U.S. patent number 6,804,551 [Application Number 09/793,653] was granted by the patent office on 2004-10-12 for method and apparatus for the early diagnosis of subacute, potentially catastrophic illness.
This patent grant is currently assigned to University of Virginia Patent Foundation. Invention is credited to M. Pamela Griffin, Boris P. Kovatchev, J. Randall Moorman.
United States Patent |
6,804,551 |
Griffin , et al. |
October 12, 2004 |
Method and apparatus for the early diagnosis of subacute,
potentially catastrophic illness
Abstract
In one aspect of the invention, there is provided a method and
apparatus for early detection of subacute, potentially catastrophic
illness in a patient. The method comprises: (a) monitoring heart
rate variability in the patient; and (b) identifying at least one
characteristic abnormality in the heart rate variability that is
associated with the illness. This method can be use to diagnose
illnesses such as, but not limited to, sepsis, necrotizing
enterocolitis, pneumonia and meningitis, as well as noninfectious
illnesses. In another aspect of the present invention, there is
provided a method and apparatus for early detection of subacute,
potentially catastrophic illness in a patient. The method
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 second and higher moments, including the standard
deviation (ii) percentile values of the data set, (iii) sample
entropy, and (iv) sample asymmetry; and (d) identifying an abnormal
heart rate variability associated with the illness based on one or
more of the moments, the percentile values, sample entropy, and
sample asymmetry analysis.
Inventors: |
Griffin; M. Pamela
(Charlottesville, VA), Moorman; J. Randall (Charlottesville,
VA), Kovatchev; Boris P. (Amherst, VA) |
Assignee: |
University of Virginia Patent
Foundation (Charlottesville, VA)
|
Family
ID: |
25160461 |
Appl.
No.: |
09/793,653 |
Filed: |
February 27, 2001 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
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770653 |
Jan 29, 2001 |
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271279 |
Mar 17, 1999 |
6216032 |
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Current U.S.
Class: |
600/515 |
Current CPC
Class: |
A61B
5/7275 (20130101); A61B 5/02405 (20130101); A61B
5/4362 (20130101); G16H 10/60 (20180101); A61B
5/344 (20210101); A61B 5/349 (20210101); A61B
5/412 (20130101); G16H 50/20 (20180101); A61B
5/02411 (20130101); G16H 15/00 (20180101); G16H
50/50 (20180101) |
Current International
Class: |
A61B
5/0444 (20060101); A61B 5/0402 (20060101); G06F
19/00 (20060101); G06F 17/00 (20060101); A61B
5/024 (20060101); A61B 005/045 () |
Field of
Search: |
;600/372-374,377,382,508-509,513,515-517,519,521 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
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Variability", Physical Review Letters, vol. 74, No. 7, pp.
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Variability In Preterm Infants", Early Human Development, vol. 37,
No. 2, pp. 117-131 (May 1994). .
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Rate Variability",Journal of Cardiovascular Electrophysiology, Vo.
5, No. 2, pp. 112-124 (Feb. 1994). .
J.S. Richman, et al.: "Physiological Time-Series Analysis Using
Approximate Entropy and Sample Entropy," Am. J. Physiology Heart
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Diagnosis of Neonatal Sepsis and Sepsis-Like Illness Using Novel
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Variability In The Sepsis Syndrome", Clinical Autonomic Research,
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Respiratory Distress Syndrome On Heart Rate Variability In Very
Preterm Infants", Early Human Development, vol. 27, No. 3, pp.
207-221 (Dec. 1991). .
Marc Boucher, et al.: Perinatal Listeriosis (Early-Onset):
Correlation Of Antenatal Manifestations And Neonatal Outcome,
Obstetrics and Gynecology, vol. 68, No. 5, pp. 593-597 (Nov. 1986).
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Ronald D. Berger, et al.: "An Efficient Algorithm For Spectral
Analysis Of Heart Rate Variability", IEEE Transactions on
Biomedical Engineering, vol. BME-33, No. 9, pp. 900-904 (Sep.
1986). .
Patricia Braly, et al.: "Fetal Heart Rate Patterns In Infants In
Whom Necrotizing Enterocolitis Develops", Archieves of Surgery,
American Medical Association Publication, vol. 115, pp. 1050-1053
(Jan.-Dec. 1980). .
Luis A. Cabal, et al.: "Factors Affecting Heart Rate Variability In
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|
Primary Examiner: Jastrzab; Jeffrey R.
Assistant Examiner: Oropeza; Frances P.
Attorney, Agent or Firm: Decker; Robert J.
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a Continuation-In Part Application of U.S.
patent application Ser. No. 09/770,653 (filed Jan. 29, 2001), which
is a Continuation Application of U.S. patent application Ser. No.
09/271,279 (filed Mar. 17, 1999) (now U.S. Pat. No. 6,216,032B1),
which claims priority under 35 U.S.C. .sctn.119(e) of Provisional
Application No. 60/078,319 (filed Mar. 17, 1998), which
applications are incorporated herein in their entireties.
Claims
We claim:
1. A method for early detection of subacute, potentially
catastrophic illness in a patient comprising: (a) monitoring the
patient's frequency histograms of RR, intervals; (b) generating a
data set of the frequency histograms of RR intervals; (c)
calculating one or more of (i) moments of the data set selected
from the second and higher moments, including standard deviation
(ii) percentile values of the data set, (iii) sample entropy, and
(iv) sample asymmetry, (d) identifying an abnormal heart rate
variability associated with the illness based on one or more of the
moments and the percentile values, the sample entropy and the
sample asymmetiy analysis.
2. The method of claim 1, wherein the moments include the second
moment of the data set.
3. The method of claim 1, wherein the moments include the third
moment and the standard deviation of the data set.
4. The method of claim 1, wherein the moments include the fourth
moment of the data set.
5. The method of claim 1, wherein the percentile values include
about the 10th and/or 50.sup.th percentile value.
6. The method of claim 1, wherein the sample entropy is
calculated.
7. The method of claim 1, wherein the sample asymmetry is
calculated.
8. The method of claim 1 wherein step (c) is carried out using a
multivariable statistical analysis selected from the group
consisting of but not limited to multivariable regression analysis,
neural networks, k-nearest neighbor analysis, and combinations
thereof.
9. The method of claim 8 wherein the multivariable statistical
analysis is multivariable regression analysis.
10. The method of claim 8 wherein the multivariable statistical
analysis is a neural network.
11. The method of claim 8 wherein the multivariable statistical
analysis is k-nearest neighbor analysis.
12. The method of claim 8 wherein the patient is a neonate.
13. The method of claim 8 wherein the patient is an infant.
14. The method of claim 8 wherein the patient is a toddler.
15. The method of claim 8 wherein the patient is a child.
16. An apparatus for early detection of subacute, potentially
catastrophic infectious illness in a patient comprising (1) a
monitoring device, which monitors the patient's frequency
histograms of RR intervals, and (2) a microprocessor, said
microprocessor performing steps comprising: (a) generating a data
set of the frequency histograms of RR intervals; (b) calculating
one or more of (i) moments of the data set selected from the second
and higher moments, (ii) percentile values of the data set, (iii)
sample entropy, and (iv) sample asymmetry, (c) identifying an
abnormal heart rate variability associated with the illness based
on one or more of the moments, the percentile values, the sample
entropy and the sample asymmetry analysis.
