System and methods for processing analyte sensor data for sensor calibration

• US 8,579,816 B2
• Filed: 01/07/2010
• Issued: 11/12/2013
• Est. Priority Date: 03/10/2005
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Kamath, Apurv Ullas, Goode, Paul V., Brauker, James H.
• Primary Examiner(s): Natnithithadha, Navin
• Application Number:
  US12/683,724
• Publication Number:
  US 20100168545A1
• Time in Patent Office: 1,405 Days
• Field of Search: 600/309, 600345-366
• US Class Current: 600/365
• 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 continuous glucose sensor, comprising:

receiving sensor data from a continuous glucose sensor, wherein the sensor data is indicative of a glucose concentration in a host, wherein a predictive relationship exists between in vitro sensitivity and in vivo sensitivity represented by m_{in vivo}≈

f(m_{in vitro}), wherein m_{in vivo} is the in vivo sensitivity after inserting the continuous glucose sensor in the host, m_{in vitro} is the in vitro sensitivity prior to inserting the continuous glucose sensor in the host, and f is the predictive relationship between m_{in vitro} and m_{in vivo};

receiving a priori sensitivity information associated with the continuous glucose sensor; and

calibrating, using a processor module, the continuous glucose sensor based on the a priori sensitivity information, wherein the calibrating is performed without reliance on reference glucose values obtained from an in vitro self-monitoring blood glucose test after insertion of the glucose sensor in vivo.

2. The method of claim 1, wherein the a priori sensitivity information comprises the m_{in vitro}.

3. The method of claim 2, wherein the m_{in vitro} is derived from an analysis of sensor sensitivities collected from a representative set of sensors.

4. The method of claim 1, wherein the a priori sensitivity information comprises the predictive relationship, f.

5. The method of claim 4, wherein the predictive relationship is determined based on a retrospective analysis of sensor sensitivities collected from a representative set of sensors.

6. The method of claim 1, wherein the receiving the a priori sensitivity information is from a calibration code associated with the continuous glucose sensor.

7. The method of claim 6, wherein the calibration code is provided on a housing that supports the continuous glucose sensor.

8. The method of claim 1, further comprising measuring a change in sensitivity and/or baseline based on one or more signals obtained from electrodes of the continuous glucose sensor.

9. The method of claim 8, further comprising calibrating based on the measured change in sensitivity and/or baseline.

10. A system for calibrating a continuous glucose sensor, comprising:

a continuous glucose sensor configured to provide sensor data indicative of a glucose concentration in a host, wherein a predictive relationship exists between in vitro sensitivity and in vivo sensitivity represented by m_{in vivo}≈

f(m_{in vitro}), wherein m_{in vivo} is the in vivo sensitivity after inserting the continuous glucose sensor in the host, m_{in vitro} is the in vitro sensitivity prior to inserting the continuous glucose sensor in the host, and f is the predictive relationship between m_{in vitro} and m_{in vivo}; and

a processor module configured receive a priori sensitivity information associated with the continuous glucose sensor and calibrate the continuous glucose sensor based on the a priori sensitivity information, wherein the processor module is configured to calibrate the continuous glucose sensor without reliance on reference glucose values obtained from an in vitro self-monitoring blood glucose test after insertion of the glucose sensor in vivo.

11. The system of claim 10, wherein the a priori sensitivity information comprises the m_{in vitro}.

12. The system of claim 11, wherein the m_{in vitro} is derived from an analysis of sensor sensitivities collected from a representative set of sensors.

13. The system of claim 10, wherein the a priori sensitivity information comprises the predictive relationship, f.

14. The system of claim 13, wherein the predictive relationship is determined based on a retrospective analysis of sensor sensitivities collected from a representative set of sensors.

15. The system of claim 10, wherein the processor module is configured to receive the a priori sensitivity information from a calibration code associated with the continuous glucose sensor.

16. The system of claim 15, wherein the calibration code is provided on a housing that supports the continuous glucose sensor.

17. The system of claim 10, wherein the processor module is configured to measure a change in sensitivity and/or baseline based on one or more signals obtained from electrodes of the continuous glucose sensor.

18. The system of claim 17, wherein the calibrating is further based on the measured change in sensitivity and/or baseline.

19. The system of claim 10, further comprising:

a transmitter unit operably coupled to the glucose sensor, the transmitter unit having a radio frequency transmitter configured to wirelessly transmit sensor data; and

a receiver unit comprising a radio frequency receiver and a display, the radio frequency receiver configured to receive the transmitted sensor data and the display configured to display information based on the transmitted sensor data.

20. The system of claim 19, wherein the processor module is incorporated in the transmitter unit and the transmitted sensor data is calibrated sensor data.