Analytical device with prediction module and related methods

• US 20040253736A1
• Filed: 08/28/2003
• Published: 12/16/2004
• Est. Priority Date: 06/06/2003
• Status: Abandoned Application

• Current Assignee: Lifescan Incorporated (Duv Holding Corporation)
• Original Assignee: Lifescan Incorporated (Duv Holding Corporation)
• Inventors: Melander, Todd, Stout, Phil
• Application Number:
  US10/652,464
• Publication Number:
  US 20040253736A1
• Time in Patent Office: Days
• US Class Current: 436/43
• CPC Class Codes: A61B 2560/0431 Portable apparatus, e.g. comprising a handle or case
• CPC Class Codes: A61B 2562/0295 Strip shaped analyte sensors for apparatus classified in A61B5/145 or A61B5/157 disposable multilayered or laminated electrodes G01N27/307; details of handling chemical test elements G01N33/4875
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/022 by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/1486 using enzyme electrodes, e.g. with immobilised oxidase
• CPC Class Codes: A61B 5/150022 for capillary blood or interstitial fluid
• CPC Class Codes: A61B 5/150358 Strips for collecting blood, e.g. absorbent optical reagent test strips G01N21/8483; chemical reagent test strips G01N33/4875, G01N33/52
• CPC Class Codes: A61B 5/157 Devices characterised by integrated means for measuring characteristics of blood
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: Y10T 436/11 Automated chemical analysis

Abstract

An analytical device for predicting a subject'"'"'s whole blood analyte concentration based on the subject'"'"'s interstitial fluid (ISF) analyte concentration includes an ISF sampling module, an analysis module and a prediction module. The ISF sampling module is configured to sequentially extract a plurality of ISF samples from a subject. The analysis module is configured to sequentially determining an ISF analyte concentration (e.g., ISF glucose concentration) in each of the ISF samples, resulting in a series of ISF analyte concentrations. The prediction module is configured for storing the series of ISF analyte concentrations and predicting the subject'"'"'s whole blood analyte concentration based on the series by performing at least one algorithm. A method for predicting a subject'"'"'s whole blood analyte concentration based on the subject'"'"'s interstitial fluid analyte concentration includes extracting a plurality of interstitial fluid (ISF) samples from a subject in a sequential manner and sequentially determining an ISF analyte concentration in each of the plurality of ISF samples to create a series of ISF analyte concentrations. The subject'"'"'s blood analyte concentration is then predicted based on the series of ISF analyte concentrations by performing at least one algorithm.

Claims

1. An analytical device for predicting a subject'"'"'s whole blood analyte concentration based on the subject'"'"'s interstitial fluid analyte concentration, the analytical device comprising:

an interstitial fluid sampling module for extracting a plurality of interstitial fluid (ISF) samples from a subject in a sequential manner;

an analysis module for sequentially determining an ISF analyte concentration in each of the plurality of ISF samples, thereby creating a series of ISF analyte concentrations; and

a prediction module for storing the series of ISF analyte concentrations and predicting the subject'"'"'s whole blood analyte concentration based on the series of ISF analyte concentrations by performing at least one algorithm of the following general form;

PC=f(ISF_i^k, rate_j, significant interaction terms)

where;

PC=the predicted subject'"'"'s whole blood analyte concentration;

i is an integer with predetermined values selected from the values of 0, 1, 2, 3, 4 and 5;

j is an integer with predetermined values selected from the values of 1, 2, 3, 4 and 5;

k is an integer with predetermined values selected from the values of 1 and 2;

ISF_i is a measured ISF analyte concentration in the series of ISF analyte concentrations;

rate_j is a rate of change between immediately adjacent ISF analyte concentrations in the series of ISF analyte concentrations; and

significant interaction terms=statistically significant interaction terms involving terms selected from the group consisting of ISF_i^k and rate_j.

2. The analytical device of claim 1, wherein i=0, k=1, j=2, 3, 4 and 5 and interaction terms=rate₂*rate₁, rate₁*rate₃, rate₂*rate₄, and rate₂*rate₂*rate_4.

3. The analytical device of claim 1, wherein the analyte is glucose.

4. The analytical device of claim 1, wherein the predicting the subject'"'"'s whole blood analyte concentration is based on the series of ISF analyte concentrations by performing at least one algorithm of the following general form:

PC=f(ISF_i^k, rate_j, ma_nrate_m^p, significant interaction terms)

where;

p is an integer with predetermined values selected from the values of 1 and 2;

n and m are integers with predetermined values selected from the values of 1, 2 and 3;

ma_nrate_m is the moving average rate between adjacent averages of groupings of ISF values; and

significant interaction terms=statistically significant interaction terms involving terms selected from the group consisting of ISF_i^k, rate_j, and ma_nrate_m^p.

