Methods, Systems, and Devices for Analyzing Patient Data

• US 20110245634A1
• Filed: 04/01/2011
• Published: 10/06/2011
• Est. Priority Date: 04/03/2010
• Status: Abandoned Application

• Current Assignee: Lifescan Incorporated (Duv Holding Corporation)
• Original Assignee: Lifescan Incorporated (Duv Holding Corporation)
• Inventors: Price, David, Ray, Pinaki
• Application Number:
  US13/078,711
• Publication Number:
  US 20110245634A1
• Time in Patent Office: Days
• US Class Current: 600/309
• 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 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1486 using enzyme electrodes, e.g. with immobilised oxidase
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• 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: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Described herein is a method of analyzing an analyte distribution from discrete, quasi-continuous or continuous measurements to determine a glycemic state of a patient in order to understand how often, and for how long, a patient'"'"'s post-prandial glucose is out of control without requiring laboratory blood test and especially post-prandial levels laboratory analysis. The systems, devices, and methods assist in predicting risk levels of developing diabetes-associated complications. Therefore applicants have recognized also a need for a tool which facilitates stratification of patients for risk of and/or onset of one or more complications having the same HbA1c level.

Claims

1. ) A method of analyzing an analyte distribution from discrete, quasi-continuous or continuous measurements comprising:

defining a predetermined time period T;

performing n analyte measurements G_i each associated with a time t_i within predetermined time period T with each analyte measurement by a transformation of analyte disposed in body fluid into an enzymatic by-product disposed in body fluid into an enzymatic by-product;

repeating step (b) for N predetermined time periods T;

aggregating the analyte measurements G_i to determine the number of occurrences of each value of G_i across N predetermined time periods T; and

fitting a curve y=f(G_i) to the number of occurrences versus the value of G to determine an estimated probability y=f(G) of the value G occurring within any given predetermined time period T_i.

3. ) The method according to one of claim 1 or 2 in which the step of collecting comprises measuring n analyte measurements G_i at times t_i within predetermined time period T.

4. ) The method according to one of claim 1 or 2 in which the steps of collecting and repeating comprise receiving n analyte measurements G_i taken at times t_i within predetermined time period T for N predetermined time periods.

5. ) The method according to any one of the preceding claims further comprising displaying, storing and/or transmitting the estimated probability of occurrence y=f(G) of at least one value of G.

6. ) The method according to claim 5 further comprising displaying a numeric value of the estimated probability of occurrence y=f(G) at of least one value of G.

7. ) The method according to one of the preceding claims further comprising displaying the estimated probability of occurrence of a range of values of G.

8. ) The method according to one of the preceding claims comprising displaying at least a portion of the fitted curve y=f(G).

9. ) The method according to any one of claims 1 to 8, further comprising determining a lower limit L₁ and calculating a first excursion area A₁ under a first fitted probability curve above limit L₁ representing the probability of measurement occurring above limit L₁.

10. ) The method according to claim 9, further comprising determining a Figure of Merit for a patient by:

measuring a corresponding patient characteristic; and

forming a mathematical relationship between the first excursion area A₁ and the measured characteristic.

11. ) The method according to claim 10 in which the mathematical relationship comprises a product of the first excursion area and the measured characteristic.

12. ) The method according to any one of the preceding claims further comprising selecting N prior to commencing collecting data.

13. ) The method according to any one of the preceding claims further comprising displaying, storing and/or transmitting N and/or T.

14. ) The method according to any one of the preceding claims in which N is increased by 1 after each completed predetermined time period T.

15. ) The method according to any one of the preceding claims in which N is selected from a group comprising 2, 5, 7, 14, 28, 30, 56, 60, 84, 90, 112, 120 or 240.

16. ) The method according to any one of the preceding claims in which the predetermined time period T is selected from the group of 1, 2, 3, 4, 6, 12, 24, 48, 72, 96, 168 hours.

17. ) The method according to any one of the preceding claims in which the step of aggregating to determine the number of occurrences of G is carried out for each range of G including G±

Δ

G, G+Δ

G, G−

Δ

G.

18. ) The method according to any one of the preceding claims in which the analyte measurement G comprises a concentration of the analyte.

