Method for the early diagnosis of subacute, potentially catastrophic illness

• US 6,856,831 B2
• Filed: 01/29/2001
• Issued: 02/15/2005
• Est. Priority Date: 03/17/1998
• Status: Expired due to Term

• Current Assignee: University of Virginia Patent Foundation (University of Virginia)
• Original Assignee: University of Virginia Patent Foundation (University of Virginia)
• Inventors: Moorman, J. Randall, Griffin, M. Pamela
• Primary Examiner(s): Sykes, Angela D.
• Assistant Examiner(s): Oropeza, Frances P.
• Application Number:
  US09/770,653
• Publication Number:
  US 20030100841A1
• Time in Patent Office: 1,478 Days
• Field of Search: 600/372, 600/373, 600/374, 600/377, 600/382, 600/508, 600/509, 600/513, 600/515, 600/516, 600/517, 600/519, 600/521
• US Class Current: 600/515
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02411 of foetuses
• CPC Class Codes: A61B 5/344 Foetal cardiography
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/4362 Assessing foetal parameters
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• 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 50/50 for simulation or modelling of medical disorders

Abstract

In one aspect of the invention, there is provided a method for early detection of subacute, potentially catastrophic illness in an infant. The method comprises: (a) monitoring heart rate variability in the infant; and (b) identifying at least one characteristic abnormality in the heart rate variability that is associated with the illness. This method can be used to diagnose illnesses such as, but not limited to, sepsis, necrotizing enterocolitis, pneumonia and meningitis. In another aspect of the present invention, there is provided a method for early detection of subacute, potentially catastrophic illness in an infant, which comprises: (a) monitoring the patient'"'"'s RR intervals; (b) generating a normalized data set of the RR intervals; (c) calculating one or more of (i) moments of the data set selected from the third and higher moments and (ii) percentile values of the data set; and (d) identifying an abnormal heart rate variability associated with the illness based on one or more of the moments and the percentile values.

Claims

1. A method for early detection of subacute, potentially catastrophic illness in an infant comprising:

(a) monitoring frequency histograms of RR intervals in the infant;

(b) identifying at least one characteristic abnormal pattern or distribution; and

(c) correlating the at least one abnormal pattern or distribution with said illness.

2. The method of claim 1, wherein the illness is infectious.

3. The method of claim 2, wherein antibiotic therapy is initiated and a diagnostic work-up for the illness, comprising obtaining a blood culture from the patient, is provided when the at least one characteristic abnormal pattern or distribution is identified.

4. The method of claim 2, wherein the illness is necrotizing enterocolitis.

5. The method of claim 4, wherein a diagnostic work-up for the illness, comprising an X-ray of the infant or a pathological specimen from the infant, is provided when the at least one characteristic abnormal pattern or distribution is identified.

6. The method of claim 2 wherein the illness is selected from the group consisting of pneumonia, sepsis, and meningitis.

7. The method of claim 1, wherein a diagnostic work-up is provided when the at least one characteristic abnormal pattern or distribution is identified.

8. The method of claim 1, wherein the infant is a neonate.

9. The method of claim 1, wherein the at least one characteristic abnormal pattern or distribution is identified from a data set of RR intervals.

10. The method of claim 9, wherein the data set is normalized.

11. The method of claim 10, wherein the data set contains on the order of about 10³ to 10⁴ sequential RR intervals.

12. The method of claim 10, wherein the at least one characteristic abnormal pattern or distribution is identified based on at least one of the third and higher moments of the data set.

13. The method of claim 12, wherein the at least one moment of the data set includes the skewness of the data set.

14. The method of claim 13, wherein the illness is sepsis or necrotizing enterocolitis.

15. The method of claim 12, wherein the at least one moment of the data set includes the kurtosis of the data set.

16. The method of claim 15, wherein the illness is sepsis or necrotizing enterocolitis.

17. The method of claim 10, wherein the at least one characteristic abnormal pattern or distribution is identified based on at least one percentile value of the data set.

18. The method of claim 17, wherein the at least one percentile value is the 10^th percentile value.

19. The method of claim 18, wherein the illness is sepsis or necrotizing enterocolitis.

20. The method of claim 10, wherein the at least one characteristic abnormal pattern or distribution is identified based on the variance, standard deviation or coefficient of variation of the data set.

21. The method of claim 20, wherein the illness is sepsis or necrotizing enterocolitis.

22. The method of claim 14, further comprising a diagnostic work-up.

23. The method of claim 15, further comprising a diagnostic work-up.

24. The method of claim 8, further comprising a diagnostic work-up.

25. The method of claim 20, further comprising a diagnostic work-up.

26. An apparatus for early detection of subacute, potentially catastrophic infectious illness in a patient, wherein the patient is an infant, a newborn infant, a toddler, or a child, the apparatus comprising:

(a) a monitoring device, continuously monitoring frequency histograms of RR intervals in the patient; and

(b) a microprocessor, identifying at least one characteristic abnormal pattern or distribution in the RR intervals that is associated with the illness.

27. The apparatus of claim 26, wherein the microprocessor performs the step of generating a normalized data set of RR intervals.

28. The apparatus of claim 27, wherein the microprocessor calculates one or more of the third and higher moments of the data set and identifies the characteristic abnormal pattern or distribution based on the one or more moments.

29. The apparatus of claim 28, wherein the microprocessor calculates the skewness of the data set and identifies the characteristic abnormal pattern or distribution based on the skewness.

30. The apparatus of claim 28, wherein the microprocessor calculates the kurtosis of the data set and identifies the characteristic abnormal pattern or distribution based on the kurtosis.

31. The apparatus of claim 27, wherein the microprocessor calculates one or more percentile values of the data set and identifies the characteristic abnormal pattern or distribution based on the one or more percentile values.

32. The apparatus of claim 31, wherein the microprocessor calculates the 10th percentile value of the data set and identifies the characteristic abnormal pattern or distribution based on the 10th percentile value.