Granted Patent B2

US 11,672,450 · App. 16/142,664 · Granted Jun 13, 2023

Inventors: Xiaoxiao Chen (Washington, DC); Ravi Rastogi (Columbia, MD); Andrew DeHennis (Germantown, MD); Patricia Sanchez (Germantown, MD)

Assignee: Senseonics, Incorporated

A61B5/1495A61B5/1451A61B5/1459A61B5/14532A61B5/742G01N33/66A61B5/14503A61B5/4866A61B2560/0223G16H20/10

Abstract

Claims (195)

1. A method of calibrating an analyte sensor using one or more reference measurements, the method comprising:

receiving first sensor data from an analyte sensor, wherein the first sensor data comprises one or more measurements of an analyte level in a second medium;

using a conversion function and the first sensor data to calculate a first sensor analyte level, wherein the calculated first sensor analyte level is a level of analyte in a first medium, and using the conversion function and the first sensor data used to calculate the first sensor analyte measurement comprises:

calculating a second medium analyte level using at least the first sensor data;

calculating a second medium level rate of change using at least the second medium analyte level; and

calculating the first sensor analyte level using at least the second medium analyte level, the second medium level rate of change, and one or more lag parameters;

receiving a first reference analyte measurement (RM 1 );

storing the RM 1 in a calibration point memory that includes one or more previous reference analyte measurements;

weighting the RM 1 and the one or more previous reference analyte measurements according to a weighted average cost function;

updating the conversion function using the weighted RM 1 and the one or more weighted previous reference analyte measurements as calibration points;

receiving second sensor data from the analyte sensor; and

using the updated conversion function and the second sensor data to calculate a second sensor analyte level.

2. The method of claim 1 , wherein the RM 1 is a self-monitoring blood glucose (SMBG) measurement obtained from a finger-stick blood sample.

3. The method of claim 1 , wherein the calibration point memory is a circular buffer.

4. The method of claim 1 , wherein the weightings for the weighted RM 1 and the one or more weighted previous reference analyte measurements are calculated using an exponential growth formula.

5. The method of claim 4 , wherein the exponential growth formula includes a growth parameter α defined as

α

=

(

t

i

-

t

0

λ

)

,

t 0 is the time stamp of the current calibration point, i equals

−(N−1), −(N−2), . . . 0, N is the number of calibration points, and λ is the relative time difference between the current and the previous calibration points.

6. The method of claim 5 , wherein N is a constant value.

7. The method of claim 5 , wherein λ is a constant value.

8. The method of claim 1 , wherein the weighted average cost function includes an accuracy metric, Error(θ) i .

9. The method of claim 1 , wherein the first medium is blood, and the second medium is interstitial fluid.

10. The method of claim 1 , wherein the one or more lag parameters include one or more of an analyte diffusion rate and an analyte consumption rate.

11. The method of claim 1 , further comprising determining whether to update one or more of the lag parameters.

12. The method of claim 11 , further comprising updating one or more of the lag parameters.

13. The method of claim 11 , wherein determining whether to update one or more of the lag parameters comprises determining whether a period of time has passed since the one or more of the lag parameters has been updated.

14. The method of claim 1 , further comprising updating one or more of the lag parameters.

15. The method of claim 14 , wherein updating one or more lag parameters comprises using one or more of a first method and a second method to estimate one or more updated lag parameters.

16. The method of claim 15 , wherein the first method is a ratio method.

17. The method of claim 15 , wherein the second method is a two-parameter method.

18. The method of claim 15 , wherein updating one or more lag parameters comprises using the first method during a first period and using the second method during a second period.

19. The method claim 15 , wherein updating one or more lag parameters comprises using both the first and second methods.

20. The method of claim 19 , wherein using both the first and second methods comprises:

using the first method to estimate a first set of updated lag parameters;

using the second method to estimate a second set of updated lag parameters;

using the first set of updated lag parameters to calculate one or more first sensor measurements;

using the second set of updated lag parameters to calculate one or more second sensor measurements;

evaluating the one or more first sensor measurements and the one or more second sensor measurements by comparing the one or more first sensor measurements and the one or more second sensor measurements to one or more reference measurements; and

selecting the more accurate of (a) the one or more first sensor measurements and (b) the one or more second sensor measurements for display to a user.

