Method and apparatus for predicting the presence of congenital and acquired imbalances and therapeutic conditions

• US 5,708,591 A
• Filed: 06/07/1995
• Issued: 01/13/1998
• Est. Priority Date: 02/14/1995
• Status: Expired due to Term

• Current Assignee: Biomerieux B.V. (Compagnie Merieux Alliance SAS)
• Original Assignee: Akzo Nobel N.V. (Nouryon BV)
• Inventors: Fischer, Timothy J., Braun, Paul, Givens, Thomas B.
• Primary Examiner(s): Cosimano, Edward R.
• Assistant Examiner(s): CHOI, KYLE JAEHUN
• Application Number:
  US08/477,839
• Time in Patent Office: 951 Days
• Field of Search: 364/413.01, 364/413.02, 364/413.07, 364/413.08, 364/413.09, 364/497, 364/498, 364/496, 395/924, 356/39, 382/133, 382/134, 436/66
• US Class Current: 702/22
• CPC Class Codes: C07K 16/18 against material from animals or humans
• CPC Class Codes: G01N 2800/226 Thrombotic disorders, i.e. thrombo-embolism irrespective of location/organ involved, e.g. renal vein thrombosis, venous thrombosis
• CPC Class Codes: G01N 2800/385 Congenital anomalies
• CPC Class Codes: G01N 33/4905 Determining clotting time of blood by chemical methods G01N33/86, C12Q1/54
• CPC Class Codes: G01N 33/86 involving blood coagulating time or factors, or their receptors
• CPC Class Codes: G01N 35/00 Automatic analysis not limited to methods or materials provided for in any single one of groups G01N1/00 - G01N33/00; Handling materials therefor
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method and apparatus are disclosed for predicting the presence of at least one congenital or acquired imbalance or therapeutic condition associated with thrombosis/hemostasis from at least one time-dependent measurement profile. At least one time-dependent measurement on an unknown sample is performed and a respective property of said sample is measured over time so as to derive a time-dependent measurement profile. A set of a plurality of predictor variables are defined which sufficiently define the data of the time-dependent measurement profile. A model is then derived that represents the relationship between the congenital or acquired imbalance or therapeutic condition, and the set of predictor variables. Subsequently, the model is utilized to predict the existence of the congenital or acquired imbalance or therapeutic condition in the unknown sample.

Claims

1. A method for predicting the presence of at least one congenital or acquired imbalance or therapeutic condition associated with thrombosis/hemostasis from at least one time-dependent measurement profile, comprising:

a) performing at least one time-dependent measurement on an unknown sample and measuring a respective property over time so as to derive a time-dependent measurement profile;

b) defining a set of a plurality of predictor variables which sufficiently define the data of the time-dependent measurement profile;

c) deriving a model that represents the relationship between the congenital or acquired imbalance or therapeutic condition, and the set of predictor variables; and

d) utilizing the model of step c) to predict the existence of the congenital or acquired imbalance or therapeutic condition in the unknown sample;

wherein said set of predictor variables includes a plurality of;

a minimum of the first derivative of the profile, a time index of the minimum of the first derivative, a minimum of the second derivative of the profile, a time index of the minimum of the second derivative, a maximum of the second derivative of the profile, a time index of the maximum of the second derivative, an overall change in the coagulation parameter during the time-dependent measurement on the unknown sample, a clotting time, a slope of the profile prior to clot formation, and a slope of the profile after clot formation.

2. A method according to claim 1, wherein said at least one time-dependent measurement profile is at least one optical profile.

3. A method according to claim 2, wherein said at least one optical profile is provided by an automated analyzer for thrombosis and hemostasis testing.

4. A method according to claim 2, wherein a plurality of optical measurements at one or more wavelengths are taken over time so as to derive said at least one optical profile, said optical measurements corresponding to changes in light scattering and/or light absorption in the unknown sample.

5. A method according to claim 2, wherein a plurality of optical measurements are taken over time so as to derive said at least one optical profile, and wherein said plurality of optical measurements are each normalized to a first optical measurement.

6. A method according to claim 3, wherein in step a) said at least one optical profile is provided automatically by said analyzer, whereby said unknown sample is automatically removed by an automated probe from a sample container to a test well, one or more reagents are automatically added to said test well so as to initiate said property changes within said sample, and the development of said property over time is automatically optically monitored so as to derive said optical data profile.

7. A method according to claim 6, wherein after step d), a predicted congenital or acquired imbalance or therapeutic condition is automatically stored in a memory of said automated analyzer and/or displayed on said automated analyzer.

8. A method according to claim 6, wherein in step d), one or more assays for confirming the existence of said congenital or acquired imbalance or therapeutic condition is automatically performed.

9. A method according to claim 8, wherein said one or more confirming assays are automatically ordered and performed on said analyzer, with results of said one or more assays being stored in a memory of said automated analyzer and/or displayed on said analyzer.

10. A method according to claim 1, further comprising:

before step a), providing a set of data from known samples, which data is used in step c) for deriving said model.

11. A method according to claim 10, wherein said data from known samples is provided by performing a plurality of assays on said known samples.

12. A method according to claim 10, wherein said model of step c) is a neural network.

13. A method according to claim 1, wherein said relationship in step c) is determined via at least one automated algorithm.

