Method for evaluating a set of measurement data from an oral glucose tolerance test

• US 20130297224A1
• Filed: 07/03/2013
• Published: 11/07/2013
• Est. Priority Date: 01/07/2011
• Status: Abandoned Application

• Current Assignee: Roche Diabetes Care Inc. (Roche Holding AG)
• Original Assignee: Roche Diagnostics Operations Incorporated (Roche Holding AG)
• Inventors: Quarder, Ortrud, Werner, Gerhard, Staib, Arnulf
• Application Number:
  US13/935,151
• Publication Number:
  US 20130297224A1
• Time in Patent Office: Days
• US Class Current: 702/21
• CPC Class Codes: G01N 2800/00 Detection or diagnosis of diseases
• CPC Class Codes: G01N 33/66 involving blood sugars, e.g. galactose
• CPC Class Codes: G09B 23/28 for medicine
• CPC Class Codes: G16C 99/00 Subject matter not provided for in other groups of this subclass

Abstract

A method is provided for evaluating a set of measurement data from an oral glucose tolerance test. The method may include calculating a similarity measure that quantifies the similarity between a time profile of the series of measured data of the glucose concentration and a corresponding glucose reference profile. The method may include calculating a further similarity measure that quantifies the similarity between the profile of the series of measured values of the further analyte concentration and the corresponding analyte sample profile, wherein the data set is represented by a point in a vector space that comprises coordinate axes that are formed by the similarity measures, whereby the coordinates of said point contain the calculated values of the similarity measures. The method also may include evaluating the position of the point with respect to reference points, which each represent a defined state of health, in order to calculate a parameter that specifies the state of the glucose metabolism of the patient.

Claims

1. A method for evaluating a set of measurement data from an oral glucose tolerance test, whereby the set of measurement data includes a series of measurement data of the glucose concentration and, in addition, at least one series of measurement data of a further analyte concentration, the method comprising:

calculating, by at least one computing device, a similarity measure that quantifies the similarity between a time profile of the series of measured data of the glucose concentration and a corresponding glucose reference profile, wherein the calculation of the similarity measure uses the series of measured data of the glucose concentration and one each of several predefined glucose reference profiles;

calculating, by the at least one computing device, one value each of a further similarity measure that quantifies the similarity between the profile of the series of measured values of the further analyte concentration and the corresponding analyte sample profile, wherein the calculation of one value each of a further similarity measure uses the series of measured values of the further analyte concentration and one each of several predefined analyte reference profiles, wherein the data set is represented by a point in a vector space that comprises coordinate axes that are formed by the similarity measures, whereby the coordinates of said point contain the calculated values of the similarity measures; and

evaluating, by the least one computing device, the position of the point with respect to reference points, which each represent a defined state of health, in order to calculate a parameter that specifies the state of the glucose metabolism of the patient.

2. The method of claim 1, wherein the step of evaluating, by the least one computing device, the position of the point characterizing the set of measurement data is evaluated with respect to the reference points by projecting the point onto a norm trajectory which follows a disease progression in said vector space from a healthy normal patient via a pre-diabetic conditions to a diabetic disease and contains at least a fraction of the reference points, wherein the length of a section of the trajectory from the start of the trajectory to the point of the trajectory onto which the point representing the set of measurement data was projected is used to determine the parameter specifying the state of glucose metabolism.

3. The method of claim 2, wherein the vector space comprises multiple norm trajectories, each of which specify different disease progressions from a healthy normal patient via a pre-diabetic condition to an insulin-dependent diabetic disease, whereby the point characterising the set of measurement data is projected onto the norm trajectory situated at the smallest distance from it.

4. The method of claim 3, wherein the point characterizing the set of measurement data is, in addition, also projected onto a second norm trajectory situated at the second smallest distance from it.

5. The method of claim 1, wherein the concentration profiles are normalized before calculating the similarity measures.

6. The method of claim 1, wherein the similarity measures are calculated as scalar product of vectors, whereby one of the vectors is determined from the corresponding series of measured values and the other vector is determined from the corresponding sample profile.

7. The method of claim 5, wherein the similarity measures are each calculated as scalar product of two normalized vectors.

8. The method of claim 1, wherein a norm of a vector formed from the series of measured values of the glucose concentration is used as a further coordinate of the vector space.

9. The method of claim 1, wherein a norm of a vector formed from the series of measured values of the further analyte concentration is used as further coordinate of the vector space.

10. The method of claim 1, wherein at least one coordinate axis of the vector space specifies the value of a biometric or genetic variable that is measured independent of a concentration measurement.

11. The method of claim 10, wherein the biometric variable is the body mass index, fraction of body fat, waist-to-hip ratio, blood pressure or heart rate.

12. The method of claim 1, wherein the further analyte concentration is the concentration of a secretory hormone.

13. The method of claim 1, wherein at least one coordinate axis of the vector space specifies the concentrations of a metabolite that shows no or little change on the time scale of an oral glucose tolerance test.

14. A non-transitory computer-readable medium comprising:

executable instructions such that when executed by at least one processor cause the at least one processor to;

calculate a similarity measure that quantifies the similarity between a time profile of the series of measured values of the glucose concentration and a corresponding glucose reference profile, wherein the calculation of the similarity measure uses the series of measured values of the glucose concentration and one each of several predefined glucose reference profiles;

calculate one value each of a further similarity measure that quantifies the similarity between the profile of the series of measured values of the further analyte concentration and the corresponding analyte sample profile, wherein the calculation of one value each of a further similarity measure uses the series of measured values of the further analyte concentration and one each of several predefined analyte reference profiles, wherein the data set is represented by a point in a vector space that comprises coordinate axes that are formed by the similarity measures, whereby the coordinates of said point contain the calculated values of the similarity measures; and

evaluate the position of the point with respect to reference points, which each represent a defined state of health, in order to calculate a parameter that specifies the state of the glucose metabolism of the patient.