17. The apparatus of claim 16, wherein the microprocessor
calculates the second moment of the data set.
18. The apparatus of claim 16, wherein the microprocessor
calculates the third moment of the data set.
19. The apparatus of claim 16, wherein the microprocessor
calculates the fourth moment of the data set.
20. The apparatus of claim 16, wherein the microprocessor
calculates the 10.sup.th and/or 50.sup.th percentile of the data
set.
21. The apparatus of claim 16, wherein the microprocessor
calculates the sample entropy.
22. The apparatus of claim 16, wherein the microprocessor
calculates sample asymmetry of the data set.
23. The application of claim 16, wherein the microprocessor
performs step (b) by carrying Out using a multivariable
statatistical analysis selected from the group including but not
limited to multivariable regression analysis, neural networks,
k-nearest neighbor analysis, and combinations thereof, k-nearest
neighbor analysis, neural networks, or combination thereof.
Description
FIELD OF THE INVENTION
The present invention relates to the indication of early phases of
potentially catastrophic illnesses and relates to heart rate
variability monitoring in patients. In particular, the present
invention relates to methods and apparatus for early detection of
potentially catastrophic illnesses in a patient.
BACKGROUND OF THE INVENTION
Approximately 40,000 very low birth weight ("VLBW") infants (less
than 1,500 gm) are born in the United States each year. Ventura et
al., "Advance Report of Final Natality Statistics, 1994," Monthly
Vital Statistics Report; 44, pp. 1-88 (1996). Survival of this
group has improved with advances in neonatal intensive care, but
late-onset sepsis and necrotizing enterocolitis ("NEC") continue to
be major causes of morbidity and mortality. Stoll B. J., Gordon T.,
Korones S. B., Shankaran S., Tyson J. E., Bauer C. R., "Late-onset
Sepsis in Very Low Birth Weight Neonates: A Report from the
National Institute of Child Health and Human Development Neonatal
Research Network," Journal of Pediatrics; 129:63-71 (1996); Gray J.
E., Richardson D. K., McCormick M. C., Goldmann D. A.,
"Coagulase-Negative Staphylococcal Bacteremia Among Very Low Birth
Weight Infants: Relation to Admission Illness Severity, Resource
Use, and Outcome," Pediatrics, 95:225-230 (1995). Unfortunately
these illnesses are common in neonates, and infected infants have a
significant increase in the number of days spent on the ventilator
and an average increase in duration of hospital stay of 25 days.
See Stoll et al. above.
Neonatal sepsis occurs in as many as 25% of infants weighing less
than 1,500 gm at birth, and the rate is about 1 per 100 patient
days. Gladstone, I. M., R. A. Ehrenkrantz, S. C. Edberg, and R. S.
Baltimore, "A Ten-Year Review of Neonatal Sepsis and Comparison
with the Previous Fifty Year Experience," Pediatric Infectious
Disease Journal; 9:819-825 (1990); Moro, M. L., A. DeToni, I.
Stolfi, M. P. Carrieri, M. Braga, and C. Zunin, "Risk Factors for
Nosocomial Sepsis in Newborn Infants and Intermediate Care Units,"
European Journal of Pediatrics; 155:315-322 (1996). The National
Institute of Child Health & Human Development ("NICHD")
Neonatal Research Network found that neonates who develop
late-onset sepsis have a 17% mortality rate, more than twice the 7%
mortality rate of noninfected infants.
Risk factors for late-onset sepsis are ubiquitous in the neonatal
intensive care unit ("NICU"): intubation, umbilical catheters,
prolonged mechanical ventilation, low birth weight, parenteral
nutrition via central venous catheters, respiratory distress
syndrome, bronchopulmonary dysplasia, severe intraventricular
hemorrhage, and nasogastric and tracheal cannulae are all
independently associated with sepsis. See Moro et al. supra. Each
interventional device represents a potential source of infection
and increases the risk of catastrophic infectious illness. Id.
Necrotizing enterocolitis affects up to 4,000 infants in the U.S.
yearly, and an estimated 10 to 50% of infants who develop NEC die.
Neu, J., "Necrotizing Enterocolitis," Pediatric Clinics of North
America 43:409-432 (1996). Infants who develop NEC often require
intubation and an increase in respiratory support. Survivors are
often left with strictures and short-bowel syndrome.
Unfortunately, prior to the discovery of the present invention
there has been no reliable clinical means for early diagnosis of
these diseases. Clinical neonatologists caring for these VLBW
infants recognize sepsis and NEC as potentially catastrophic
illnesses, and thus do not hesitate to obtain blood cultures and
administer antibiotics empirically at the first appearance of
symptoms in an attempt to avert disaster. Likewise, physicians do
not hesitate to stop feeding and obtain radiographic studies should
any abdominal finding occur. Early diagnosis of neonatal sepsis is
difficult (Escobar, G. J, "The Neonatal "Sepsis Work-up": Personal
Reflections on the Development of an Evidence-Based Approach Toward
Newborn Infections in a Managed Care Organization," Pediatrics,
103:360-373 (1999)), as the clinical signs are neither uniform nor
specific. Because of this, there are many unnecessary blood
cultures, many unnecessary administration of short courses of
antibiotics to infants without bacterial infection, and many
unnecessary interruptions in neonatal nutrition. Moreover, despite
these practices, sepsis and necrotizing enterocolitis continue to
occur and continue to cause neonatal deaths. Indeed, by the time
clinical signs and symptoms for either sepsis or NEC have
developed, the illness may have progressed to an irreversible
stage.
In addition, not all patients with clinical signs of sepsis have
positive blood cultures. While the blood culture is felt to be the
gold standard for establishing the diagnosis of sepsis due to
systemic bacterial infection, there are concerns regarding its
reliability (Kaftan, H. and J. S. Kinney, "Early Onset Neonatal
Bacterial Infections," Seminars in Perinatology, 22:15-24 (1998)),
especially if single samples of small volume are submitted
(Aronson, M. D. and D. H. Bor, "Blood Cultures," Ann. Intern. Med.,
106:246-253 (1987); Kellogg, J. A., F. L. Ferrentino, M. H.
Goodstein, J. Liss, Shapiro, S L, and D. A. Bankert, "Frequency of
Low Level Bacteremia in Infants from Birth to Two Months of Age,"
Pediatric Infectious Disease Journal, 16:381-385 (1997)), as is
often the practice in critically ill newborn infants. For example,
as many as 60% of culture results may be falsely negative if only
0.5 mL blood is obtained from infants with low-colony-count sepsis.
Schelonka, R. L., M. K. Chai, B. A. Yoder, D. Hensley, R. M.
Brockett, and D. P. Ascher, "Volume of Blood Required to Detect
Common Neonatal Pathogens," J. Pediatr., 129:275-278 (1996). In a
study of 298 aerobic culture specimens, the mean blood volume
submitted was 0.53 mL and 55% of samples contained less than 0.5
mL. Neal, P. R., M. B. Kleiman, J. K. Reynolds, S. D. Allen, J. A.
Lemons, and P. L. Yu, "Volume of Blood Submitted for Culture from
Neonates," Journal of Clinical Microbiology, 24:353-356 (1986). It
is suspected that 30-40% of all infants with sepsis have negative
blood cultures. For example, in two studies, approximately 20% of
infants with infection proven by post-mortem cultures and autopsy
were not so identified using pre-mortem blood cultures (Pierce, J.
