Method and apparatus for improving lag correction during in vivo measurement of analyte concentration with analyte concentration variability and range data

• US 10,842,420 B2
• Filed: 03/02/2018
• Issued: 11/24/2020
• Est. Priority Date: 09/26/2012
• Status: Active Grant

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Inventors: Budiman, Erwin Satrya
• Primary Examiner(s): Winakur, Eric F
• Application Number:
  US15/910,927
• Publication Number:
  US 20180184954A1
• Time in Patent Office: 998 Days
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• 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 5/14532 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 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• 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: G06F 2218/10 by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Methods, devices, and systems are provided for correcting lag in measurements of analyte concentration level in interstitial fluid. The invention includes receiving a signal representative of sensor data from an analyte monitoring system related to an analyte level measured over time, computing rates of change of the sensor data for a time period of the sensor data, computing a rate distribution of the rates of change, transforming the rate distribution into a linear arrangement, determining a best-fit line for the transformed rate distribution, computing a slope of the best-fit line; and using the slope of the best-fit line as a representation of a variability of the analyte level to adjust an amount of lag correction applied to the sensor data. Numerous additional features are disclosed.

Claims

1. A method comprising:

determining at least one of analyte concentration variability and analyte concentration range;

determining a lag correction value to apply to sensor data representative of analyte concentration measured in interstitial fluid using an analyte measurement system;

adjusting the lag correction value to apply to the sensor data based upon the at least one of the analyte concentration variability and the analyte concentration range; and

computing an analyte concentration level representative of a blood analyte concentration level based on the adjusted lag correction value.

2. The method of claim 1, wherein the lag correction value is adjusted by an amount related to the analyte concentration variability.

3. The method of claim 1, wherein the lag correction value is adjusted by an amount based on the analyte concentration range.

4. A computer-implemented method, comprising:

defining a scaling factor for lag correction;

collecting a moving window of historical analyte sensor data;

defining a probability density function of the historical analyte sensor data within the moving window;

determining a normalized analyte variability ratio;

storing the normalized analyte variability ratio computed at regular intervals;

comparing the normalized analyte variability ratio to a predetermined value and a number of prior values;

setting a value of the scaling factor based on the probability density function; and

computing lag corrected values based on the scaling factor.

5. The method of claim 4, wherein defining the scaling factor for lag correction includes relating a lag corrected value to the scaling factor based on the equation:

G lag(t)=G latest(t)+(K G c(t))

wherein K represents the scaling factor, G latest(t) represents a non-lag corrected analyte value at time t, G c(t) represents a nominal lag correction amount at time t, and G lag(t) represents the lag corrected value at time t.

6. The method of claim 4, wherein defining the scaling factor for lag correction includes defining a scaling factor that ranges in value from zero to one and wherein a value of zero corresponds to applying no lag correction and a value of one corresponds to applying full lag correction.

7. The method of claim 4, wherein defining the probability density function of the historical analyte sensor data within the moving window includes using a moving window larger than two days.

8. The method of claim 4, wherein defining the probability density function of the historical analyte sensor data within the moving window includes using a second window having a smaller window size than the moving window.

9. The method of claim 8, wherein the second window has a window size of less than three days.

10. A system for determining analyte concentration in blood based on analyte concentration measured in interstitial fluid, the system comprising:

a processor; and

a memory coupled to the processor, the memory storing processor executable instructions to;

define a scaling factor for lag correction;

collect a moving window of historical analyte sensor data;

define a probability density function of the historical analyte sensor data within the moving window;

determine a normalized analyte variability ratio;

store the normalized analyte variability ratio computed at regular intervals;

compare the normalized analyte variability ratio to a predetermined value and a number of prior values;

set a value of the scaling factor based on the probability density function; and

compute lag corrected values based on the scaling factor.

11. The system of claim 10, wherein the instruction to define the scaling factor for lag correction includes an instruction to relate a lag corrected value to the scaling factor based on the equation:

G lag(t)=G latest(t)+(K G c(t))

wherein K represents the scaling factor, G latest(t) represents a non-lag corrected analyte value at time t, G c(t) represents a nominal lag correction amount at time t, and G lag(t) represents the lag corrected value at time t.

12. The system of claim 10, wherein the instruction to define the scaling factor for lag correction includes an instruction to define a scaling factor that ranges in value from zero to one and wherein a value of zero corresponds to applying no lag correction and a value of one corresponds to applying full lag correction.

13. The system of claim 10, wherein the instruction to define the probability density function of the historical analyte sensor data within the moving window includes an instruction to use a moving window larger than two days.

14. The system of claim 10, wherein the instruction to define the probability density function of the historical analyte sensor data within the moving window includes an instruction to use a second window having a smaller window size than the moving window.

15. The system of claim 14, wherein the second window has a window size of less than three days.

16. A computer program product stored on a computer-readable medium comprising executable instructions to:

define a scaling factor for lag correction;

collect a moving window of historical analyte sensor data;

define a probability density function of the historical analyte sensor data within the moving window;

determine a normalized analyte variability ratio;

store the normalized analyte variability ratio computed at regular intervals;

compare the normalized analyte variability ratio to a predetermined value and a number of prior values;

set a value of the scaling factor based on the probability density function; and

compute lag corrected values based on the scaling factor.

17. The computer program product of claim 16, wherein the instruction to define the scaling factor for lag correction includes an instruction to relate a lag corrected value to the scaling factor based on the equation:

G lag(t)=G latest(t)+(K G c(t))

wherein K represents the scaling factor, G latest(t) represents a non-lag corrected analyte value at time t, G c(t) represents a nominal lag correction amount at time t, and G lag(t) represents the lag corrected value at time t.

18. The computer program product of claim 16, wherein the instruction to define the scaling factor for lag correction includes an instruction to define a scaling factor that ranges in value from zero to one and wherein a value of zero corresponds to applying no lag correction and a value of one corresponds to applying full lag correction.

19. The computer program product of claim 16, wherein the instruction to define the probability density function of the historical analyte sensor data within the moving window includes an instruction to use a moving window larger than two days.

20. The computer program product of claim 16, wherein the instruction to define the probability density function of the historical analyte sensor data within the moving window includes an instruction to use a second window having a smaller window size than the moving window.

21. The computer program product of claim 20, wherein the second window has a window size of less than three days.