Methods for measuring analyte in a subject and/or compensating for incomplete reaction involving detection of the analyte

• US 20050130249A1
• Filed: 01/24/2005
• Published: 06/16/2005
• Est. Priority Date: 05/16/2000
• Status: Active Grant

• Current Assignee: LifeScan IP Holdings, LLC
• Original Assignee: Cygnus, Inc. (Johnson & Johnson)
• Inventors: Potts, Russell O., Tierney, Michael J., Parris, Norman A., Uhegbu, Christopher
• Granted Patent: US 7,604,593 B2
• Time in Patent Office: Days
• US Class Current: 435/14
• CPC Class Codes: A61B 5/14514 using means for aiding extraction of interstitial fluid, e.g. microneedles or suction
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1486 using enzyme electrodes, e.g. with immobilised oxidase

Abstract

The present invention relates to a predictive-kinetic method for use with data processing of a sensor-generated signal, as well as, microprocessors and monitoring systems employing such a predictive-kinetic method. Data from a transient region of a signal is used with suitable models and curve-fitting methods to predict the signal that would be measured for the system at the completion of the reaction. The values resulting from data processing of sensor response using the methods of the present invention are less sensitive to measurement variables.

Claims

1. A method for measuring glucose present in a subject, said method comprising:

(A) transdermally extracting a sample comprising glucose from the subject using a sampling system that is in operative contact with a skin or mucosal surface of said subject;

(B) obtaining a measured signal over time, comprising a measured signal response curve, from the extracted glucose, wherein said measured signal is specifically related to the amount or concentration of glucose, and said measured signal response curve comprises kinetic and equilibrium regions;

(C) using (i) a mathematical model comprising selected parameters, wherein said model describes the measured signal response curve, and said mathematical model is selected from the group consisting of a first order process, combined first order and zero order process, a parallel multiple first order process, a flux process, and an n^th order process, and (ii) an error minimization method, to iteratively estimate values of the parameters using said model and error minimization method to fit a predicted response curve to said measured signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between said predicted and measured signal response curves, and (b) said estimating is iteratively performed until the calculated error between the predicted and measured signal response curves falls within an acceptable range or until no further statistically significant change is seen in the calculated error, at which time iterative estimation of the parameters is stopped, said iterative estimation and error minimization results in a predicted response curve corresponding to said measured signal response curve, said predicted response curve yields a predicted end-point value and a measurement correlated to the amount or concentration of the glucose.

2. The method of claim 1, wherein said measured signal response curve comprises a measurement of current over time, or measurement of charge over time.

3. The method of claim 2, wherein said measured signal response curve comprises a measurement of current over time, said predicted end-point value is an estimated signal at equilibrium, where said predicted end-point value provides a predicted final background value, and said measurement correlated to the amount or concentration of glucose corresponds to an area under the predicted response curve.

4. The method of claim 3, wherein said area under the predicted response curve is obtained by integration of the predicted response curve.

5. The method of claim 4, wherein before said integration is performed said final background value is used to perform a background subtraction correction of the predicted response curve and said measurement correlated to the amount or concentration of glucose corresponds to an area under the predicted response curve.

6. The method of claim 4, wherein the end-point value of the integrated predicted response curve is converted to an amount or concentration of the glucose.

7. The method of claim 5, wherein the end-point value of the integrated predicted response curve is converted to an amount or concentration of the glucose.

8. The method of claim 1, wherein the mathematical model further comprises a zero-order component.

9. The method of claim 6, wherein conversion of the end-point value of the integrated predicted response curve to an amount or concentration of glucose is performed by a method comprising applying a calibration value.

10. The method of claim 1, wherein said mathematical model comprises more than one process and each process comprises selected parameters.

11. The method of claim 10, wherein each process has a corresponding weighting factor.

12. The method of claim 1, wherein a background subtraction is performed on the measured signal response curve before (C) is performed.

13. The method of claim 1, wherein (A), (B), and (C) are performed at least two times to obtain a series of measurements.

14. The method of claim 13, wherein after estimation of each predicted response curve for each measured signal response curve in the series of measurements an amount or concentration of the glucose is determined based on the predicted response curve.

15. The method of claim 1, wherein said measured signal response curve comprises data points.

16. The method of claim 15, wherein at least three data points are obtained from the kinetic region of the measured signal response curve, and these data points are used to estimate the half-life of the measured signal.

18. The method of claim 1, wherein said obtaining is carried out for a defined period of time.

21. The method of claim 1, wherein said mathematical model comprises a mathematical model selected from the group consisting of a first order process, combined first order and zero order process, a parallel multiple first order process, a flux process, and an n^th order process.

49. The method of claim 1, wherein said transdermally extracting the sample is performed using a sampling method comprising a sampling method selected from the group consisting of iontophoresis, sonophoresis, microdialysis, suction, and passive diffusion.

17. (canceled)

19-20. -20. (canceled)

22-48. -48. (canceled)

50-55. -55. (canceled)

56. A method for compensating for an incomplete reaction involving the detection of an analyte by predicting a background signal, said method comprising

(A) providing a measured signal over time, comprising a measured signal response curve, wherein said measured signal is specifically related to an amount or concentration of analyte, and said measured signal response curve comprises kinetic and equilibrium regions;

(B) using (i) a mathematical model comprising selected parameters, wherein said model describes the measured signal response curve, and said mathematical model is selected from the group consisting of a first order process, combined first order and zero order process, a parallel multiple first order process, a flux process, and an n^th order process, and (ii) an error minimization method, to iteratively estimate values of the parameters using said model and error minimization method to fit a predicted response curve to said measured signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between said predicted and measured signal response curves, and (b) said estimating is iteratively performed until the calculated error between the predicted and measured signal response curves falls within an acceptable range or until no further statistically significant change is seen in the calculated error, at which time iterative estimation of the parameters is stopped, said iterative estimation and error minimization results in a predicted response curve corresponding to said measured signal response curve, said predicted response curve yields a predicted final background value and a measurement correlated to the amount or concentration of the analyte; and

(C) performing a background subtraction correction of the predicted response curve using the predicted final background value, wherein said background subtraction compensates for an incomplete reaction involved in the detection of the analyte.

57-85. -85. (canceled)