Accommodating subject and instrument variations in spectroscopic determinations

• US 7,098,037 B2
• Filed: 08/16/2002
• Issued: 08/29/2006
• Est. Priority Date: 10/13/1998
• Status: Expired due to Fees

• Current Assignee: National Technology & Engineering Solutions Of Sandia LLC (Honeywell International Inc.)
• Original Assignee: Inlight Solutions, Inc.
• Inventors: Haas, Michael J., Rowe, Robert K., Thomas, Edward V.
• Primary Examiner(s): Gakh, Yelena G.
• Application Number:
  US10/222,721
• Publication Number:
  US 20040033618A1
• Time in Patent Office: 1,474 Days
• Field of Search: None
• US Class Current: 436/95
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1459 invasive, e.g. introduced into the body by a catheter A61B5/1464 takes precedence
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: Y10T 436/10 Composition for standardization, calibration, simulation, stabilization, preparation or preservation; processes of use in preparation for chemical testing
• CPC Class Codes: Y10T 436/144444 Glucose

Abstract

A method and apparatus for measuring a biological attribute, such as the concentration of an analyte, particularly a blood analyte in tissue such as glucose. The method utilizes spectrographic techniques in conjunction with an improved instrument-tailored or subject-tailored calibration model. In a calibration phase, calibration model data is modified to reduce or eliminate instrument-specific attributes, resulting in a calibration data set modeling intra-instrument or intra-subject variation. In a prediction phase, the prediction process is tailored for each target instrument separately using a minimal number of spectral measurements from each instrument or subject.

Claims

1. A non-invasive method for determining the presence, concentration, or both of an analyte in tissue of a specific subject comprising the steps of:

a) providing an apparatus for measuring infrared absorption, said apparatus including an energy source emitting infrared energy at multiple wavelengths, an input element, an output element and a spectrum analyzer;

b) providing an apparatus for making a direct measurement of the presence, concentration or both of the analyte;

c) coupling said input and output elements to said tissue;

d) irradiating said tissue through said input element with multiple wavelengths of infrared energy with resulting absorption of at least some of said wavelengths;

e) collecting at least a portion of the non-absorbed infrared energy with said output element followed by determining the intensities of said infrared energy; and

f) determining the presence, concentration, or both of the analyte in the tissue of said specific subject utilizing a multivariate calibration model, based on reference measurements from multiple subjects and one or more reference measurements from said specific subject, wherein each of said reference measurements includes infrared spectroscopic and corresponding direct measurement of a presence, concentration, or both of the analyte.

2. The method of claim 1, wherein the analyte comprises glucose.

3. The method of claim 1, wherein the analyte comprises a glycosolation byproduct.

4. The method of claim 1, wherein the analyte comprises glucose, and wherein the apparatus for making a direct measurement of the concentration comprises an invasive glucose concentration meter.

5. The method of any of claims 1–

4, wherein infrared absorption is near infrared absorption.

6. A method as in claim 1, wherein the multivariate calibration model comprises a partial least squares regression on the reference measurements from multiple subjects and one or more reference measurements from the specific subject.

7. The method of claim 1, wherein the multivariate calibration model comprises a partial least squares regression on the reference measurements from multiple subjects, subsequently modified by the one or more reference measurements from the specific subject.

8. The method of claim 7, wherein modifying the multivariate calibration model comprises applying a partial least squares regression to the multivariate calibration model and the one or more reference measurements from the specific subject.

9. A non-invasive method of determining the presence, concentration, or both of an analyte in a subject, comprising:

a) generating a multivariate calibration model from reference measurements of a plurality of subjects, where the reference measurements are indirect optical spectroscopic measurements and corresponding direct measurements of the presence, concentration, or both of the analyte;

b) collecting tailoring information, where tailoring information comprises reference optical spectroscopic information and corresponding direct measurement pertaining to the subject;

c) collecting sample optical spectroscopic information pertaining to the subject at a different time than the collection of the tailoring information; and

d) determining the presence, concentration, or both of the analyte from a combination of the sample optical spectroscopic information, the model, and the tailoring information.

10. The method of claim 9, wherein step d) comprises:

a) determining a relationship between the sample optical spectroscopic information and the tailoring optical spectroscopic information, and

b) determining the presence, concentration, or both of the analyte relative to the tailoring direct measurement from the determined relationship and the model.

11. The method of claim 9, wherein step d) comprises:

a) determining a relationship between a combination of the model and the sample optical spectroscopic information, and a combination of the model and the tailoring optical spectroscopic information, and

b) determining the presence, concentration, or both of the analyte relative to the tailoring direct measurement from the determined relationship.

12. The method of claim 9, wherein the analyte is glucose or a related compound.

13. The method of claim 12, wherein the optical spectroscopic information is near infrared spectroscopic information.

14. A method as in claim 9, wherein the multivariate calibration model comprises a partial least squares regression on optical spectroscopic information and corresponding reference measurements of a plurality of subjects.

15. The method of claim 9, wherein step d) comprises modifying the multivariate calibration model with the tailoring information, and applying the modified model to the spectroscopic information collected at the first time.

16. The method of claim 15, wherein modifying the multivariate calibration model comprises applying a partial least squares regression to the multivariate calibration model and the tailoring information.

17. A method of determining a presence, concentration, or both of an analyte in a subject, comprising:

a) at a first time, collecting infrared spectroscopic information pertaining to the subject;

b) determining the presence, concentration, or both of the analyte from;

(i) the infrared spectroscopic information collected at the first time;

(ii) a multivariate calibration model determined from infrared spectroscopic information collected from a plurality of subjects and corresponding direct measurements indicative of the presence, concentration, or both of the analyte for the plurality of subjects; and

(iii) tailoring information, where tailoring information comprises infrared spectroscopic information collected from the subject at a second time, different from the first time, and a corresponding direct measurement indicative of the presence, concentration, or both of the analyte in the subject at the second time.

18. The method of claim 17, wherein step b) comprises:

c) determining a relationship between the infrared spectroscopic information pertaining to the subject collected at the first time and the tailoring information; and

d) determining the presence, concentration, or both of the analyte relative to the tailoring direct measurement from the relationship determined in step c) and the model.

19. The method of claim 17, wherein step b) comprises:

c) determining a relationship between a combination of the model and the spectroscopic information collected at the first time, and a combination of the model and the tailoring spectroscopic information; and

d) determining the presence, concentration, or both of the analyte relative to the tailoring direct measurement from the determined relationship determined in step c).

20. The method of claim 17, wherein the analyte is glucose or a related compound.

21. The method of claim 20, wherein the optical spectroscopic information is near infrared spectroscopic information.

22. A method as in claim 17, wherein the multivariate calibration model comprises a partial least squares regression on the infrared spectroscopic information collected from a plurality of subjects and corresponding direct measurements indicative of the presence, concentration, or both of the analyte.

23. The method of claim 17, wherein step b) comprises modifying the multivariate calibration model with the tailoring information, and applying the modified model to the spectroscopic information collected at the first time.

24. The method of claim 23, wherein modifying the multivariate calibration model comprises applying a partial least squares regression to the multivariate calibration model and the tailoring information.