Methods of Determining an Analyte Concentration in a Body Fluid Sample Having Disturbance Variables, as Well as Computer Programs and Devices Therefor

Abstract

Methods are provided for deriving/determining an analyte concentration that include recording measurement values during a time development indicating a progress of a detection reaction of at least one test substance and a body fluid sample and providing at least one measurement curve F(t) containing the measurement values, where the detection reaction is known to be influenced by the analyte concentration and at least one disturbance variable Y. The methods also include deriving an end value of the measurement curve to form a first variable x1, and deriving at least one fit parameter by taking into account an exponential characteristic of the measurement curve, and where the fit parameter forms at least one second variable x2. The methods further include deriving/determining the analyte concentration by using at least one multivariate evaluation algorithm adapted to combine x1 and x2. Also provided are computer programs and devices that incorporate the same.

1 .- 23 . (canceled) 
     
     
         24 . A computer-implemented method for resource efficient analysis of at least one sample of a body fluid, the body fluid comprising at least one analyte, by which analysis a concentration c of the analyte is derived, the method comprising the following steps:
 a) using a detector to obtain a plurality of measurement values derived from a detection reaction created by contacting at least one sample of a body fluid from a subject with at least one test substance, the detection reaction detected by the detector, the detector in communication with an analytic computer comprising a processor and a memory;   b) using the processor configured to analyze the at least one sample of the body fluid by the following steps:
 recording at the memory, the plurality of measurement values by monitoring a time development of at least one measurement value indicating a progress of the detection reaction of the at least one test substance and the at least one sample of the body fluid, and providing at least one measurement curve F(t) which contains the time development of the plurality of the measurement values, wherein the measurement values contained in the measurement curve are acquired at differing points in time, wherein the measurement curve has at least one evaluation part having an exponential characteristic, wherein the detection reaction is influenced by a concentration c of an analyte to be detected in the at least one sample of the body fluid and at least one disturbance variable Y, and wherein the processor includes one or more software components configured to record the plurality of measurement values by 
   c) deriving an end value of the measurement curve provided in step b), wherein the end value forms a first variable x 1 ;   d) deriving at least one fit parameter from the measurement curve provided in step b) by taking into account the exponential characteristic of at least the evaluation part of the measurement curve, wherein the fit parameter forms at least one second variable x 2 ; and   e) deriving the concentration c of the analyte by using at least one multivariate evaluation algorithm, the multivariate evaluation algorithm combining the first variable x 1  provided by step c) and the second variable x 2  provided by step d) into a vector (x 1 , x 2 ), wherein a weighted average of the concentration c is derived from a result vector (c, Y) of at least two procedures of the method and thereby provides the determined concentration.   
     
     
         25 . The method of  claim 24 , wherein the disturbance variable Y comprises a parameter which is able to influence the viscosity of the body fluid. 
     
     
         26 . The method of  claim 24 , wherein the at least one disturbance variable is selected from the group consisting of: a particulate content of the sample and a temperature of the sample. 
     
     
         27 . The method of  claim 24 , wherein the exponential characteristic contains at least one exponential function selected from the group consisting of:
 F(t)=a+b*exp[−Γ*t], wherein t is the time, a is an offset, b is a contrast and Γ is a decay constant;   F(t)=a+b*exp[−(Γ*t) β ], wherein t is the time, a is an offset, b is a contrast, Γ is a decay constant and β is a stretching parameter.   
     
     
         28 . The method of  claim 24 , wherein the second variable x 2  is selected from the decay constant Γ or from a quantity which is in relationship with the decay constant Γ. 
     
     
         29 . The method of  claim 24 , wherein, in step d), the fit parameter is obtained from a comparison of the first order derivative of the measurement curve at two differing points in time. 
     
     
         30 . The method of  claim 29 , wherein the two differing points in time are obtained by applying two differing threshold values. 
     
     
         31 . The method of  claim 29 , wherein two differing values for the two differing points in time are used, wherein each of the two differing values are in the vicinity of a threshold value. 
     
     
         32 . The method of  claim 24 , wherein, in step e), further the at least one disturbance variable Y is determined. 
     
     
         33 . The method of  claim 24 , wherein a forecast model provides a probability of each specific value of the at least one disturbance variable Y. 
     
     
         34 . The method of  claim 24 , wherein the multivariate evaluation algorithm is determined by using a plurality of calibration measurements. 
     
     
         35 . The method of  claim 24 , wherein the evaluation part of the measurement curve is a remainder of the measurement curve starting after a definable starting time span after a commencement of a measurement. 
     
     
         36 . The method of  claim 24 , wherein, in step b), a slope of the measurement curve is compared to at least one threshold value for determining if the measurement curve has reached the end value. 
     
     
         37 . The method of  claim 24 , wherein difference values of neighboring measurement values of the measurement curve are formed and compared to the at least one threshold value. 
     
     
         38 . The method of  claim 24 , wherein, in step d), a first order derivative F′(t) or a higher order derivative F n (t) of the measurement curve is formed before deriving the fit parameter. 
     
     
         39 . The method of  claim 38 , wherein the first or higher order derivative is approximated by calculating differences between neighboring measurement values. 
     
     
         40 . The method of  claim 24 , wherein, in step d), a ratio of two subsequent derivatives F n (t) and F n+1 (t) of the measurement curve is formed, the ratio forming the fit parameter. 
     
     
         41 . The method of  claim 24 , wherein, in step d), an integral is formed over the measurement curve F(t) or a first order or higher order derivative of F(t), the integral forming the fit parameter. 
     
     
         42 . The method of  claim 24 , wherein, in step c), the end value is derived from at least one measurement value of the measurement curve and, in step d), the at least one second variable is derived from at least one fit parameter from the measurement curve. 
     
     
         43 . The method of  claim 24 , wherein, in step c), the end value is derived from an earlier part of the measurement curve, wherein the earlier part is a part of the measurement curve being distant from a plateau of the measurement curve.