R., G. B. Merenstein, and J. T. Stocker, "Immediate Postmortem
Cultures in an Intensive Care Nursery," Pediatric Infectious
Disease, 3:510-513 (1984); Squire, E., B. Favara, and J. Todd,
"Diagnosis of Neonatal Bacterial Infection: Hematologic and
Pathologic Findings in Fatal and Nonfatal Cases," Pediatrics,
64:60-64 (1970)).
The current hypothesis is that the clinical syndrome of sepsis is
brought about by the host response as a response to insults such as
bacterial infection. The major host response is the release of
cytokines, small circulating peptides that serve as mediators of
the inflammatory response. The syndrome common to sepsis and
sepsis-like illness has been named the Systemic Inflammatory
Response Syndrome (SIRS) (Members of the ACCP/SCCM Consensus
Conference Committee, "American College of Chest Physicians/Society
of Critical Care Medicine Consensus Conference: Definitions for
Sepsis and Organ Failure and Guidelines for the Use of Innovative
Therapies in Sepsis," Critical Care Medicine, 20:864-874 (1992)),
and the pathogenesis suggested to be an imbalance between
pro-inflammatory and anti-inflammatory effects of cytokines. Bone,
R. C., C. J. Grodzin, and R. A. Balk, "Sepsis: a New Hypothesis for
Pathogenesis of the Disease Process," Chest, 112:235-243 (1997). In
sepsis and sepsis-like illness, circulating cytokines play a major
role in initiating and maintaining the inflammatory response, and
cytokine levels correlate with the severity of illness. Anderson,
M. R. and J. L. Blumer, "Advances in the Therapy for Sepsis in
Children," Pediatric Clinics of North America, 44:179-205 (1997);
Harris, M. C., A. T. J. Costarino, J. S. Sullivan, S. Dulkerian, L.
McCawley, L. Corcoran, S. Butler, and L. Kilpatrick, "Cytokine
Elevations in Critically Ill Infants with Sepsis and Necrotizing
Enterocolitis," J. Pediatr., 124:105-111 (1994); Glauser, M. P., D.
Heumann, J. D. Baumgartner, and J. Cohen, "Pathogenesis and
Potential Strategies for Prevention and Treatment of Septic Shock:
an Update," Clinical Infectious Diseases," 18:S205-S216 (1994).
Kuster and colleagues have recently found elevated levels of
circulating cytokines for up to two days prior to the clinical
diagnosis of clinical sepsis. Kuster, H., M. Weiss, A. E.
Willeitner, S. Detlefsen, I. Jeremias, J. Zbojan, R. Geiger, G.
Lipowsky, and G. Simbruner, "Interleukin-1 Receptor Antagonist and
Interleukin-6 for Early Diagnosis of Neonatal Sepsis 2 Days Before
Clinical Manifestation," Lancet, 352:1271-1277 (1998). Cytokines
have widespread effects on signal transduction processes and may
interfere with normal events of Heartrate ("HR") control by the
sympathetic and parasympathetic nervous systems. For example, the
cytokines TNF-.alpha., IL-1.beta. and IL-6 increase HR, but they
blunt HR responses to .beta.-adrenergic agonists. Oddis, C. V. and
M. S. Finkel, "Cytokines and Nitric Oxide Synthase Inhibitor as
Mediators of Adrenergic Refractoriness in Cardiac Myocytes,"
European Journal of Pharmacology, 320:167-174 (1997); Oddis, C. V.,
R. L. Simmons, B. G. Hattler, and M. S. Finkel, "Chronotropic
Effects of Cytokines and the Nitric Oxide Synthase Inhibitor,
L-NMMA, on Cardiac Myocytes," Biochemical & Biophysical
Research Communications, 205:992-997 (1994). In addition, sepsis
and sepsis-like illness are associated with alterations in
beta-adrenergic receptor number and distribution, (Tang, C., J.
Yang, and M. S. Liu, "Progressive Internalization of
Beta-Adrenoceptors in the Rat Liver During Different Phases of
Sepsis," Biochimica et Biophysica Acta, 1407:225-233 (1998); Hahn,
P. Y., P. Yoo, Z. F. Ba, I. H. Chaudry, and P. Wang, "Upregulation
of Kupffer Cell Beta-Adrenoceptors and cAMP Levels During the Late
Stage of Sepsis," Biochimica et Biophysica Acta,
1404:377-384(1998)) and with multiple steps of signal transduction
via b-adrenergic receptors. Bernardin, G., A. D. Strosberg, A.
Bernard, M. Mattei, and S. Marullo, "Beta-Adrenergic
Receptor-Dependent and -Independent Stimulation of Adenylate
Cyclase Is Impaired During Severe Sepsis in Humans," Intensive Care
Medicine, 24:1315-1322 (1998).
Clinicians have found no clinical signs or laboratory test findings
to be reliable for very early diagnosis of neonatal sepsis. In
fact, 10 to 20 infants are treated for sepsis for every one infant
that has a positive blood culture. Gerdes, J. S. and R. A. Polin,
"Sepsis Screen in Neonates with Evaluation of Plasma Fibronectin,"
Pediatric Infectious Disease Journal, 6:443-446 (1987). Thus, a
successful surveillance strategy which leads to an earlier
diagnosis of potentially catastrophic illnesses such as sepsis and
NEC as well as non-infectious illnesses in neonates and premature
newborns is necessary and critical in decreasing mortality and
morbidity. Moreover, such a surveillance strategy is also useful
for detecting potentially catastrophic illnesses in other patients,
including infants, toddlers, young children, adolescents and
adults. The present invention provides such surveillance
strategies. Using the novel surveillance strategies of the present
invention, the inventors have found that abrupt clinical
deteriorations that prompted physicians to obtain blood cultures
and start antibiotics were proceeded for up to 24 hours by
increasing abnormal heart rate characteristics "HRC" of reduced
baseline variability and sub-clinical, short-lived decelerations in
HR, and by increasingly abnormal Score for Neonatal Acute
Physiology "SNAP" scores.
Heretofore, heart rate variability ("HRV") measurement has been
used as a means of assigning long-term prognosis, usually in adults
with heart disease. Additionally, since it is known that HRV is
abnormal during illness, physicians have traditionally measured HRV
as an indication of such illnesses. For example, in healthy newborn
infants, time series of heart period (or RR intervals, the time
between successive heart beats) show obvious variability. Numerous
publications are available which detail the measurement and
characterization of such heart rate variability. See, e.g., Ori,
Z., G. Monir, J. Weiss, X. Sayhouni, and D. H. Singer, "Heart Rate
Variability: Frequency Domain Analysis," Cardiology Clinics
10:499-533 (1992); Kleiger, R. E., P. K. Stein, M. S. Bosner, and
J. N. Rottman, "Time Domain Measurements of Heart Rate
Variability," Cardiology Clinics 10:487-498 (1992).
HRV arises from the interplay of the sympathetic and
parasympathetic arms of the autonomic nervous system, which act
respectively to speed or slow the heart rate. In newborn infants,
as in adults, HRV is substantially reduced during severe illness.
Burnard, E. D., "Changes in Heart Size in the Dyspnoeic Newborn
Infant." Brit Med J 1:1495-1500 (1959); Rudolph, A. J., C.
Vallbona, and M. M. Desmond, "Cardiodynamic Studies in the Newborn,
III. Heart Rate Patterns in Infants with Idiopathic Respiratory
Distress Syndrome," Pediatrics 36:551-559 (1965); Cabal, L. A., B.
