System and methods for processing analyte sensor data for sensor calibration

• US 9,918,668 B2
• Filed: 03/09/2016
• Issued: 03/20/2018
• Est. Priority Date: 03/10/2005
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Pryor, Jack, Kamath, Apurv Ullas, Goode, Jr., Paul V., Brauker, James H.
• Primary Examiner(s): Natnithithadha, Navin
• Application Number:
  US15/065,623
• Publication Number:
  US 20160183856A1
• Time in Patent Office: 741 Days
• Field of Search: 600309, 600345-366
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• 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 5/1451 for interstitial fluid
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/1468 using chemical or electrochemical methods, e.g. by polarographic means A61B5/1486 takes precedence
• CPC Class Codes: A61B 5/1473 invasive, e.g. introduced into the body by a catheter A61B5/1482 takes precedence
• CPC Class Codes: A61B 5/14735 comprising an immobilised reagent
• CPC Class Codes: A61B 5/1486 using enzyme electrodes, e.g. with immobilised oxidase
• 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: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Systems and methods for processing sensor analyte data are disclosed, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. The sensor can be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. Reference data resulting from benchtop testing an analyte sensor prior to its insertion can be used to provide initial calibration of the sensor data. Reference data from a short term continuous analyte sensor implanted in a user can be used to initially calibrate or update sensor data from a long term continuous analyte sensor.

Claims

1. A method for calibrating a second continuous analyte sensor using sensor data from a first continuous analyte sensor, comprising:

receiving sensor data from a first continuous analyte sensor, wherein the sensor data is indicative of a glucose concentration in a host;

determining a conversion function, using a processor module, wherein the conversion function comprises a slope and a baseline, wherein the slope is representative of a sensor sensitivity;

converting the sensor data using the conversion function;

receiving sensor data from a second continuous analyte sensor; and

calibrating the sensor data from the second continuous analyte sensor using the sensor data from the first continuous analyte sensor, including providing calibration information, including data about the sensor sensitivity, to the second continuous analyte sensor to increase its intelligence and enhance its performance, wherein the calibrating includes constructing a first curve from the sensor data from the first continuous analyte sensor and constructing a second curve from the sensor data from the second continuous analyte sensor and matching the first curve with the second curve.

2. The method of claim 1, wherein determining the conversion function comprises determining the slope based on prior slope distribution information.

3. The method of claim 1, wherein determining the conversion function comprises determining the baseline based on prior baseline distribution information.

4. The method of claim 1, wherein determining the conversion function comprises determining the slope and the baseline based on prior slope and baseline distribution information.

5. The method of claim 4, wherein determining the slope and the baseline comprises selecting a most probable slope and baseline from a distribution of slopes and baselines based on the prior slope and baseline distribution information.

6. The method of claim 5, wherein selecting the most probable slope and baseline comprises selecting a slope and baseline closest to the maximum joint probability of both the slope and baseline distributions.

7. The method of claim 1, where determining the conversion function comprises selecting one of a plurality of possible calibration lines having various baselines and slopes based on prior information indicative of typical distribution of slopes and/or baselines.

8. The method of claim 1, wherein the slope is determined, at least in part, based on in-vitro slope information determined by a benchtop calibration process.

9. The method of claim 1, further comprising receiving reference data from a reference analyte monitor, wherein the slope is determined, at least in part, based on the reference analyte value.

10. The method of claim 1, wherein the prior distribution information is derived from in vivo testing of at least one other continuous glucose sensor or in vitro testing.

11. The method of claim 1, wherein time periods during which the first and second continuous analyte sensors receive sensor data partially overlap.

12. The method of claim 11, wherein the first continuous analyte sensor has a predefined lifespan and wherein the overlap is near the end of the predefined lifespan.

13. The method of claim 1, wherein time periods during which the first and second continuous analyte sensors receive sensor data do not overlap.

14. The method of claim 1, wherein the first and second continuous analyte sensors are glucose sensors.

15. The method of claim 1, wherein the sensor data from the first continuous analyte sensor comprises at least one sensor data point, wherein the calibrating the sensor data from the second continuous analyte sensor using the sensor data from the first continuous analyte sensor includes forming at least one matched data pair by matching the at least one sensor data point from the first analyte sensor to at least one sensor data point from the second continuous analyte sensor and forming a calibration set comprising at least one matched data pair.

16. The method of claim 1, wherein each of the first and second continuous analyte sensors have a predefined lifespan in the range of one hour to three weeks.

17. The method of claim 1, wherein the calibrating is performed in substantially real time.

18. A method of calibrating an analyte sensor, the method including:

receiving sensor data from a first substantially continuous transcutaneous analyte sensor;

receiving sensor data from a second substantially continuous transcutaneous analyte sensor;

calibrating the sensor data from the second sensor using the sensor data from the first sensor, the calibrating including using data about a sensor sensitivity determined for the first sensor, the sensor sensitivity used in a conversion function to convert the sensor data to clinical data,

wherein the first and second sensors are sequentially employed in a host,

wherein the method further includes receiving data from a non-continuous reference source, and

wherein said calibrating further includes using data from the non-continuous reference source to calibrate the sensor data from the second sensor.

19. The method of claim 18, wherein the non-continuous reference source is an in-vitro calibration.

20. The method of claim 18, wherein the first and second sensors are glucose sensors.

21. The method of claim 18, wherein the first and second sensors each have a predefined lifespan in the range of one hour to three weeks.

22. The method of claim 18, wherein the sensor data from the first substantially continuous transcutaneous analyte sensor comprises at least one sensor data point, wherein the calibrating the sensor data from the second substantially continuous transcutaneous analyte sensor using the sensor data from the first substantially continuous transcutaneous analyte sensor includes forming at least one matched data pair by matching the at least one sensor data point from the first substantially continuous transcutaneous analyte sensor to at least one sensor data point from the second substantially continuous transcutaneous analyte sensor and forming a calibration set comprising at least one matched data pair.

23. The method of claim 18, wherein the calibrating is performed in substantially real time.

24. The method of claim 18, wherein the non-continuous reference source is a sensor ID or code.