14. A method according to claim 13, wherein said model is a multilayer perceptron, and wherein said at least one algorithm is a back propagation learning algorithm.

15. A method according to claim 1, wherein in step a), a plurality of time-dependent measurement profiles are derived for use in step b).

16. A method according to claim 15, wherein said plurality of time dependent measurement profiles includes at least two profiles from assays initiated with PT reagents, APTT reagents, fibrinogen reagents and TT reagents.

17. A method according to claim 1, wherein three or more of said predictor variables are within said set.

18. A method according to claim 17, wherein more than three of said predictor variables are within said set.

19. A method according to claim 1, wherein said unknown sample is a sample from a medical patient, and wherein in step d), both said model and additional patient medical data are utilized for predicting the existence of said congenital or acquired imbalance or therapeutic condition.

20. An apparatus for performing at least one time-dependent measurement on an unknown sample to derive at least one time-dependent measurement profile, and predicting the presence of at least one congenital or acquired imbalance or therapeutic condition associated with thrombosis/hemostasis from the at least one time-dependent measurement profile, comprising:

means for performing at least one time-dependent measurement on an unknown sample and measuring a respective property over time so as to derive a time-dependent measurement profile;

means for defining a set of a plurality of predictor variables which sufficiently define the data of the time-dependent measurement profile;

means for deriving a model that represents the relationship between the congenital or acquired imbalance or therapeutic condition, and the set of predictor variables; and

means for utilizing the model of step c) to predict the existence of the congenital or acquired imbalance or therapeutic condition in the unknown sample;

wherein said set of predictor variables includes a plurality of;

a minimum of the first derivative of the profile, a time index of the minimum of the first derivative, a minimum of the second derivative of the profile, a time index of the minimum of the second derivative, a maximum of the second derivative of the profile, a time index of the maximum of the second derivative, an overall change in the coagulation parameter during the time-dependent measurement on the unknown sample, a clotting time, a slope of the profile prior to clot information, and a slope of the profile after clot formation.

21. An apparatus according to claim 20, wherein said means for performing at least one time-dependent measurement comprises an optical system for performing at least one optical measurement over time and so as to derive an at least one optical profile.

22. An apparatus according to claim 21, wherein said optical system is part of an automated analyzer for thrombosis and hemostasis testing.

23. An apparatus according to claim 21, wherein said optical means comprises a means for performing a plurality of optical measurements at one or more wavelengths over time so as to derive said at least one optical profile, said optical measurements corresponding to changes in light scattering and/or light absorption in the unknown sample.

24. An apparatus according to claim 21, wherein in said optical system, a plurality of optical measurements are taken over time so as to derive said at least one optical profile, and wherein said plurality of optical measurements are each normalized to a first optical measurement.

25. An apparatus according to claim 22, which is an automated analyzer for thrombosis and hemostasis testing, and wherein said at least one optical profile is provided automatically by said analyzer, whereby said unknown sample is automatically removed by an automated probe from a sample container to a test well, one or more reagents are automatically added to said test well so as to initiate said property changes within said sample, and the development of said property over time is automatically optically monitored so as to derive said optical data profile.

26. An apparatus according to claim 25, further comprising at least one of a memory and a display wherein a predicted congenital or acquired imbalance or therapeutic condition is automatically stored in said memory of said automated analyzer and/or displayed on said display of said automated analyzer.

27. An apparatus according to claim 25, further comprising means for automatically performing one or more assays for confirming the existence of said congenital or acquired imbalance or therapeutic condition.

28. An apparatus according to claim 27, wherein said means for performing one or more confirming assays is an automatic performing means wherein said confirming assays are automatically ordered and performed on said analyzer, with results of said one or more assays being stored in a memory of said automated analyzer and/or displayed on a display of said analyzer.

29. An apparatus according to claim 20, further comprising means for providing a set of data from known samples, which data is used in step c) for deriving said model.

30. An apparatus, according to claim 29, wherein said data from known samples is provided by said means for performing a plurality of assays on said known samples.

31. An apparatus according to claim 29, wherein said means for deriving a model is a means for deriving a model by means of a neural network.

32. An apparatus according to claim 20, wherein said relationship determined by said deriving means comprises a means for determining said relationship via at least one automated algorithm.

33. An apparatus according to claim 32, wherein said model is a multilayer perceptron, and wherein said at least one algorithm is a back propagation learning algorithm.

34. An apparatus according to claim 20, wherein said means for performing at least one time-dependent measurement is capable of performing a plurality of time-dependent measurement profiles.

35. An apparatus according to claim 34, wherein said means for performing a plurality of time dependent measurement profiles includes a means for performing at least two profiles from assays initiated with PT reagents, APTT reagents, fibrinogen reagents and TT reagents.

36. An apparatus according to claim 20, wherein three or more of said predictor variables are within said set.

37. An apparatus according to claim 36, wherein more than three of said predictor variables are within said set.

38. An apparatus according to claim 20, wherein said unknown sample is a sample from a medical patient, and wherein said utilizing means comprising a means for utilizing both said model and additional patient medical data for predicting the existence of said congenital or acquired imbalance or therapeutic condition.