Siassi, B. Zanini, J. E. Hodgman, and E. E. Hon, "Factors Affecting
Heart Rate Variability in Preterm Infants," Pediatrics 65:50-56
(1980); Griffin, M. P., D. F. Scollan, and J. R. Moorman, "The
Dynamic Range of Neonatal Heart Rate Variability," J Cardiovasc.
Electrophysiol 5:112-124 (1994).
These measurements, however, typically involve only a single
measurement of HRV and do not include multivariable logistic
regression analysis or other mulitvariable predictive statistical
models. In addition, these conventional measures of HRV fail to
detect abnormal HRV in patients because the measurements, such as
standard deviation and power are optimized only to detect low
variability. Some types of abnormal HRV patterns do not have low
variability and must be detected using other kinds of measures. The
present invention overcomes the deficiencies in conventional HRV
measurements, and thus is useful as a means of early diagnosis of
potentially catastrophic illnesses such as sepsis and necrotizing
enterocolitis. These novel measures thus serve to quantify
well-established markers of early fetal and neonatal distress, and
they add to clinical observations by detecting sub-clinical changes
in HRC.
In addition, the new measures have the advantage of reliability in
data sets with missed beats, unlike conventional frequency domain
measures of heart rate time series. (Bemtson, G. G. and J. R.
Stowell, "ECG Artifacts and Heart Period Variability: Don't Miss a
Beat," Psychophysiol, 35:127-132 (1998).; Schechtman, V. L., K. A.
Kluge, and R. M. Harper, "Time Domain System for Assessing
Variations in Heart Rate," Med Biol. Eng. Comp., 26:367-373
(1988)).
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an exemplary Heart Rate Characteristic ("HRC")
analysis.
FIGS. 1A to 1C show three 4096 beat RR interval time series. Their
corresponding frequency histograms (note the logarithmic ordinate)
are shown to the right in
FIGS. 1D to 1F. All were recorded from the same infant who had an
abrupt clinical deterioration because of coagulase-negative
staphylococcal septicemia and an enterococcal urinary tract
infection. Panel A shows a normal heart rate time series recorded 6
days before the event. Panels B and C show abnormal heart rate time
series recorded within three to six hours before the clinical
suspicion of sepsis. The abnormalities are reduced baseline
variability and short-lived decelerations of heart rate. A
representative deceleration is marked by an arrow in FIG. 1B. The
y-axis is RR interval, and the upward spike represents longer RR
intervals and thus, a slower rate. The data in FIG. 1C show many
such decelerations. These changes lead to asymmetry of the
frequency histograms, with positive skewness, that is, there is a
longer tail extending toward higher values of RR intervals.
FIG. 2 depicts the results of a time course of HRC measures. Data
points summarize a six-hour epoch ending at the time value on the
abscissa. Day 0, which ends at the time of the abrupt clinical
deterioration, is marked with a vertical line and the word CRASH
(Cultures, Resuscitation and Antibiotics Started Here). Bars are
standard error of the mean. Panel A depicts the mean RR interval.
Panel B depicts standard deviation (S.D.). As can be seen, there
are no significant differences in the mean RR interval and standard
deviation (A and B). Panel C depicts skewness and panel D depicts
the 50.sup.th percentile data point, or the median (p50). The novel
HRC measures skewness and p50 change over the 24 hours before the
event in the sepsis and sepsis-like illness groups as shown in
Panels C and D.
FIG. 3 illustrates a time course of clinical scores. Data points
are the mean score over 24 hours; bars are standard error of the
mean ("S.E.M"). Both SNAP and NTISS are higher in the sepsis and
sepsis-like illness groups. In the 24 hours S.E.M.=standard error
of the mean; SNAP=Score for Neonatal Acute Physiology;
NTISS=Neonatal Therapeutic Intervention Scoring System;
CRASH=Cultures, Resuscitation and Antibiotics Started Here.
FIG. 4 illustrates a time course of Receiver Operating
Characteristic ("ROC") areas. Panels A and B illustrate HRC
measures. Panel C illustrates clinical scores and panel D
illustrates regression models combining HRC and clinical scores.
Abbreviation: S.D. is standard deviation; p10, p50 and p90 are the
10.sup.th, 50.sup.th, and 90.sup.th percentile data points
respectively; SNAP is Score for Neonatal Acute Physiology; NTISS is
Neonatal Therapeutic Intervention Scoring System; CRASH is
Cultures, Resuscitation and Antibiotics Started Here.
FIG. 5 is a schematic block diagram of an embodiment of the
apparatus of the invention.
SUMMARY OF THE INVENTION
In one aspect of the invention, there is provided a method for
early detection of subacute, potentially catastrophic illness in
patients. The method comprises: (a) monitoring heart rate
variability in the patient; 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, as well as non-infectious illnesses.
Preferably, one or more diagnostic work-ups are conducted for a
suspected illness when a characteristic abnormality or
abnormalities are identified. Such diagnostic work-ups include, but
are not limited to, obtaining blood cultures, taking X-rays, or
obtaining pathological specimens from the patient.
In one preferred embodiment, the characteristic abnormality or
abnormalities are identified from a normalized data set of RR
intervals. A substantially large data set is preferred. Such a data
set more preferably contains on the order of about 10.sup.3 to
10.sup.4 RR intervals.
The characteristic abnormality or abnormalities are preferably
identified based on at least one of the second and higher moments,
percentile values (more preferably the 10.sup.th and 50.sup.th
percentile value), sample entropy, and/or sample asymmetry analysis
of the normalized data set.
In another aspect of the present invention, there is provided a
method for early detection of subacute, potentially catastrophic
illness in a patient. The method 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 second and higher moment including
standard deviation; (ii) percentile values of the data set; (iii)
sample entropy; and (iv) sample asymmetry; and (d) identifying an
abnormal heart rate variability associated with the illness based
on one or more of the moments, percentile values, sample entropy,
and sample asymmetry analysis, and correlating the variability to
illnesses. The calculation of step (c) is carried out using a
multivariable statistical analysis including but not limited to
multivariable regression analysis, neural network, k-nearest
neighbor analysis, and combinations thereof.
In yet another aspect of the present invention, there is provided
an apparatus for early detection of subacute, potentially
catastrophic illness in a patient. The apparatus comprises: (a) a
monitoring device, monitoring heart rate variability in a patient;
and (b) a microprocessor, identifying at least one characteristic
abnormality in heart rate variability that is associated with
illness. The microprocessor preferably generates a normalized data
set of RR intervals and also preferably calculates one or more of
the second and higher moments of the data set (more preferably
standard deviation, skewness and/or kurtosis), percentile values of
the data set (more preferably 10.sup.th and 50.sup.th percentile),
sample entropy, and/or sample asymmetry analysis of the normalized
data set, and identifies the characteristic abnormality based on
the same.
Preferably the microprocessor performs steps comprising: (a)
generating a normalized data set of the RR intervals; (b)
calculating one or more of: (i) moments of the data set selected
from the second and higher moments, (ii) percentile values of the
data set, (iii) sample entropy; (iv) sample symmetry; (c)
identifying an abnormal heart rate variability based on one or more
of the moments, the percentile values, sample entropy and sample
asymmetry analysis; and (d) correlating the abnormal heart rate
variability to said illness.
