Method and Device for Providing Offset Model Based Calibration for Analyte Sensor

• US 20100191085A1
• Filed: 01/29/2009
• Published: 07/29/2010
• Est. Priority Date: 01/29/2009
• Status: Active Grant

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Inventors: Budiman, Erwin Satrya
• Granted Patent: US 8,224,415 B2
• Time in Patent Office: Days
• US Class Current: 600/365
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14865 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: Y10T 436/144444 Glucose

Abstract

Methods and devices to detect analyte in body fluid are provided. Embodiments include processing sampled data from analyte sensor, determining a single, fixed, normal sensitivity value associated with the analyte sensor, estimating a windowed offset value associated with the analyte sensor for each available sampled data cluster, computing a time varying offset based on the estimated windowed offset value, and applying the time varying offset and the determined normal sensitivity value to the processed sampled data to estimate an analyte level for the sensor.

Claims

1. A method, comprising:

processing sampled data from analyte sensor;

determining a single, fixed, normal sensitivity value associated with the analyte sensor;

estimating a windowed offset value associated with the analyte sensor for each available sampled data cluster;

computing a time varying offset based on the estimated windowed offset value; and

applying the time varying offset and the determined normal sensitivity value to the processed sampled data to estimate an analyte level for the sensor.

2. The method of claim 1 wherein processing the sampled data includes performing retrospective lag correction of the sampled data.

3. The method of claim 1 wherein processing the sampled data includes smoothing the sampled data from the analyte sensor.

4. The method of claim 1 wherein processing the sampled data includes performing temperature correction to the sampled data.

5. The method of claim 1 wherein determining the normal sensitivity includes pairing the sampled data from the analyte sensor with one or more time corresponding reference measurement values.

6. The method of claim 5 wherein the one or more reference measurement values includes a blood glucose measurement.

7. The method of claim 5 including determining an immediate sensitivity value for each paired sampled data and the one or more time corresponding reference measurement values.

8. The method of claim 7 including estimating a latest sensitivity based on the determined immediate sensitivity for each time window.

9. The method of claim 8 including defining a subset of the estimated latest sensitivities associated with a subset of the total available time windows corresponding to the respective paired sampled data and the one or more time corresponding reference measurement values.

10. The method of claim 9 including weighted averaging the subset of estimated latest sensitivities to determine the normal sensitivity associated with the analyte sensor.

11. The method of claim 10 including confirming the determined normal sensitivity.

12. The method of claim 11 wherein confirming the determined normal sensitivity includes comparing the determined normal sensitivity to a predetermined value.

13. The method of claim 12 wherein the predetermined value includes a median sensitivity value determined based on the immediate sensitivity associated with each time window.

14. An apparatus, comprising:

a data communication interface;

one or more processors operatively coupled to the data communication interface; and

a memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to process sampled data from analyte sensor, determine a single, fixed, normal sensitivity value associated with the analyte sensor, estimate a windowed offset value associated with the analyte sensor for each available sampled data cluster, compute a time varying offset based on the estimated windowed offset value, and apply the time varying offset and the determined normal sensitivity value to the processed sampled data to estimate an analyte level for the sensor.

15. The apparatus of claim 14 wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to perform retrospective lag correction of the sampled data.

16. The apparatus of claim 14 wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to smooth the sampled data from the analyte sensor.

17. The apparatus of claim 14 wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to perform temperature correction for the sampled data.

18. The apparatus of claim 14 wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to pair the sampled data from the analyte sensor with one or more time corresponding reference measurement values.

19. The apparatus of claim 18 wherein the one or more reference measurement values includes a blood glucose measurement.

20. The apparatus of claim 18 wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to determine an immediate sensitivity value for each paired sampled data and the one or more time corresponding reference measurement values.

21. The apparatus of claim 20 wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to estimate a latest sensitivity based on the determined immediate sensitivity for each time window.

22. The apparatus of claim 21 wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to define a subset of the estimated latest sensitivities associated with a subset of the total available time windows corresponding to the respective paired sampled data and the one or more time corresponding reference measurement values.

23. The apparatus of claim 22 wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to perform weighted averaging of the subset of estimated latest sensitivities to determine the normal sensitivity associated with the analyte sensor.

24. The apparatus of claim 23 wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to confirm the determined normal sensitivity.

25. The apparatus of claim 24 wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to compare the determined normal sensitivity to a predetermined value.

26. The apparatus of claim 25 wherein the predetermined value includes a median sensitivity value determined based on the immediate sensitivity associated with each time window.

27. One or more storage devices having processor readable code embodied thereon, said processor readable code for programming one or more processors to estimate analyte level, comprising:

processing sampled data from analyte sensor;

determining a single, fixed, normal sensitivity value associated with the analyte sensor;

estimating a windowed offset value associated with the analyte sensor for each available sampled data cluster;

computing a time varying offset based on the estimated windowed offset value; and

applying the time varying offset and the determined normal sensitivity value to the processed sampled data to estimate an analyte level for the sensor.