21. The method of claim 1 , wherein the first sensor analyte level is calculated using a two-compartment model that models the transport of the analyte from the first medium and in the second medium.

22. The method of claim 21 , wherein the two-compartment model is

dC

2

dt

=

p

2

*

[

C

1

(

t

)

-

C

2

(

t

)

]

-

p

3

*

C

2

(

t

)

,

wherein C 1 (t) is a concentration of the analyte in the first medium, C 2 (t) is a concentration of the analyte in the second medium, p2 is an analyte diffusion rate, and p3 is an analyte consumption rate.

23. The method of claim 22 , wherein 1/p2 and p3/p2 are lag parameters.

24. The method of claim 1 , further comprising determining whether to dynamically update one or more of the lag parameters.

25. The method of claim 24 , further comprising dynamically updating one or more of the lag parameters.

26. The method of claim 25 , wherein dynamically updating one or more of the lag parameters comprises using a minimum deviation divergence method.

27. The method of claim 1 , further comprising dynamically updating one or more of the lag parameters.

28. The method of claim 1 , wherein the conversion function employs an asymmetrical lag methodology.

29. The method of claim 28 , wherein the asymmetrical lag approach decelerates a rate of change of falling glucose levels during a low blood glucose event and accelerates a rate of change of increasing glucose levels during recovery from the low blood glucose event.

30. An analyte monitoring system comprising:

an analyte sensor including an indicator element that exhibits one or more detectable properties based on a concentration of an analyte in proximity to the indicator element; and

a transceiver configured to:

receive first sensor data from the analyte sensor, wherein the first sensor data comprises one or more measurements of the analyte level in a second medium;

use a conversion function and the first sensor data to calculate a first sensor analyte level, wherein the calculated first sensor analyte level is a level of the analyte in a first medium, and the transceiver, in using the conversion function and the first sensor data to calculate the first sensor analyte level, is configured to:

calculate a second medium analyte level using at least the first sensor data;

calculate a second medium level rate of change using at least the second medium analyte level; and

calculate the first sensor analyte level using at least the second medium analyte level, the second medium level rate of change, and one or more lag parameters;

receive a first reference analyte measurement (RM 1 );

store the RMI in a calibration point memory that includes one or more previous reference analyte measurements;

weight the RM 1 and the one or more previous reference analyte measurements according to a weighted average cost function;

update the conversion function using the weighted RM 1 and the one or more weighted previous reference analyte measurements as calibration points;

receive second sensor data from the analyte sensor; and

use the updated conversion function and the second sensor data to calculate a second sensor analyte level.

31. The analyte monitoring system of claim 30 , wherein the RM 1 is a self-monitoring blood glucose (SMBG) measurement obtained from a finger-stick blood sample.

32. The analyte monitoring system of claim 30 , wherein the calibration point memory is a circular buffer.

33. The analyte monitoring system of claim 30 , wherein the weightings for the weighted RM 1 and the one or more weighted previous reference analyte measurements are calculated using an exponential growth formula.

34. The analyte monitoring system of claim 33 , wherein the exponential growth formula includes a growth parameter α defined as

α

=

(

t

i

-

t

0

λ

)

,

t 0 is the time stamp or the current calibration point, i equals −(N−1), −(N−2), . . . 0, N is the number of calibration points, and λ is the relative time difference between the current and the previous calibration points.

35. The analyte monitoring system of claim 34 , wherein N is a constant value.

36. The analyte monitoring system of claim 34 , wherein λ is a constant value.

37. The analyte monitoring system of claim 30 , wherein the weighted average cost function includes an accuracy metric, Error(θ) i .

38. The analyte monitoring system of claim 33 , wherein the first medium is blood, and the second medium is interstitial fluid.

39. The analyte monitoring system of claim 33 , wherein the one or more lag parameters include one or more of an analyte diffusion rate and an analyte consumption rate.

40. The analyte monitoring system of claim 33 , wherein the transceiver is further configured to determine whether to update one or more of the lag parameters.

41. The analyte monitoring system of claim 40 , wherein the transceiver is further configured to update one or more of the lag parameters.