DETAILED DESCRIPTION OF THE INVENTION
The reasons for reduced HRV during illness has been debated, and
three theories concerning the mechanisms of reduced HRV have been
developed. These theories focus on the mathematical characteristics
of RR interval time series showing normal and low HRV.
The first theory focuses on the notion that the mechanism behind
reduced HRV is a reduction of parasympathetic tone. Akselrod, S.,
D. Gordon, F. A. Ubel, D.C. Shannon, A. C. Barger, and R. J. Cohen,
"Power Spectrum Analysis of Heart Rate Fluctuation: a Quantitative
Probe of Beat-to-beat Cardiovascular Control," Science 213:220-222
(1981); But see Malik, M. and A. J. Camm, "Heart Rate Variability:
from Facts to Fancies," J Am Coll Cardiol 22:566-568 (1993).
The second theory centers on the notion that normal physiology is
more complex than abnormal, hence heart rhythm is more irregular
during health. Goldberger, A. L., D. R. Rigney, and B. J. West,
"Chaos and Fractals in Human Physiology," Scientific American
262:42-46 (1990); Goldberger, A. L., V. Bhargava, B. J. West, and
A. J. Mandell, "On a Mechanism of Cardiac Electrical Stability: the
Fractal Hypothesis," Biophys J 48:525-528 (1985); Goldberger, A. L.
and B. J. West "Chaos in Physiology: Health or Disease? Chaos in
Biological Systems," H. Degn, A. V. Holden, and L. F. Olsen,
editors. Plenum Press, 1-4, New York (1987); Goldberger, A. L. and
B. J. West, "Applications of Nonlinear Dynamics to Clinical
Cardiology," Ann NY Acad Sci 504:195-213 (1987); Goldberger, A.
"Fractal Electrodynamics of the Heartbeat In Mathematical
Approaches to Cardiac Arrhythmias," J. Jalife, editor, The New York
Academy of Sciences, New York. 402-409 (1990); Peng, C.-K., J.
Mietus, J. M. Hausdorff, S. Havlin, H. E. Stanley, and A. L.
Goldberger, "Long-Range Anticorrelations and Non-Gaussian Behavior
of the Heartbeat," Phys Rev Lett 70:1343-1346 (1993).
Without wishing to be held to any particular explanation or theory,
the present inventors have developed a third theory of the
mechanism of observed abnormalities of HRV: an explanation based on
the events of signal transduction cascades. See Nelson J. C.,
Rizwan-Uddin, Griffin M. P., Moorman J. R., "Probing the Order of
Neonatal Heart Rate Variability," Pediatric Research, 43: 823-831
(1998). The sinus node cell membrane has beta-adrenergic receptors
which, on binding agonists released from sympathetic nerve endings
or the adrenal medulla, lead to the activation of cAMP-dependent
protein kinase, which phosphorylates cardiac ion channels and
results in cell depolarization, an action potential, and a
heartbeat. This readily explains the rise in heart rate after
sympathetic stimulation. The sinus node cell membrane also contains
muscarinic acetylcholine receptors--when bound with acetylcholine
from parasympathetic nerve endings, the process is inhibited and
the heart rate falls. As the amounts of sympathetic and
parasympathetic activity vary, so heart rate varies. Thus, for as
long as the complex steps of intracellular signal transduction can
be successfully completed, the sinus node can be viewed as an
amplifier of the input signals of the autonomic nervous system, and
heart rate as the output signal.
Consider now a severe illness such as sepsis. In such an
unfavorable metabolic milieu, optimal conditions for signal
transduction are unlikely. The inventors hypothesized that HRV
becomes abnormal during such illness because sinus node cells, like
all other cells, are unable to respond normally to sympathetic and
parasympathetic inputs. From this viewpoint, sinus node cells
report in real time on their ability to respond to adrenergic and
muscarinic stimuli. Effective reporting depends on optimal
intracellular conditions, and the inventors view HRV as a sensitive
measure of the state of cells.
The inventors have found that monitoring HRV in patient populations
at high risk leads to an early diagnosis and opportunity for early
treatment for potentially catastrophic illnesses, such as severe
infections. For example, records of RR intervals in patients prior
to the clinical diagnosis of sepsis demonstrate at least two
characteristic abnormalities. First, the baseline shows very
reduced variability. Second, there are short-lived episodes of
deceleration of heart rate. The present invention relates to novel
mathematical approaches to detecting these characteristic
abnormalities.
The present invention relates to successful patient HRV monitoring,
and the ability to distinguish abnormal HRV from normal HRV using
objective criteria. Patient HRV correlates with the severity of
patient illness such that a decrease in HRV occurs before clinical
manifestations of potentially catastrophic illnesses such as sepsis
and necrotizing enterocolitis appear.
The invention relates to a real-time heart rate variability monitor
whose signal can be interpreted as the probability of an impending
catastrophic clinical event. The present invention can be applied
in patient populations that are at high risk of potentially
catastrophic impending events such as, but not limited to, sepsis,
necrotizing enterocolitis, pneumonia and meningitis, as well as
non-infectious illnesses. Generally, the method is applicable for
diagnosis of illnesses that lead to the systemic inflammatory
response syndrome, including sepsis-like illness, a clinical
condition in which patients have signs and symptoms of sepsis, but
do not have documented infection. The method is also applicable for
other sub-acute illnesses that present late in the course with
abrupt deterioration, such as intracranial hemorrhage. Patient
populations include patients at any life stage, including but not
limited to low birth weight infants, premature neonates, newborn
infants, infants, toddlers, children, adolescents, and adults.
The invention relates to a process by which monitoring of novel
parameters of heart rate variability can be used to make the early
diagnosis of subacute illness in patients.
The analysis of the present invention preferably includes all or
some of the following steps to construct a digitally filtered and
normalized data set from data sets of sufficient numbers of
consecutive RR intervals:
1. Acquire EKG signal and RR interval time series data, preferably
continuously.
2. Separate into piecewise continuous beat records (e.g., the 4096
beat records used in the Examples).
3. Filter, for example, using a (2M+1) point moving average filter.
##EQU1##
4. Calculate the mean, variance and standard deviation of each
record.
5. Normalize the data by subtracting the mean and dividing by the
standard deviation.
6. Calculate, for example, the third and fourth moments of the
normalized data, where: ##EQU2##
where m.sub.r, is the rth moment of the time series variable X. The
moment coefficient of skewness is m.sub.3 /(m.sub.2).sup.3/2, and
the moment coefficient of kurtosis is m.sub.4 /(m.sub.2).sup.2.
When the data are normalized, m.sub.2 (the variance) is 1, and the
third and fourth moments are identical to the skewness and the
kurtosis, respectively.
7. Determine percentiles of the normalized filtered data by sorting
the intervals from smallest to largest. The 50th percentile value,
or P50, is the value halfway from smallest to largest. It is the
median value of the data set. In the same way, other percentile
values of interest can be determined. For example, P10 is the value
that lies 10% of the way between the smallest and the largest. For
our data sets of 4096 points, it is the 410th point starting from
the smallest.
