Method and apparatus for providing data processing and control in a medical communication system

• US 10,119,956 B2
• Filed: 10/27/2017
• Issued: 11/06/2018
• Est. Priority Date: 05/14/2007
• Status: Active Grant

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Inventors: Hayter, Gary Alan, McGarraugh, Geoffrey V., Naegeli, Andrew H., Mazza, John Charles, Feldman, Benjamin Jay
• Primary Examiner(s): Charioui, Mohamed
• Application Number:
  US15/796,274
• Publication Number:
  US 20180059093A1
• Time in Patent Office: 375 Days
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0252 using ambient temperature
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1473 invasive, e.g. introduced into the body by a catheter A61B5/1482 takes precedence
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• 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 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 40/40 for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Methods for data processing and control for a medical communication system are provided, including determining a sensitivity value for each sensor of a batch of in vivo analyte sensors; determining a sensitivity variation for each sensor of the batch; determining a mean sensitivity based on the sensitivity value determined for each sensor of the batch when it is determined that the determined sensitivity variation does not exceed a tolerance threshold level; associating a sensor code with each sensor of the batch of in vivo analyte sensors when the mean sensitivity is within a predetermined sensitivity range, wherein the sensor code associated with each sensor is based on a characteristic of the associated sensor; and storing the sensor code associated with at least one sensor of the batch configured to generate signals corresponding to monitored analyte level in a memory of a data processing device associated with the sensor.

Claims

1. A method, comprising:

determining a sensitivity value for each sensor of a batch of in vivo analyte sensors;

determining a sensitivity variation for each sensor of the batch of in vivo analyte sensors;

determining a mean sensitivity based on the sensitivity value determined for each sensor of the batch of in vivo analyte sensors when it is determined that the determined sensitivity variation does not exceed a tolerance threshold level;

associating a sensor code with each sensor of the batch of in vivo analyte sensors when the mean sensitivity is within a predetermined sensitivity range, wherein the sensor code associated with each sensor of the batch of in vivo analyte sensors is based on a characteristic of the associated sensor; and

storing the sensor code associated with at least one sensor of the batch of in vivo analyte sensors configured to generate signals corresponding to monitored analyte level in a memory of a data processing device associated with the sensor.

2. The method of claim 1, wherein the analyte of the in vivo analyte sensors is one of glucose or lactate.

3. The method of claim 1, wherein the sensor code associated with each sensor of the batch of in vivo analyte sensors is based on a sensitivity of the sensor, and wherein the sensor code corresponds to a sensitivity range.

4. The method of claim 1, further comprising labeling a sensor packaging for the batch of in vivo analyte sensors with the sensor code.

5. The method of claim 1, further comprising performing a retrospective analyte level determination using the sensor code.

6. The method of claim 1, wherein each analyte sensor of the batch of in vivo analyte sensors comprises a plurality of electrodes including a working electrode comprising an analyte-responsive enzyme bonded to a polymer disposed on the working electrode.

7. The method of claim 6, wherein the analyte-responsive enzyme is chemically bonded to the polymer disposed on the working electrode.

8. The method of claim 6, wherein the working electrode comprises a mediator bonded to the polymer disposed on the working electrode.

9. The method of claim 8, wherein the mediator is crosslinked with the polymer disposed on the working electrode.

10. The method of claim 1, wherein each analyte sensor of the batch of in vivo analyte sensors comprises a plurality of electrodes including a working electrode comprising a mediator bonded to a polymer disposed on the working electrode.

11. A method, comprising:

determining a sensitivity value for each sensor of a batch of in vivo analyte sensors;

determining a sensitivity variation for each sensor of the batch of in vivo analyte sensors;

determining a mean sensitivity based on the sensitivity value determined for each sensor of the batch of in vivo analyte sensors when it is determined that the determined sensitivity variations each do not exceed a tolerance threshold level;

associating a sensor code with each sensor of the batch of in vivo analyte sensors when the mean sensitivity is within a predetermined sensitivity range;

generating signals corresponding to a monitored analyte level of a user using at least one sensor of the batch of in vivo analyte sensors; and

storing the sensor code associated with the at least one sensor of the batch of in vivo analyte sensors configured to generate the signals corresponding to the monitored analyte level in a memory of a data processing device associated with the sensor.

12. The method of claim 11, wherein the analyte of the in vivo analyte sensors is one of glucose or lactate.

13. The method of claim 11, wherein the sensor code corresponds to the predetermined sensitivity range.

14. The method of claim 11, further comprising determining, using the data processing device and the sensor code, an updated sensitivity for the at least one sensor of the batch of in vivo analyte sensors;

and calibrating, using the data processing device and the updated sensitivity, the generated signals to obtain the monitored analyte level.

15. The method of claim 14, further comprising performing a retrospective analyte level determination using the updated sensitivity.

16. The method of claim 11, wherein each analyte sensor of the batch of in vivo analyte sensors comprises a plurality of electrodes including a working electrode comprising an analyte-responsive enzyme bonded to a polymer disposed on the working electrode.

17. The method of claim 16, wherein the analyte-responsive enzyme is chemically bonded to the polymer disposed on the working electrode.

18. The method of claim 16, wherein the working electrode comprises a mediator bonded to the polymer disposed on the working electrode.

19. The method of claim 18, wherein the mediator is crosslinked with the polymer disposed on the working electrode.

20. The method of claim 11, wherein each analyte sensor of the batch of in vivo analyte sensors comprises a plurality of electrodes including a working electrode comprising a mediator bonded to a polymer disposed on the working electrode.