42. The analyte monitoring system of claim 40 , wherein determining whether to update one or more of the lag parameters comprises determining whether a period of time has passed since the one or more of the lag parameters has been updated.

43. The analyte monitoring system of claim 33 , wherein the transceiver is further configured to update one or more of the lag parameters.

44. The analyte monitoring system of claim 43 , wherein updating one or more lag parameters comprises using one or more of a first method and a second method to estimate one or more updated lag parameters.

45. The analyte monitoring system of claim 44 , wherein the first method is a ratio method.

46. The analyte monitoring system of claim 44 , wherein the second method is a two-parameter method.

47. The analyte monitoring system of claim 44 , wherein updating one or more lag parameters comprises using the first method during a first period and using the second method during a second period.

48. The analyte monitoring system of claim 44 , wherein updating one or more lag parameters comprises using both the first and second methods.

49. The analyte monitoring system of claim 48 , wherein using both the first and second methods comprises:

using the first method to estimate a first set of updated lag parameters;

using the second method to estimate a second set of updated lag parameters;

using the first set of updated lag parameters to calculate one or more first sensor measurements;

using the second set of updated lag parameters to calculate one or more second sensor measurements;

evaluating the one or more first sensor measurements and the one or more second sensor measurements by comparing the one or more first sensor measurements and the one or more second sensor measurements to one or more reference measurements; and

selecting the more accurate of (a) the one or more first sensor measurements and (b) the one or more second sensor measurements for display to a user.

50. The analyte monitoring system of claim 33 , wherein the transceiver is further configured to determine whether to dynamically update one or more of the lag parameters.

51. The analyte monitoring system of claim 50 , wherein the transceiver is further configured to dynamically update one or more of the lag parameters.

52. The analyte monitoring system of claim 51 , wherein dynamically updating one or more of the lag parameters comprises using a minimum deviation divergence method.

53. The analyte monitoring system of claim 33 , wherein the transceiver is further configured to dynamically update one or more of the lag parameters.

54. The analyte monitoring system of claim 33 , wherein the first sensor analyte level is calculated using a two-compartment model that models the transport of the analyte from the first medium and in the second medium.

55. The analyte monitoring system of claim 30 , wherein the conversion function employs an asymmetrical lag methodology.

56. The analyte monitoring system of claim 55 , wherein the asymmetrical lag approach decelerates a rate of change of falling glucose levels during a low blood glucose event and accelerates a rate of change of increasing glucose levels during recovery from the low blood glucose event.

57. An analyte monitoring system comprising:

an analyte sensor including an indicator element that exhibits one or more detectable properties based on a concentration of an analyte in proximity to the indicator element; and

a transceiver configured to:

receive first sensor data from the analyte sensor, wherein the first sensor data comprises one or more measurements of the analyte level in a second medium;

use a conversion function and the first sensor data to calculate a first sensor analyte level, wherein the calculated first sensor analyte level is a level of the analyte in a first medium, and the transceiver, in using the conversion function and the first sensor data to calculate the first sensor analyte level, is configured to:

calculate a second medium analyte level using at least the first sensor data;

calculate a second medium level rate of change using at least the second medium analyte level; and

calculate the first sensor analyte level using at least the second medium analyte level and the second medium level rate of change;

receive a first reference analyte measurement (RM 1 );

store the RMI in a calibration point memory that includes one or more previous reference analyte measurements;

weight the RM 1 and the one or more previous reference analyte measurements according to a weighted average cost function;

update the conversion function using the weighted RM 1 and the one or more weighted previous reference analyte measurements as calibration points;

receive second sensor data from the analyte sensor; and

use the updated conversion function and the second sensor data to calculate a second sensor analyte level;

wherein the first sensor analyte level is calculated using a two-compartment model that models the transport of the analyte from the first medium and in the second medium, and the two-compartment model is

dC

2

dt

=

p

2

*

[

C

1

(

t

)

-

C

2

(

t

)

]

-

p

3

*

C

2

(

t

)

,

wherein C 1 (t) is a concentration of the analyte in the first medium, C 2 (t) is a concentration of the analyte in the second medium, p2 is an analyte diffusion rate, and p3 is an analyte consumption rate.

58. The analyte monitoring system of claim 57 , wherein 1/p2 and p3/p2 are lag parameters.

Assignments (9)