The present invention also relates to novel parameters of heart
rate variability that can be correlated to the presence of
potentially catastrophic illness in a patient, which include, but
are not limited to, the following:
1. Higher moments of the data, including
a. The second moment of the digitally filtered and normalized data
set (the moment coefficient of standard deviation, also referred to
simply as "standard deviation"): a low value signifies abnormally
reduced variability, which allows for a diagnosis early in the
course of subacute illnesses, such as sepsis.
b. The third moment of the digitally filtered and normalized data
set (the moment coefficient of skewness, also referred to simply as
"skewness"): a high positive value indicates the presence of
short-lived subclinical decelerations in heart rate, which allows
for a diagnosis early in the course of subacute illnesses, such as
sepsis.
c. The fourth moment of the filtered and normalized data set (the
moment coefficient of kurtosis, also referred to simply as
"kurtosis"): a high positive value indicates a peaked frequency
histogram of the RR intervals, which allows for a diagnosis early
in the course of subacute illnesses, such as sepsis.
2. Percentiles of the data, such as the 10th percentile value of
the filtered and normalized data set (P10): a value closer to 0
allows for a diagnosis early in the course of subacute illnesses,
such as sepsis. Other reasonably low percentiles (e.g., P1 to P20)
are likely to be equally appropriate. Moreover, characteristic
abnormalities of other percentile values (for example, the 25th,
50th (median), 75th and 90th percentile values of data set) are
contemplated.
3. Sample entropy (SampEn), which is a measure of the relative
patterness of data series. SampEn measures complexity and
regularity of clinical and experimental time series data. It is the
negative logarithm of the conditional probability that two
sequences of m+1 points will match within a tolerance r given that
they match for the first m points. It is similar to approximate
entropy but is significantly less biased, especially for short data
series because it does not count sequences as matching themselves.
See Richman J. S., Moorman J. R., "Physiological Time-series
Analysis Using Approximate Entropy and Sample Entropy," American
Journal of Physiology, 278: H2039-2049 (2000), which is herein
incorporated by reference in its entirety. SampEn is reduced for RR
interval records showing reduced variability and transient
decelerations.
4. Sample asymmetry analysis is a method of determining asymmetry
of frequency histograms. Generally, a power function is used to
weigh the deviation of each RR interval in the series from a
certain RR interval value. The average weighted deviation for
intervals lower than this reference value is calculated, and is
lower for RR interval records showing reduced variability and
transient decelerations. The average weighted deviation for
intervals higher than the median is also calculated, and is higher
for RR interval records showing reduced variability and transient
decelerations. The ratio right/left weighted deviation is computed
as an indicator of asymmetry of each RR sample. For this particular
application the power function is assumed quadratic, e.g. the power
is 2, and the reference point is the median of each RR sample.
Graphically, the third and fourth moments report on the nature of
the frequency histogram of the RR intervals. Specifically, the
third moment reports on the symmetry of the histogram, and becomes
large as the histogram is skewed to the right by the long RR
intervals associated with the episode of relative bradycardia.
Since the variance of the normalized record is 1, the third moment
is referred to as the skewness. The fourth moment reports on the
nature of the peak, and becomes larger as the peak becomes sharper.
Since records with predominantly low HRV have RR interval values
that are tightly clustered, the histogram has a sharp main peak,
and the fourth moment is relatively large. Since the variance of
the normalized record is 1, the fourth moment is referred to as the
kurtosis. Thus one aspect of the present invention relates to the
examination of records for elevated values of skewness and
kurtosis.
The present invention also utilizes SampEn, which is a new family
of statistics that is related to approximate entropy (ApEn). SampEn
was developed to reduce the shortcomings and biases present in ApEn
due to the necessity of requiring the counting of self-matches,
which suggest more similarity than is actually present. See Richman
J. S., Moorman J. R., "Physiological Time-series Analysis Using
Approximate Entropy and Sample Entropy," American Journal of
Physiology, 278: H2039-2049 (2000). SampEn (m, r, N) is precisely
the negative natural logarithm of the conditional probability that
two sequences similar for m points remain similar at the next
point, where self-matches are not included in calculating the
probability. Thus, a lower value of SampEn also indicates more
self-similarity in the time series.
In addition to eliminating self-matches and the inherent bias, the
SampEn algorithm is simpler than the ApEn algorithm, requiring
approximately one-half as much time to calculate. Further, SampEn
is largely independent of record length and displays relative
consistency under circumstances where ApEn does not. Further,
SampEn does not use a template-wise approach when estimating
conditional probabilities. To be defined, SampEn requires only that
one template find a match of length m+1. SampEn does not consider
self-matches. Second, only the first N-m vectors of length m are
considered, which ensures that, for 1.ltoreq.i.ltoreq.N-m,
.chi..sub.m(i) and x.sub.m +1.sup.(i) .omega..sub.m+1(i) are
defined.
We defined B.sub.i.sup.m (r) as (N-m-1).sup.-1 times the number of
vectors x.sub.m (j) within r of x.sub.m (i), where j ranges from 1
to N-m, and j.notident.i to exclude self-matches. We then defined
B.sup.m (r)=(N-m).sup.-1 .SIGMA..sub.i=1.sup.N-m B.sub.i.sup.m (r).
Similarly we defined A.sub.i.sup.m (r) as (N-m-1).sup.-1 times the
number of vectors x.sub.m+1 (j) within r of x.sub.m+1 (i), where j
ranges from 1 to N-m(j.notident.i), and set A.sup.m
(r)=(N-m).sup.-1 .SIGMA..sub.i=1.sup.N-m A.sub.i.sup.m (r). B.sup.m
(r) is then the probability that two sequences will match for m
points, whereas A.sup.m (r) is the probability that two sequences
will match for m+1 points. We then defined the parameter SampEn(m,
r)=lim.sub.N -.infin.{-11n [A.sup.m (r)/B.sup.m (r)]}, which is
estimated by the statistic SampEn(m, r, N)=-1n [A.sup.m (r)/B.sup.m
(r)]. Where there is no confusion about the parameter r and length
m of the template vector, we set B={[(N-m-1)(N-m)]/2}B.sup.m (r)
and A={[(N-m-1)(N-m)]/2}A.sup.m (r), so that B is the total number
of template matches of length m and A is the total number of
forward matches of length m+1. We note that A/B=[A.sup.m
(r)]/B.sup.m (r)], so SampEn(m, r, N) can be expressed as -1n
(A/B).
The quantity A/B is precisely the conditional probability that two
sequences within a tolerance r for m points remain within r of each
other at the next point. In contrast to ApEn(m, r, N), which
calculates probabilities in a template-wise fashion, SampEn(m, r,
N) calculates the negative logarithm of a probability associated
with the time series as a whole. SampEn(m, r, N) will be defined
except when B=0, in which case no regularity has been detected, or
when A=0, which corresponds to a conditional probability of 0, and
in infinite value of SampEn(m, r, N). The lowest nonzero
conditional probability that this algorithm can report is
2[(N-m-1)(N-m)].sup.-1. Thus, the statistic SampEn(m, r, N) has in
(N-m)+1n(N-m-1)-1n(2) as an upper bound, nearly doubling in (N-m),
the dynamic range of ApEn(m, r, N).
The present invention also utilizes Sample Asymmetry analysis of RR
intervals. This analysis includes weighting of all RR intervals
with respect to their deviation from a reference point, followed by
computation of left weighted deviation (R.sub.1), right weighted
deviation (R.sub.2) and their ratio R=R.sub.2 /R.sub.1, which is
the defined here sample asymmetry. The definition of sample
asymmetry follows the following formal mathematical
construction:
a) Defining weighting power function: Let .xi. be a random variable
with values in its sampling space X and unspecified distribution,
and let .mu. .epsilon. X be a point within the sampling space X.