5. The analytical device of claim 4, wherein the prediction module predicts the subject'"'"'s whole blood analyte concentration by determining whether the series of ISF analyte concentrations is indicative of a rising ISF analyte concentration or a falling ISF analyte concentration, selecting an algorithm based on the determination and performing the selected algorithm.

6. The analytical device of claim 5, wherein the prediction module predicts the subject'"'"'s whole blood analyte concentration by determining whether the series of ISF analyte concentrations is indicative of a rising ISF analyte concentration or a falling ISF analyte concentration based on an ma_nrate_m.

7. The analytical device of claim 6, wherein the algorithm employed for a falling ISF analyte concentration is:

PC=8.23ma1rate1+0.88ISF₃+12.04ma1rate2+10.54rate1+1.71rate1*rate2−

0.056ISF*rate1+0.71(rate1)^2+0.68(rate2)²+0.0014(ISF)²_—

sq−

0.0011 (ISF₃)².

8. The analytical device of claim 6, wherein the algorithm employed for a rising ISF analyte concentration is:

PC=4.13ISF″

1.51ISF₃31 1.69ISF₃−

37.06ma1rate2+13.67ma3rate1−

28.35rate1−

3.56rate1*rate2+0.10ISF*rate1+0.15ISF*rate2+0.47rate1*rate2*rate3−

1.13(rate3)²−

0.0061(ISF)²+0.0060(ISF₂)².

9. The analytical device of claim 4, wherein the analyte is glucose.

10. The analytical device of claim 1, wherein the series of ISF analyte concentrations includes five ISF analyte concentrations.

11. The analytical device of claim 1, wherein the sampling module extracts the plurality of ISF samples at a time interval in the range of five to fifteen minutes.

12. A method for predicting a subject'"'"'s whole blood analyte concentration based on the subject'"'"'s interstitial fluid analyte concentration, the method comprising:

extracting a plurality of interstitial fluid (ISF) samples from a subject in a sequential manner;

sequentially determining an ISF analyte concentration in each of the plurality of ISF samples, thereby creating a series of ISF analyte concentrations; and

predicting the subject'"'"'s blood analyte concentration based on the series of ISF analyte concentrations by performing at least one algorithm of the following form;

PC=f(ISF_i^k, rate_j, significant interaction terms)

where;

PC=the predicted subject'"'"'s whole blood analyte concentration;

i is an integer with predetermined values selected from the values of 0, 1, 2, 3, 4 and 5;

j is an integer with predetermined values selected from the values of 1, 2, 3, 4 and 5;

k is an integer with predetermined values selected from the values of 1 and 2;

ISF_i is a measured ISF analyte concentration in the series of ISF analyte concentrations;

rate_j is the rate of change between adjacent ISF analyte concentrations in the series of ISF analyte concentrations; and

significant interaction terms=statistically significant interaction terms involving terms selected from the group consisting of ISF_i^k and rate_j.

13. The method of claim 12, wherein the predicting set employs an algorithm of the form:

PC=f(ISF_i^k, rate_j, ma_nrate_m^p, significant interaction terms)

where;

p is an integer with predetermined values selected from the values of 1 and 2;

m and n are integers with predetermined values selected from the values of 1, 2 and 3;

ma_nrate_m is the moving average rate between adjacent averages of groupings of ISF values; and

significant interaction terms=statistically significant interaction terms involving terms selected from the group consisting of ISF_i^k, rate_j, and ma_nrate_m^p.

14. The method of claim 13, wherein the predicting step predicts the subject'"'"'s whole blood analyte concentration by determining whether the series of ISF analyte concentrations is indicative of a rising ISF analyte concentration or a falling ISF analyte concentration, selecting the algorithm based on the determination and performing the selected algorithm.

15. The method of claim 13, wherein the predicting step predicts the subject'"'"'s whole blood analyte concentration by determining whether the series of ISF analyte concentrations is indicative of a rising ISF analyte concentration or a falling ISF analyte concentration based on an ma_nrate_m.

16. The method of claim 12, wherein the extracting step extracts a plurality of ISF samples from a subject'"'"'s dermis.