System and method of increasing sample throughput

• US 9,113,833 B2
• Filed: 08/16/2012
• Issued: 08/25/2015
• Est. Priority Date: 08/16/2011
• Status: Active Grant

• Current Assignee: Instrumentation Lab Co (Instrumentation Lab)
• Original Assignee: Instrumentation Lab Co (Instrumentation Lab)
• Inventors: Mansouri, Sohrab, Cervera, Jose Maria, Moraski, Ashley
• Primary Examiner(s): HARWARD, SOREN T
• Application Number:
  US13/587,431
• Publication Number:
  US 20130046483A1
• Time in Patent Office: 1,104 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/1486 using enzyme electrodes, e.g. with immobilised oxidase
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: C12Q 1/002 Electrode membranes
• CPC Class Codes: C12Q 1/006 for glucose
• CPC Class Codes: G01N 2035/00702 Curve-fitting; Parameter matching; Calibration constants
• CPC Class Codes: G01N 2035/0097 monitoring reactions as a function of time
• CPC Class Codes: G01N 27/327 Biochemical electrodes , e.g. electrical or mechanical details for in vitro measurements
• CPC Class Codes: G01N 33/48792 Data management, e.g. communication with processing unit for in vivo diagnostics A61B5/0002; transmission systems for measured values G08C
• CPC Class Codes: G01N 33/50 Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing measuring or testing processes involving enzymes or microorganisms, compositions or test papers therefor; processes for forming such compositions, condition responsive control in microbiological or enzymological processes C12Q
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis

Abstract

Technologies for increasing sample throughput by predicting the end point response time of a sensor for the analysis of an analyte in a sample are disclosed. In one aspect, a system includes a sensor that generates data signals associated with the measurement of an analyte within the sample. A processor records appropriate data points corresponding to the signals, converts them to a logarithmic function of time scale, and plots the converted data points. The processor then determines a curve that fits the plotted data points and determines a curve fitting equation for the curve. Once the equation is determined, the processor extrapolates an end point response of the sensor using the equation. A value, such as analyte concentration, is then calculated using the extrapolated end point response.

Claims

1. A method for determining and improving usefulness of the curve fitting equation obtained from data from a sensor, the method comprising;

a) receiving, from the sensor, data signals generated in response to being exposed to an analyte within a sample;

b) recording data points associated with the data signals;

c) selecting a series of data points corresponding to a portion of a kinetic region time range from the recorded data points;

d) determining a curve fitting equation that fits the series of data as a logarithmic scale of time, wherein the curve fitting equation is of the form s(t)=a*(log(t))^2−

2aV(log(t))+c, and V is a log of a time at which extremum occurs, wherein t represents time and a and c are the fit parameters for second order polynomial;

e) determining an outlier candidate with a largest residual;

f) comparing a residual of the outlier candidate with the largest residual to a predetermined residual limit;

g) classifying the outlier candidate with the largest residual as an outlier if the residual of the outlier candidate with the largest residual is greater than the predetermined residual limit;

h) obtaining a measure of effect of the outlier on the parameters of the curve fitting equation;

i) comparing the measure of the effect of the outlier to a predetermined measure limit;

j) incrementing an outlier count, if the measure of the effect of the outlier is greater than the predetermined measure limit;

k) comparing the outlier count to a predetermined outlier number limit, if the measure of the effect of the outlier is greater than the predetermined measure limit; and

l) removing the outlier from the data points, if the measure of the effect of the outlier is greater than the predetermined measure limit, resulting in an analyzed set of data points, thereby increasing sample throughput.

2. The method of claim 1 further comprising:

determining a curve fitting equation that fits a series of data from the analyzed set of data points as a function of time; and

,

m) repeating steps e) to l) for the analyzed set of data points.

3. The method of claim 1 further comprising:

forming an iteration set of data points by removing the outlier from the data points, if the measure of the effect of the outlier is at most equal to the predetermined measure limit;

determining a curve fitting equation that fits a series of data from the iteration set of data points as a function of time; and

m) repeating steps e) to l) for the iteration set of data points.

4. The method of claim 1 further comprising:

identifying the data points for review, if the outlier count is greater than the predetermined outlier number limit.

5. The method of claim 1 further comprising:

comparing each one fit parameter from a set of fit parameters for the curve fitting equation to a predetermined fit parameter limit for said one fit parameter; and

identify the data points for review, if at least one fit parameter from the set of fit parameters is greater than the predetermined fit parameter limit for said one fit parameter.

6. The method of claim 1 wherein determining an outlier candidate with the largest residual comprises determining a data point with a largest Studentized residual;

and wherein obtaining a measure of the effect of the outlier comprises obtaining a DFFITS value.

7. A computer-readable storage medium having computer executable instructions stored thereon, which when executed by a computer, cause the computer to:

a) receive, from a sensor, data signals generated in response to being exposed to an analyte within a sample;

b) determine a curve fitting equation that fits the series of data as a logarithmic scale of time, wherein the curve fitting equation is of the form s(t)=a*(log(t))^2−

2aV(log(t))+c, and V is a log of a time at which extremum occurs, wherein t represents time and a and c are the fit parameters for second order polynomial;

c) determine an outlier candidate with a largest residual;

d) compare a residual of the outlier candidate with the largest residual to a predetermined residual limit;

e) classify the outlier candidate with the largest residual as an outlier if the residual of the outlier candidate with the largest residual is greater than the predetermined residual limit;

f) obtain a measure of effect of the outlier on the parameters of the curve fitting equation;

g) compare the measure of the effect of the outlier to a predetermined measure limit;

h) increment an outlier count, if the measure of the effect of the outlier is greater than the predetermined measure limit;

i) compare the outlier count to a predetermined outlier number limit, if the measure of the effect of the outlier is greater than the predetermined measure limit; and

j) remove the outlier from the data points, if the measure of the effect of the outlier is greater than the predetermined measure limit, resulting in an analyzed set of data points;

k) determine, if the measure of the effect of the outlier is greater than the predetermined measure limit, a curve fitting equation that fits the analyzed set of data points as a logarithmic function of time;

l) repeat, if the measure of the effect of the outlier is greater than the predetermined measure limit, steps c) to j) for the analyzed set of data points; and

m) identify the data points for review, if the outlier count is greater than the predetermined outlier number limit.

8. The computer-readable storage medium of claim 7 having further computer-executable instructions stored thereon, which when executed by the computer cause the computer to:

form an iteration set of data points by removing the outlier from the data points, if the measure of the effect of the outlier is at most equal to the predetermined measure limit;

determine a curve fitting equation that fits a series of data from the iteration set of data points as a function of time; and

repeat steps c) to j) for the iteration set of data points.

9. The computer-readable storage medium of claim 7 having further computer-executable instructions stored thereon, which when executed by the computer cause the computer to:

compare each one fit parameter from a set of fit parameters for the curve fitting equation to a predetermined fit parameter limit for said one fit parameter; and

identify the data points for review, if at least one fit parameter from the set of fit parameters is greater than the predetermined fit parameter limit for said one fit parameter.