For any x .epsilon. X we define a weighting function w(x;
.alpha.)=(x-.mu.).sup..alpha., where .alpha.>0 is a parameter
describing the degree of weighting of deviations from the reference
point .mu.. For example, if .alpha.=1, deviations from .mu. will
receive linearly increasing weights, while if .alpha.=2, deviations
from .mu. will receive quadratically increasing weights. Note that
the weighting parameter .alpha. could be selected in various
applications to be any positive, including non-integer, number. A
number smaller than 1 will result in slower than linear increase of
weights, a number greater than 2 will result in a faster than
quadratic increase of weights.
In order to add flexibility to this model, a different degree of
weighting to the left (.alpha.) and to the right (.beta.) from the
reference point .mu. can be employed. In many applications the
left- and right weightings could be equal. Separate weightings for
left and right deviations of from its reference point .xi. are
defined as follows:
Left weighting function: w.sub.1 (x;.alpha.)=w(x;.alpha.) whenever
x<.mu. and 0 otherwise;
Right weighting function: w.sub.1 (x;.beta.)=w(x;.beta.) whenever
x.gtoreq..mu. and 0 otherwise, where the parameters, similarly to
a, describes the degree of weighting of deviation to the right of
the reference point.
b) Defining Sample Asymmetry of a random variable: Let x.sub.1,
x.sub.2, . . . x.sub.n be a sample of n observations on .xi.. Given
this sample, two quantities representing the sum of the weighted
deviations to the left and to the right from the reference point
.mu. are defined as follows: ##EQU3##
Thus, if .alpha.=.beta., and the sample x.sub.1, x.sub.2, . . . x
is approximately symmetric with respect to the reference point,
then R.sub.1 will be approximately equal to R.sub.2. If the sample
is asymmetric with larger, and/or more frequent deviations to the
right from the reference point .mu., then R.sub.2 will be greater
than R.sub.1. Inversely, if the sample is asymmetric with larger,
and/or more frequent deviations to the left from the reference
point .mu., then R.sub.1 will be greater than R.sub.2.
The ratio R(.alpha.,.beta.)=R.sub.1 (.alpha.)/R.sub.2 (.beta.)
represents the sample asymmetry of the random variable .xi..
The following properties are pertinent to this application of
sample asymmetry:
If .alpha.=.beta., when the sample x.sub.1, x.sub.2, . . . x.sub.n
is approximately symmetric with respect to the reference point,
then R(.alpha., .beta.) will be approximately equal to 1. Values
greater than one will indicate larger, and/or more frequent
deviations to the right from the reference point .mu. while values
less than one will indicate larger, and/or more frequent deviations
to the left.
The sensitivity of the ratio R(.alpha., .beta.) to left- and right
deviations from the reference point can be controlled through
separate adjustment of the parameters .alpha. and .beta..
R.sub.1 (.alpha.) and R.sub.2 (.beta.) can be used separately as
estimates of the absolute weighted mass of the distribution of 4
with respect to its reference point .mu..
The reference point u can be the empirical mean of the random
variable .xi., e.g., ##EQU4##
the median of .xi., or any other theoretically, or practically
relevant number.
If .mu. is the mean (or the median) of the distribution and
.alpha.=.beta.=2 then, under the null hypothesis that the
distribution is normal, the sample asymmetry R(2,2) will have an
F-distribution with (n/2-1;n/2-1) degrees of freedom
(n/2-1/2;n/2-3/2 if n is odd number). This property gives a
straightforward test for symmetry of a single data sample.
Abnormalities in HRV that are characteristic of illness can be
identified, for example, by comparing the above parameters of heart
rate variability to threshold or by combining multiple measurements
of HRV using logistic regression models, neural networks, multiple
variable analysis, nearest neighbor analysis, or other predictive
mathematical instruments. Appropriate parameters for thresholds or
for mathematical modeling can be assigned by those skilled in the
art. Ideally, these parameters will be based on the results of a
large group of patients, for example, a group of newborn patients
at risk of sepsis and necrotizing enterocolitis. For example, from
the infants observed to date, reasonable threshold values include:
skewness on the order of about 1 or more, kurtosis on the order of
about 7 or more and P10 on the order of about -1.1 or more.
EXAMPLES
Example 1
Study Population
Infants who had risk factors for acquiring late-onset sepsis were
monitored in the neonatal intensive care unit at the University of
Virginia from August 1995 to April 1999. These risk factors
included low birth weight, prematurity, need for central venous
access, and NICU stay longer than 2 weeks. Three groups of infants
defined by the actions of the physicians and the results of blood
cultures were studied. Infants who had an abrupt clinical
deterioration after 3 days of age that prompted physicians to
obtain blood cultures and to give antibiotics were in the sepsis
(positive blood culture) or sepsis-like illness (negative blood
culture) group. The infants without sepsis group raised no clinical
suspicion of sepsis and had no blood cultures obtained over a
10-day period. HRC monitoring results were not visible to the
treating physicians and did not influence medical management.
Heart Rate Data Acquisition and Analysis
An analog EKG voltage signal from the bedside monitor (Marquette)
was digitized and filtered, and then evaluated for QRS complexes
using specially-equipped PCs (National Instruments AT-DSP2200). RR
intervals were compared with the previous 100 intervals and
excluded if they differed by more than 5 S.D. All data sets were
visually inspected, and records with obviously artefactual data
were excluded. This resulted in removal of 1% of all the data sets.
HRC measures were calculated from 4096-beat epochs of RR
intervals.
HRC Analysis
HRC measures that give a description of the symmetry of the
histogram of RR intervals were selected. "Moments" are descriptive
statistics calculated from the individual differences of data
points from the mean. The first moment is itself the mean and the
second moment is the standard deviation, the square root of the
average of the squared individual differences. The third moment or
skewness reports on the symmetry of the histogram. A symmetrical
histogram has a skewness of 0, and a histogram with a tail of
values that are larger than the median has positive skewness.
Weisstein, E. W., "CRC Concise Encyclopedia of Mathematics,"
Chapman and Hall/CRC, Boca Raton (1999). "Percentiles" of the data
were also calculated. The median is the 50.sup.th percentile data
point, meaning that it resides at the mid-point of the data after
sorting from smallest to largest. In addition to the median, the
p50, the p10 (10th percentile data point), p25 (25th percentile
data point), p75 (75th percentile data point) and p90 (90th
percentile data point) were calculated. Prior to these
calculations, the mean and S.D. were used to normalize the data so
that the mean and S.D. of each 4096-beat record were 0 and 1
respectively. This normalization allowed direct comparison of HRC
among all records. It is important to note that these measures are
based only on the distribution of the data points and not the
sequence in which they occur. These measures are not changed by
missing points, unlike frequency domain measures. Berntson, G. G.
and J. R. Stowell, "ECG artifacts and heart period variability:
don't miss a beat," Psychophysiol, 35:127-132 (1998).
Clinical Scores
The SNAP (Richardson, D. K., J. E. Gray, M. C. McCormick, K.
Workman, and D. A. Goldmann, "Score for Neonatal Acute Physiology:
a physiologic severity index for neonatal intensive care,"
Pediatrics, 91:617-623 (1993)) and NTISS (Gray, J. E., D. K.
