Methods of evaluating medical measurement curves, as well as computer programs and devices therefor

Patent metadata

Field	Value
Publication number	US-10984892-B2
Application number	US-201916502363-A
Country	US
Kind code	B2
Filing date	Jul 3, 2019
Priority date	Dec 20, 2012
Publication date	Apr 20, 2021
Grant date	Apr 20, 2021

Abstract

Methods are provided for detecting an analyte concentration/presence in a body fluid sample that include providing a set of at least two different evaluation rules, each evaluation rule adapted to derive a set characteristic values from an optical measurement curve, where at least one first characteristic value is derived from at least one first evaluation rule and at least one second characteristic value is derived from at least one second evaluation rule. The methods also include performing at least one multivariate analysis of the at least one first and second characteristic values by using at least one predetermined multivariate evaluation algorithm to derive at least one estimate value for at least one target variable Y of the state variables. The methods also include determining at least one analyte concentration by using the at least one target variable Y. Also provided are computer programs and devices that incorporate the same.

First claim

The invention claimed is:

1. A method for determining at least one analyte concentration in a body fluid sample, the method comprising the steps of:
a) obtaining, using a measuring unit, a plurality of measurement values that each indicate a progress of a detection reaction of at least one test substance and at least one body fluid sample at differing points in time, wherein the detection reaction is influenced by a set of state variables, each state variable characterizing at least one of a state of the body fluid sample and a condition of the detection reaction;
b) generating, using at least one processor, at least one optical measurement curve, wherein the optical measurement curve contains the plurality of measurement values;
c) providing, using the at least one processor, a set of at least two different evaluation rules, the at least two evaluation rules adapted to derive a characteristic value from at least two derivatives of the optical measurement curve, thereby deriving a set of characteristic values X={X i } l = 1 . . . N from the optical measurement curve, the set of characteristic values comprising at least one first characteristic value being derived from the optical measurement curve by using at least one first evaluation rule from the set of evaluation rules and at least one second characteristic value being derived from the optical measurement curve by using at least one second evaluation rule from the set of evaluation rules, the second evaluation rule being different from the first evaluation rule;
d) performing, using the at least one processor, at least one multivariate analysis of the at least one first characteristic value and of the at least one second characteristic value by using at least one predetermined multivariate evaluation algorithm, the at least one multivariate evaluation algorithm adapted to derive at least one result from at least two variables, wherein the at least one first characteristic value and the at least one second characteristic value are used as the at least two variables, thereby deriving at least one estimate value for at least one target variable Y of the state variables; and
e) determining, using the at least one processor, at least one analyte concentration by using the at least one target variable Y.

2. The method of claim 1 , wherein the state variables are selected from the group consisting of a composition of the body fluid sample; a content of at least one particulate component of the body fluid sample; a temperature of the body fluid sample; a humidity of an ambient atmosphere surrounding the body fluid sample; a storage time of the test substance; an interfering substance; alterations of the body fluid sample or of certain properties of the body fluid sample caused by pharmacological treatment of a donor of the body fluid sample.

3. The method of claim 2 , wherein the particulate component of the body fluid sample is a hematocrit.

4. The method of claim 1 , wherein the first evaluation rule may not be transformed into the second evaluation rule by a time transformation.

5. The method of claim 1 , wherein the second evaluation rule differs from the first evaluation rule in at least one of: in at least one coefficient, in at least one parameter, and in at least one component related to the at least one predetermined multivariate evaluation algorithm.

6. The method of claim 1 , wherein a third evaluation rule is provided, wherein in step d), the at least one first characteristic value is derived from the first evaluation rule, and wherein in the at least one multivariate evaluation algorithm, the second evaluation rule or the third evaluation rule is used depending on the at least one first characteristic value.

7. The method of claim 1 , wherein the first characteristic value is determined by using a first time interval of the optical measurement curve, wherein the second characteristic value is determined by using a second time interval of the optical measurement curve, and wherein the first time interval of the optical measurement curve is different from the second time interval of the optical measurement curve.

8. The method of claim 7 , wherein the target variable Y is different from the at least one analyte concentration.

9. The method of claim 1 , wherein the target variable Y comprises the at least one analyte concentration in the body fluid sample.

10. The method of claim 1 , wherein in step e), in addition to the at least one target variable Y, at least one electrochemical measurement value is used for determining the at least one analyte concentration, and wherein the electrochemical measurement value is determined by using at least one electrochemical measurement.

11. The method of claim 10 , wherein by using the electrochemical measurement value, an approximated value of the at least one analyte concentration in the body fluid sample is determined, and wherein the target variable Y is used for correcting the approximated value.

12. The method of claim 1 , wherein the predetermined multivariate evaluation algorithm comprises at least one polynomial algorithm selected from:
Y=A·X,   (1);

Y=X T ·A·X,   (2); and

Y=X T ·( X T ·A·X ),  (3),

wherein A is a one-dimensional, a two-dimensional or a three-dimensional evaluation tensor.

13. The method of claim 1 , wherein the predetermined multivariate evaluation algorithm comprises at least one algorithm selected from:
Y=Σ i a i ·X i ,  (4);

Y=Σ i a i ·X i +Σ i,j a ij ·X i ·X j ,  (5); and

Y=Σ i a i ·X i +Σ i,j a ij ·X i ·X j +Σ i,j,k a ijk ·X i ·X j ·X k ,  (6).

wherein a i , a ij , a ijk are predetermined coefficients, and wherein i, j and k are, mutually independently, integers from 1 to N.

14. The method of claim 1 , wherein the at least one multivariate evaluation algorithm comprises a function involving at least one decision tree, and wherein the decision tree comprises at least one decision branch that allows selecting one out of at least two alternative procedures based on an assessment whether a predetermined condition may be fulfilled.

15. The method of claim 1 , wherein at least one of the two different evaluation rules is selected from the group consisting of:
i) using a specific measurement value of the optical measurement curve or a derivative of the optical measurement curve at a predetermined point in time as the characteristic value;
ii) using a mean value of the optical measurement curve or a derivative of the optical measurement curve over a predetermined period of time as the characteristic value;
iii) using a characteristic point in time of the optical measurement curve or of a derivative of the optical measurement curve as the characteristic value;
iv) using a characteristic parameter of the optical measurement curve or of a derivative of the optical measurement curve as the characteristic value;
v) using a fit parameter derived by at least one fitting process as the characteristic value, wherein the fitting process implies a fitting of at least one predetermined fit curve to at least a section of the optical measurement curve or of a derivative of the optical measurement curve; and
vi) using at least one value derived from a phase plot of at least two derivatives of different order of the optical measurement curve as the characteristic value, wherein the phase plot comprises at least one phase space curve.

16. The method of claim 1 , wherein step c) comprises generating the set of evaluation rules, and wherein generating of the set o

Assignees

• Roche Diabetes Care Inc

Inventors

• Aigner Simon
• Chemnitius Gabriele
• Horn Carina
• Limburg Bernd
• Ottenstein Timo
• Petrich Wolfgang
• Plum Markus
• Ringemann Christian
• Serr Markus

Classifications

• G16B40/00 Primary

  ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding · CPC title

• G16B40/20

  Supervised data analysis · CPC title

• G16B40/10 Primary

  Signal processing, e.g. from mass spectrometry [MS] or from PCR · CPC title

• Y10T436/144444

  Glucose · CPC title

• G01N21/8483

  Investigating reagent band (test-element handling not specific to a test method G01N33/4875; analytical elements specific to chemical analysis of biological material G01N33/52; autometer with reagent band G01N35/04) · CPC title