Advanced analyte sensor calibration and error detection

• US 10,327,688 B2
• Filed: 05/31/2018
• Issued: 06/25/2019
• Est. Priority Date: 04/15/2011
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Bohm, Sebastian, Rong, Daiting, Simpson, Peter C.
• Primary Examiner(s): Previl, Daniel
• Application Number:
  US15/994,905
• Publication Number:
  US 20180279928A1
• Time in Patent Office: 390 Days
• Field of Search: 340604, 340612, 34063611-63615, 3406916, 34082543, 34028607
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0276 Determining malfunction
• CPC Class Codes: A61B 2560/04 Constructional details of apparatus
• CPC Class Codes: A61B 2562/085 combined with means for recording calibration data
• 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 5/14517 for sweat
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14535 for measuring haematocrit
• CPC Class Codes: A61B 5/14539 for measuring pH
• 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 5/14735 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: A61B 5/15003 for venous or arterial blood
• CPC Class Codes: A61B 5/150992 Blood sampling from a fluid line external to a patient, such as a catheter line, combined with an infusion line; blood sampling from indwelling needle sets, e.g. sealable ports, luer couplings, valves tubing connectors and couplings A61M39/00
• CPC Class Codes: A61B 5/155 : Devices specially adapted for continuous or multiple sampling, e.g. at predetermined intervals devices loaded with multiple lancets simultaneously A61B5/15146
• CPC Class Codes: A61B 5/157 : Devices characterised by integrated means for measuring characteristics of blood
• CPC Class Codes: A61B 5/412 : Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/4839 : combined with drug delivery
• CPC Class Codes: A61B 5/6848 : Needles
• CPC Class Codes: A61B 5/6849 : in combination with a needle set
• CPC Class Codes: A61B 5/6866 : Extracorporeal blood circuits, e.g. dialysis circuits
• CPC Class Codes: A61B 5/7257 : using Fourier transforms
• CPC Class Codes: A61B 5/7267 : involving training the classification device
• 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 5/743 : Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61M 2005/14296 : Pharmacokinetic models
• CPC Class Codes: A61M 2230/201 : Glucose concentration
• CPC Class Codes: A61M 5/14 : Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
• CPC Class Codes: A61M 5/16804 : Flow controllers
• 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: C12Q 1/001 : Enzyme electrodes
• CPC Class Codes: C12Q 1/006 : for glucose
• CPC Class Codes: G01D 18/00 : Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
• CPC Class Codes: G01N 27/026 : Dielectric impedance spectroscopy electrochemical impedance spectroscopy for measuring corrosion G01N17/02
• CPC Class Codes: G01N 27/3274 : Corrective measures, e.g. error detection, compensation for temperature or hematocrit, calibration coding of calibration information G01N33/48771
• 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 20/17 : delivered via infusion or injection
• CPC Class Codes: G16H 40/40 : for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
• CPC Class Codes: G16H 40/63 : for local operation
• 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 data and self-calibration are provided. In some embodiments, systems and methods are provided which are capable of calibrating a continuous analyte sensor based on an initial sensitivity, and then continuously performing self-calibration without using, or with reduced use of, reference measurements. In certain embodiments, a sensitivity of the analyte sensor is determined by applying an estimative algorithm that is a function of certain parameters. Also described herein are systems and methods for determining a property of an analyte sensor using a stimulus signal. The sensor property can be used to compensate sensor data for sensitivity drift, or determine another property associated with the sensor, such as temperature, sensor membrane damage, moisture ingress in sensor electronics, and scaling factors.

Claims

1. A method for calibrating at least one sensor data point from an analyte sensor, the method comprising:

(a) receiving a priori calibration distribution information;

(b) receiving one or more real-time inputs that influence calibration of the analyte sensor;

(c) forming posterior calibration distribution information based on the one or more real-time inputs; and

(d) converting, in real-time, at least one sensor data point calibrated sensor data based on the posterior calibration distribution information.

