FAULT DISCRIMINATION AND RESPONSIVE PROCESSING BASED ON DATA AND CONTEXT

• US 20150351672A1
• Filed: 05/20/2015
• Published: 12/10/2015
• Est. Priority Date: 06/06/2014
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Vanslyke, Stephen J., Bhavaraju, Naresh C., Bhm, Sebastian, Bowman, Leif N., Estes, Michael J., Garcia, Arturo, Kamath, Apurv Ullas, Peyser, Thomas A., Rack-Gomer, Anna Leigh, Rong, Daiting, Sheth, Disha B., Simpson, Peter C., Pal, Andrew Attila, Sokolovsky, Dmytro
• Granted Patent: US 10,231,659 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0247 for compensation or correction of the measured physiological value
• CPC Class Codes: A61B 2560/0252 using ambient temperature
• CPC Class Codes: A61B 2560/0276 Determining malfunction
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/1473 invasive, e.g. introduced into the body by a catheter A61B5/1482 takes precedence
• CPC Class Codes: A61B 5/14865 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: A61B 5/4833 Assessment of subject's compliance to treatment
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7221 : Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7246 : using correlation, e.g. template matching or determination of similarity
• 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: A61B 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 : Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61M 2205/3584 : using modem, internet or bluetooth
• CPC Class Codes: A61M 2205/3592 : using telemetric means, e.g. radio or optical transmission
• CPC Class Codes: A61M 2205/50 : with microprocessors or computers
• CPC Class Codes: A61M 2205/52 : with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2230/201 : Glucose concentration
• 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: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

Systems and methods disclosed here provide ways to discriminate fault types encountered in analyte sensors and systems and further provide ways to process such discriminated faults responsively based on sensor data, clinical context information, and other data about the patient or patient'"'"'s environment. The systems and methods thus employ clinical context in detecting and/or responding to errors or faults associated with an analyte sensor system, and discriminating the type of fault, and its root cause, particularly as fault dynamics can appear similar to the dynamics of physiological systems, emphasizing the importance of discriminating the fault and providing appropriate responsive processing. Thus, the disclosed systems and methods consider the context of the patient'"'"'s health condition or state in determining how to respond to the fault.

Claims

1. A method for performing responsive processing in response to a fault in a continuous in vivo analyte monitoring system, comprising:

receiving a signal from an analyte monitor;

receiving clinical context data;

evaluating the received clinical context data against clinical context criteria to determine clinical context information; and

performing responsive processing based on at least the received signal and the determined clinical context information.

2. The method of claim 1, wherein the discriminating includes categorizing the fault based on the received signal, the clinical context information, or both.

3. The method of claim 2, wherein the categorizing the fault includes categorizing the fault as a sensor environment fault or as a system error/artifact fault.

4. The method of claim 3, wherein the discriminating includes categorizing the fault as a sensor environment fault, and further comprising subcategorizing the fault as a compression fault or an early wound response fault.

5. The method of claim 1, wherein the discriminating includes slow versus fast sampling.

6. The method of claim 1, wherein the received clinical context data is selected from the group consisting of age, anthropometric data, drugs currently operating on the patient, temperature as compared to a criteria, a fault history of the patient, activity level of the patient, exercise level of the patient, a patient level of interaction with a glucose monitor, patterns of glucose signal values, clinical glucose value and its derivatives, a range of patient glucose levels over a time period, a duration over which patient glucose levels are maintained in a range, a patient glucose state, a glycemic urgency index, time of day, and pressure.

7. The method of claim 1, further comprising receiving an additional signal.

8. The method of claim 1, further comprising receiving an additional signal, wherein the additional signal is a sensor temperature signal, an impedance signal, an oxygen signal, a pressure signal, or a background signal.

9. The method of claim 1, wherein the clinical context information corresponds to data about the patient excluding a signal value measured at a sensor associated with the analyte monitor.

10. The method of claim 1, wherein the clinical context criteria includes predefined values or ranges of parameters selected from the group consisting of drugs currently operating on the patient, temperature, a fault history of the patient, activity level of the patient, exercise level of the patient, a patient level of interaction with a glucose monitor, patterns of glucose signal values, clinical glucose value and its derivatives, a range of patient glucose levels over a time period, a duration over which patient glucose levels are maintained in a range, a patient glucose state, a glycemic urgency index, time of day, and pressure.

11. The method of claim 1, wherein the clinical context data includes temperature, the clinical context criteria includes a pattern of temperatures, the evaluating determines the clinical context information to be that the user is in contact with water at the sensor site, and the discriminating the fault type includes discriminating the fault type as water ingress.

12. The method of claim 1, wherein the clinical context data includes patient activity level or time of day, the clinical context criteria includes a pattern of patient activity levels, the evaluating determines the clinical context information to be that the user is compressing the sensor site, and the discriminating the fault type includes discriminating the fault type as compression.

13. The method of claim 1, wherein the clinical context data includes time since implant, the clinical context criteria includes a range of times since implant in which dip and recover faults are likely, the evaluating determines the clinical context information to be that the sensor is recently implanted, and the discriminating the fault type includes discriminating the fault type as a dip and recover fault.

14. The method of claim 1, wherein the clinical context data includes a clinical glucose value and a datum selected from the group consisting of age, anthropometric data, activity, exercise, clinical use of data, and patient interaction with monitor.

15. The method of claim 1, wherein the responsive processing includes providing a display to a user, the display including a warning, an alert, an alarm, a confidence indicator, a range of values, a predicted value, or a blank screen.

16. The method of claim 1, wherein the performing responsive processing includes adjusting a level of filtering of the received signal.

17. The method of claim 1, wherein the performing responsive processing includes performing a self diagnostics routine.

18. The method of claim 1, wherein the performing responsive processing includes switching from a first therapeutic mode to a second therapeutic mode.

19. An electronic device for monitoring data associated with a physiological condition, comprising:

a continuous glucose sensor, wherein the continuous glucose sensor is configured to substantially continuously measure the concentration of glucose in the host, and to provide continuous sensor data associated with the glucose concentration in the host; and

a processor module configured to perform the method of claim 1.

20. An electronic device for delivering insulin to a host, the device comprising:

a medicament delivery device configured to deliver insulin to the host, wherein the insulin delivery device is operably connected to a continuous glucose sensor, wherein the continuous glucose sensor is configured to substantially continuously measure the concentration of glucose in the host, and to provide continuous sensor data associated with the glucose concentration in the host; and

a processor module configured to perform the method of claim 1.