Analyte monitoring system and methods of use

• US 10,082,493 B2
• Filed: 04/29/2016
• Issued: 09/25/2018
• Est. Priority Date: 11/25/2011
• Status: Active Grant

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Inventors: Harper, Wesley Scott, Budiman, Erwin Satrya, Doniger, Kenneth J., Hayter, Gary Alan
• Primary Examiner(s): BERHANU, ETSUB D
• Application Number:
  US15/143,413
• Publication Number:
  US 20160238589A1
• Time in Patent Office: 879 Days
• Field of Search: None
• 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 5/0026 characterised by the transmission medium
• 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 5/1451 for interstitial fluid
• 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 5/6833 Adhesive patches
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• 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: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• 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: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

The present disclosure provides methods of processing data provided by a transcutaneous or subcutaneous analyte sensor utilizing different algorithms to strike a balance between signal responsiveness accompanied by signal noise and the introduction of error associated with that noise. The methods utilize the strengths of a lag correction algorithm and a smoothing algorithm to optimize the quality and value of the resulting data (glucose concentrations and the rates of change in glucose concentrations) to a continuous glucose monitoring system. Also provided are systems and kits.

Claims

1. A method for monitoring an analyte using an analyte monitoring system including a data processing device and a drug delivery device, comprising:

monitoring, using the data processing device of the analyte monitoring system, a data stream including a set of contiguous source data points related to a concentration of the analyte;

providing, using the data processing device of the analyte monitoring system, one or more sets of maximum lag corrected signals from the set of contiguous source data points;

providing, using the data processing device of the analyte monitoring system, a first one or more sets of maximum smoothed signals from the set of contiguous source data points, wherein each of the first one or more sets of maximum smoothed signals is generated utilizing a first smoothing algorithm;

providing, using the data processing device of the analyte monitoring system, a second one or more sets of maximum smoothed signals from the set of contiguous source data points, wherein each of the second one or more sets of maximum smoothed signals is generated utilizing a second smoothing algorithm;

determining, using the data processing device of the analyte monitoring system, the analyte concentration utilizing a weighted combination of the one or more sets of maximum lag corrected signals and the first and second one or more sets of maximum smoothed signals, wherein more weight is placed on the one or more sets of maximum lag corrected signals to determine the analyte concentration;

determining, using the data processing device of the analyte monitoring system, a rate of change in the analyte concentration utilizing a weighted combination of the one or more sets of maximum lag corrected signals and the first and second one or more sets of maximum smoothed signals, wherein more weight is placed on the first and second one or more sets of maximum smoothed signals to determine the rate of change; and

delivering, using the drug delivery device of the analyte monitoring system, a drug based on the determined analyte concentration.

2. The method of claim 1, wherein each of the one or more sets of maximum lag corrected signals is generated utilizing correction terms based on time derivative estimates and historical source data points.

3. The method of claim 2, wherein providing the one or more sets of maximum lag corrected signals includes using parameters which minimize a correlation between an expected glucose error and the time derivative estimates and minimize a correlation between the expected glucose error and a pre-determined array of the historical source data points.

4. The method of claim 3, wherein providing the one or more sets of maximum lag corrected signals includes utilizing an aggressive lag correction algorithm, the lag correction algorithm configured to drive one or more of the correlation between the expected glucose error and the time derivative estimates or the correlation between the expected glucose error and the pre-determined array of the historical source data points to zero.

5. The method of claim 1, wherein the first smoothing algorithm and the second smoothing algorithm are different.

6. The method of claim 1, further comprising averaging the first one or more sets of maximum smoothed signals and the second one or more sets of maximum smoothed signals.

7. The method of claim 1, wherein the data stream is received from a transcutaneously positioned analyte sensor.

8. The method of claim 7, wherein the analyte sensor includes a plurality of electrodes including a working electrode comprising an analyte-responsive enzyme.

9. The method of claim 8, wherein the enzyme is bound to a polymer disposed on the working electrode.

10. The method of claim 8, wherein the working electrode comprises a mediator.

11. The method of claim 10, wherein the mediator is bound to a polymer disposed on the working electrode.

12. The method of claim 10, wherein the mediator is crosslinked with a polymer disposed on the working electrode.

13. The method of claim 7, wherein the analyte sensor includes a plurality of electrodes including a working electrode comprising a mediator.

14. The method of claim 13, wherein the mediator is bound to a polymer disposed on the working electrode.

15. The method of claim 13, wherein the mediator is crosslinked with a polymer disposed on the working electrode.

16. A system, comprising:

an analyte sensor configured to be in contact with bodily fluid under a skin surface to monitor a concentration of an analyte;

a data processing device operatively coupled to the analyte sensor, wherein the data processing device is configured to;

monitor a data stream including a set of contiguous source data points related to the concentration of the analyte;

provide one or more sets of maximum lag corrected signals from the set of contiguous source data points;

provide a first one or more sets of maximum smoothed signals from the set of contiguous source data points, wherein each of the first one or more sets of maximum smoothed signals is generated utilizing a smoothing algorithm;

provide a second one or more sets of maximum smoothed signals from the set of contiguous source data points, wherein each of the second one or more sets of maximum smoothed signals is generated utilizing a second smoothing algorithm;

determine the analyte concentration utilizing a weighted combination of the one or more sets of maximum lag corrected signals and the first and second one or more sets of maximum smoothed signals, wherein more weight is placed on the one or more sets of maximum lag corrected signals to determine the analyte concentration; and

determine a rate of change in the analyte concentration utilizing a weighted combination of the one or more sets of maximum lag corrected signals and the first and second one or more sets of maximum smoothed signals, wherein more weight is placed on the first and second one or more sets of maximum smoothed signals to determine the rate of change; and

a drug delivery device configured to deliver a drug based on the determined analyte concentration.

17. The system of claim 16, wherein each of the one or more sets of maximum lag corrected signals is generated utilizing correction terms based on time derivative estimates and historical source data points.

18. The system of claim 17, wherein the data processing device is configured to provide the one or more sets of maximum lag corrected signals using parameters which minimize a correlation between an expected glucose error and the time derivative estimates and minimize a correlation between the expected glucose error and a pre-determined array of the historical source data points.

19. The system of claim 16, wherein the analyte sensor includes a plurality of electrodes including a working electrode comprising an analyte-responsive enzyme.

20. The system of claim 16, wherein the analyte sensor includes a plurality of electrodes including a working electrode comprising a mediator.