19. ) The method according to claim 18 in which the range Δ

G is selected from the group of +10, −

10, +15, −

15, +20, −

20, +25, −

25, ±

10, ±

15, ±

20, ±

25 when G is measured in mg/dl or from the group of +0.1, −

0.1, +0.15, −

0.15, +0.2, −

0.2, +0.25, −

0.25, +0.5, −

0.5, +0.75, −

0.75, +1, −

1, ±

0.1, ±

0.15, ±

0.2, ±

0.25, ±

0.5, ±

0.75, ±

1 when G is measured in mmol/L.

20. ) The method according to any one of the preceding claims in which the analyte comprises glucose.

21. ) The method to claim 20 in which the probability of occurrence of analyte measurement is determined over a range of values of G and the range(s) is/are selected from the group of glucose concentration of less than 100 mg/dL, less than 126 mg/dL, less than 140 mg/dL, less than 200 mg/dL, greater than or equal to 100 mg/dL, greater than or equal to 126 mg/dL, greater than or equal to 140 mg/dL, greater than or equal to 200 mg/dL, greater than or equal to 100 mg/dL and less than 126 mg/dL, greater than or equal to 140 mg/dL and less than 200 mg/dL.

2. ) A method of analyzing an analyte distribution from discrete, quasi-continuous or continuous measurements comprising:

defining a predetermined time period T;

providing a measuring device to perform n analyte measurements G_i in a body fluid each associated with a time t_i within predetermined time period T with each analyte measurement by a transformation of analyte disposed in body fluid into an enzymatic by-product disposed in body fluid into an enzymatic by-product;

repeating the step of collecting at step (b) for N predetermined time periods T;

providing a microprocessor adapted to aggregate the analyte measurements G_i to determine the number of occurrences of each value of G_i across N predetermined time periods T;

operating the microprocessor to fit a curve y=f(G_i) to the number of occurrences versus the value of G to determine an estimated probability y=f(G) of the value G occurring within any given predetermined time period T_i; and

determining for a user an estimated probability of occurrence of at least one value of G from the fitted curve.

22. ) A device for analyzing an analyte distribution from discrete, quasi-continuous or continuous measurements comprising:

a collector that obtains analyte measurements G_i;

a microprocessor that receives the analyte measurements, the microprocessor programmed to;

define a predetermined time period T;

collect n analyte measurements G_i each associated with a time t_i within predetermined time period T;

repeat step (b) for N predetermined time periods T;

aggregate the analyte measurements G_i to determine the number of occurrences of each value of G_i across N predetermined time periods T;

fit a curve y=f(G) to the number of occurrences versus the value of G to determine an estimated probability y=f(G) of the value G occurring within any given predetermined time period T_i; and

determine for a user an estimated probability or occurrence of at least one value of G from the fitted curve.

23. ) The device according to claim 22 in which the collector comprises a measuring circuit to measure analyte measurements G_i.

24. ) The device according to claim 23 in which the collector comprises a receiver to receive analyte measurements G_i from a separate measuring device.

25. ) The device according to any one of claim 22, 23 or 24 comprising one or more of a display transmitter or memory to display, transmit, or store the estimated probability of occurrence of at least one value of analyte measurement G.

26. ) The device according to any one of claims 22 to 25 comprising a user interface to receive at least one piece of information and forward the same to the microprocessor.

27. ) The device according to claim 26 wherein the at least one piece of information is/are selected from the group of i) setting predetermined time period T, ii) updating predetermined time period T, iii) setting N, iv) updating N, v) selecting a portion of the curve y=f(G), vi) selecting a numeric value of at least one probability of a value of G, vii) selecting an excursion area under a probability density curve of G viii) selecting a characteristic estimated from an excursion area ix) selecting one of “

IN RANGE”

, “

OUT OF RANGE”

message x) selecting one of “

HIGH RISK”

, “

LOW RISK”

, “

ACCEPTABLE RISK”

messages, xi) selecting display and/or transmission and/or storage of any of the above.

28. ) The device according to any one of claims 22 to 27, further comprising:

a first component comprising a measurement circuit to measure analyte measurements G_i; and

a second component separate from said first device comprising a microprocessor to receive the analyte measurements and programmed to;

aggregate the analyte measurements G_i to determine the number of occurrences of each value of G_i across N predetermined time periods T;

fit a curve y=f(G) to the number of occurrences versus the value of G to determine an estimated probability y=f(G) of the value G occurring within any given predetermined time period T_i; and

in which said first and second components each comprising a communication circuit for communication therebetween.

29. ) The device according to claim 28 further comprising one or more of a display, transmitter and/or memory to display, transmit, or store the probability of occurrence of at least one value of analyte measurement G.