2. The method of claim 1, wherein the a priori calibration distribution information comprises probability distributions for sensitivity (m), sensitivity-related information, baseline (b), or baseline-related information.

3. The method of claim 1, wherein the a priori calibration distribution information comprises a priori guidance or validation ranges.

4. The method of claim 1, wherein the one or more real-time inputs comprise data received or determined since a previous calibration process.

5. The method of claim 4, wherein the one or more real-time inputs comprises at least one of:

internally-derived real-time data, externally-derived real-time data, and combinations of internally- and externally-derived real-time data.

6. The method of claim 5, wherein internally-derived real-time data includes at least one type of information selected from the group consisting of:

stimulus signal output of sensor;

sensor data measured by the sensor indicative of an analyte concentration;

sensor data indicative of analyte rate-of-change;

temperature measurements;

sensor data from multi-electrode sensors;

sensor data generated by redundant sensors;

sensor data generated by one or more auxiliary sensors;

data representative of a pressure on sensor;

data generated by an accelerometer;

sensor diagnostic information;

impedance; and

certainty level.

7. The method of claim 5, wherein externally-derived real-time data includes at least one type of information selected from the group consisting of:

glucose concentration information obtained from a reference monitor;

information related to meal;

insulin dosing time and amounts;

insulin estimates;

exercise;

sleep;

illness;

stress;

hydration; and

hormonal conditions.

8. The method of claim 5, wherein combinations of internally- and externally-derived real-time data includes at least one type of information selected from the group consisting of:

information gathered from population based data;

glucose concentration of the host;

error at calibration or error in matched data pair;

site of sensor implantation specific relationships;

time since sensor manufacture;

exposure of sensor to temperature, humidity, external factors, on shelf;

a measure of noise in an analyte concentration signal; and

a level of certainty.

9. The method of claim 1, further comprising determining a level of certainty associated with the calibration information and/or calibrated sensor data.

10. The method of claim 1, wherein forming posterior calibration distribution information comprises at least one of:

1) an adjustment of the a priori calibration distribution information or

     2) a creation of a new range or distribution information based on the one or more real-time inputs.

11. The method of claim 10, wherein an adjustment of the a priori calibration distribution information comprises shifting, tightening, or loosening the a priori calibration distribution.

12. The method of claim 1, wherein the calibration distribution information is selected from the group consisting of:

sensitivity;

change in sensitivity;

rate of change of sensitivity;

baseline;

change in baseline, rate of change of baseline, baseline profile associated with the sensor;

sensitivity profile associated with the sensor;

linearity;

response time;

relationships between properties of the sensor;

relationships between particular stimulus signal output; and

patient specific relationships between sensor and sensitivity, baseline, drift, impedance, impedance/temperature relationship, site of sensor implantation.

13. The method of claim 1, further comprising providing output of calibrated sensor data.

14. The method of claim 1, wherein the method is implemented on a computer having a processor and a memory coupled to said processor, wherein at least one of steps (a) through (e) are performed using said processor.

15. A system for calibrating at least one sensor data point from a continuous analyte sensor, the system comprising sensor electronics configured to be operably connected to a continuous analyte sensor, the sensor electronics configured to:

(a) receive a priori calibration distribution information;

(b) receive one or more real-time inputs that influence calibration of the analyte sensor;

(c) form posterior calibration distribution information using the one or more real-time inputs; and

(d) convert, in real-time, at least one sensor data point calibrated sensor data based on the posterior calibration distribution information.

16. The system of claim 15, wherein the sensor electronics comprise a processor module, the processor module comprising instructions stored in computer memory, wherein the instructions, when executed by the processor module, cause the sensor electronics to perform the forming and the determining.

17. A system for calibrating at least one sensor data point from an analyte sensor, the system comprising:

means for receiving a priori calibration distribution information;

means for receiving one or more real-time inputs that influence calibration of the analyte sensor;

means for forming posterior calibration distribution information based on the one or more real-time inputs; and

means for converting, in real-time, at least one sensor data point calibrated sensor data based on the posterior calibration distribution information.