Methods for selecting, developing and improving diagnostic tests for pregnancy-related conditions

• US 20030105731A1
• Filed: 06/04/2002
• Published: 06/05/2003
• Est. Priority Date: 08/14/1997
• Status: Active Grant

• Current Assignee: Cytyc Corporation (Hologic Incorporated)
• Original Assignee: Cytyc Corporation (Hologic Incorporated)
• Inventors: DeSieno, Duane D., Lapointe, Jerome
• Granted Patent: US 7,228,295 B2
• Time in Patent Office: Days
• US Class Current: 706/15
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Decision-support systems, such as neural networks, for assessing the risk of delivery within a predetermined time or the likelihood of preterm delivery are provided.

Claims

1. A decision support system trained for assessing the risk of imminent delivery within in a predetermined time frame or the likelihood of preterm delivery.

2. The decision support system of claim 1 that comprises a neural network.

3. A computer readable medium comprising the decision support system of claim 1.

4. A computer readable medium comprising the neural network of claim 2.

5. The decision support system of claim 1 that asseses the likelihood of preterm delivery.

6. The decision support system of claim 1 that asseses the risk of delivery within in a predetermined time.

7. The decision support system of claim 6, wherein the time is 7 days.

8. The decision support system of claim 6, wherein the time is 14 days.

9. The decision support system of claim 1, wherein the risk of delivery before 35 weeks is assessed.

10. The decision support system of claim 5 that is a neural network.

11. The decision support system of claim 6 that is a neural network.

12. The decision support system of claim 7 that is a neural network.

13. The decision support system of claim 8 that is a neural network.

14. The decision support system of claim 9 that is a neural network.

15. The decision support system of claim 7, wherein input variables comprise variables containing at least three and up to all of the responses to the following queries:

Ethnic Origin Caucasian;

Uterine contractions with or without pain;

Parity-abortions;

vaginal bleeding at time of sampling;

uterine contractions per hour; and

No previous pregnancies.

16. The decision support system of claim 8, wherein input variables comprise variables containing at least three and up to all of the responses to the following queries:

Ethnic Origin Hispanic;

Marital Status living with partner;

Uterine contractions with or without pain;

Cervical dilatation;

Uterine contractions per hour; and

No previous pregnancies.

17. The decision support system of claim 9, wherein input variables comprise variables containing at least three and up to all of the responses to the following queries:

Ethnic Origin Caucasian;

Marital Status living with partner;

EGA by sonogram;

EGA at sampling;

estimated date of delivery by best;

cervical dilatation (CM);

parity-preterm;

vaginal bleeding at time of sampling;

cervical consistency at time of sampling; and

previous pregnancy without complication is provided.

18. The decision support system of claim 15 that is a neural network.

19. The decision support system of claim 16 that is a neural network.

20. The decision support system of claim 17 that is a neural network.

21. The decision support system of claim 15, wherein the variables further comprise the results from one or more of a biochemical test that detects fetal fibronectin in cervico/vaginal samples, determines the level of a local inflammatory product protein in cervico/vaginal samples;

or determines estriol or estretol in saliva.

22. The decision support system of claim 16, wherein the variables further comprise the results from one or more of a biochemical test that detects fetal fibronectin in cervico/vaginal samples, determines the level of a local inflammatory product protein in cervico/vaginal samples;

or determines estriol or estretol in saliva.

23. The decision support system of claim 17, wherein the variables further comprise the results from one or more of a biochemical test that detects fetal fibronectin in cervico/vaginal samples, determines the level of a local inflammatory product protein in cervico/vaginal samples;

or determines estriol or estretol in saliva.

24. The decision support system of claim 21 that is a neural network.

25. The decision support system of claim 22 that is a neural network.

26. The decision support system of claim 23 that is a neural network.