factors selected from the group consisting of a number of days the sensor has been in use, a rate of change of sensor sensitivity, end of life noise, oxygen concentration, glucose patterns, error between reference values, and sensor values in clinical units, wherein sensor electronics are configured to evaluate a plurality of risk factors by translating outputs of the plurality of risk factor evaluations to end of life risk factor values, wherein the sensor electronics are configured to determine an end of life status by combining the end of life risk factor values into a combined end of life score, and wherein the sensor electronics are configured to determine an end of life status based on the combined end of life score.

37. A method for determining an end of life of a continuous analyte sensor, comprising:

evaluating a plurality of risk factors associated with end of life symptoms of a sensor;

determining an end of life status of the sensor based on the evaluation of the plurality of risk factors; and

providing an output related to the end of life status of the sensor,

wherein the plurality of risk factors comprise at least two risk factors selected from the group consisting of a number of days the sensor has been in use, a rate of change of sensor sensitivity, end of life noise, oxygen concentration, glucose patterns, error between reference values, and sensor values in clinical units, wherein each of the plurality of risk factors is partially indicative of the end of life of the sensor based on a comparison of the risk factor to one or more criteria, and wherein if at least two of the plurality of risk factors are determined to meet the one or more criteria, respectively, then a combination of the at least two variables is indicative of the end of life of the sensor.