Richardson, M. C. McCormick, K. Workman-Daniels, and D. A.
Goldmann, "Neonatal therapeutic intervention scoring system: a
therapy-based severity of illness index," Pediatrics, 90:561-567
(1992)) scores were obtained either prospectively or from chart
review by trained research assistants who were closely supervised.
Scores were calculated for 24-hour epochs relative to the time that
the suspicion of sepsis was raised. The investigators were blinded
to the results of the HRC analysis at the time of scoring.
Strategy
To examine the time course of HRC early in sepsis, the time period
of five days before and three days after a reference time point
were analyzed. For this, time that was used was either the time at
which the blood culture was obtained (sepsis and sepsis-like
illness groups) or a random time (infants without sepsis). The
event time for infants without sepsis was assigned randomly during
the 6th or .sub.7 th day of their 10-day course. The data in 6-hour
epochs based on this reference point were analyzed. HRC for all the
4096-beat data sets was calculated, and each 6-hour epoch as the
median value of each measure for each patient was summarized.
Epochs with less than 50% of the expected number of heartbeats was
excluded. For comparison with clinical illness severity scores in
the regression analysis, HRC in 24-hour epochs were analyzed and
compared with the results with clinical scores obtained over the
same period.
Statistical Analysis
The significance of differences in demographic characteristics and
HRC for isolated time points was examined using the Mann-Whitney
rank sum test or ANOVA (SigmaStat, Jandel). The significance of
differences between groups for the 24 hours before and after the
event were analyzed using ANOVA with a Tukey test for multiple
comparisons (SigmaStat, Jandel). ROC analysis was performed using
Microsoft Excel and the Analyze-it plug-in (Analyze-it Software).
Multivariable logistic regression analysis was used to examine the
ability of HRC and clinical scores to distinguish septic infants
from infants without sepsis (S-Plus).
Table I shows the demographic characteristics of the infants
studied. There were 46 culture-proven episodes of sepsis in 40
patients. There were 2 deaths associated with Staphylococcus aureus
and Enterococcus infection. The most common organisms isolated were
coagulase-negative Staphylococcus (n=20) and Staphylococcus aureus
(n=15). There were 27 episodes of culture-negative sepsis in 23
infants. In the control group, there were 29 control periods in 26
patients. In the culture-positive sepsis group, the mean (S.D.)
birth weight, gestational age and post-conceptional age at event
were 784 g (409), 26 weeks (2), and 31 weeks (4). In the
culture-negative sepsis-like illness group, the values were 756 g
(258), 26 weeks (2) and 30 weeks (3). In the infants without
sepsis, the values were 976 g (265), 28 weeks (3), and 33 weeks
(3). For each parameter, the values for the control group were
significantly higher than for the sepsis and sepsis-like illness
groups (p<0.001), but the sepsis group was not significantly
different from the sepsis-like illness group.
Heart Rate Analysis
FIG. 1A shows a time series of 4096 RR intervals from an infant 6
days prior to an episode of culture-positive sepsis and represents
a normal HR pattern. FIGS. 1B and 1C show abnormal RR interval time
series from the same infant that were each obtained within 3 to 6
hours before sepsis was suspected and blood cultures were obtained.
Both have a baseline of reduced variability and are punctuated by
sharp upward deflections that represent short-lived episodes of HR
decelerations. While episodes of bradycardia in NICU patients are
common and not necessarily significant (Hodgman, J. E., T.
Hoppenbrouwers, and L. A. Cabal, "Episodes of bradycardia during
early infancy in the term-born and preterm infant," AJDC,
147:960-964 (1993)), frequent episodes of apnea, which are often
associated with heart rate decelerations, are often interpreted as
reflecting early stages of sepsis. Fanaroff, A. A., S. B. Korones,
L. L. Wright, J. Verter, R. L. Poland, C. R. Bauer, J. E. Tyson, J.
B. Philips, W. Edwards, J. F. Lucey, C. S. Catz, S. Shankaran, and
W. Oh, "Incidence, presenting features, risk factors and
significance of late onset septicemia in very low birth weight
infants. The National Institute of Child Health and Human
Development Neonatal Research Network," Pediatric Infectious
Disease Journal, 17:593-598 (1998). The HR in these records always
exceeded 120 beats per minute, and these episodes would have failed
to trigger HR alarms set at usual thresholds of 100 beats per
minute. In fact, the mean RR intervals of these records are not
very different at 323 (1A), 308 (1B) and 302 msec (1C).
Distinguishing the data in FIG. 1B from normal is nonetheless
straightforward by calculating the standard deviation, here 11 msec
compared with 32 msec for the normal. This measure, however, would
fail to diagnose the abnormal time series in FIG. 1C where the
episodes of sub-clinical HR decelerations are sufficient to elevate
the standard deviation to an apparently normal value of 26
msec.
To diagnose these abnormalities, an approach was developed based on
the frequency histograms of the RR intervals shown in the
right-hand column in FIG. 1. The long RR intervals during the
decelerations generated asymmetry of the histogram (1E and 1F). The
symmetry of histograms was quantified using the third moment or
skewness, a descriptive statistic that, like the standard
deviation, is based on the differences between individual data
points and the mean. The skewness is positive when there is a
longer tail of values extending toward longer RR intervals.
Weisstein, E. W., "CRC Concise Encyclopedia of Mathematics,"
Chapman and Hall/CRC, Boca Raton (1999). The skewness values of the
three time-series are different: -0.12 for the data in 1A,
indicating a near-symmetric distribution, but 1.99 and 1.33 for the
data in FIGS. 1B and 1C, indicating a large degree of
asymmetry.
The abnormalities of the histograms can also be quantified by
considering the relationship of values in the distribution to the
mean. Accordingly, the values of five percentile values of the
normalized data--the 10th, 25th, 50th (median), 75th, and 90th was
determined. These parameters differed among the three data sets
shown. For example, the .sub.50 th percentile value (p50) was more
negative in the abnormal data sets. The values were 0.13 in the
normal record in FIG. 1A but -0.21 and -0.33 in the abnormal
records shown in FIGS. 1B and 1C. This change results from the
preponderance of values to the left of the center of the
distribution, a consequence of the asymmetry of the histogram.
Summary HRC Data
FIG. 2 shows the time course of HRC measures for the three patient
groups. Each data point is the mean of the median values for 6
hours, and bars are S.E.M. The vertical line marks the time of the
abrupt clinical deterioration for which blood cultures were
obtained and antibiotics were started. The label "CRASH" stands for
Cultures, Resuscitation and Antibiotics Started Here. The data
points at time 0 represent the 6-hour epoch prior to, but not
including, the time of the deterioration. Values of mean RR
interval (2A) and standard deviation (2B) did not discriminate
among the groups. The skewness (2C) and p50 values (2D), on the
other hand, changed markedly in the epochs from 24 hours before to
24 hours after diagnosis. The skewness was 0.59.+-.0.10 for the
sepsis group and 0.51.+-.0.12 for the sepsis-like illness group
compared with -0.10.+-.0.13 for control over the 6 hours prior to
clinical suspicion. The values for sepsis (culture-positive) and
sepsis-like illness (culture-negative) infants were not
significantly different, but both were different than the control
values (p<0.001). The p50 was -0.0298.+-.0.014 for sepsis and
-0.0223.+-.0.015 for sepsis-like illness compared with
+0.0503.+-.0.016 for control over the 6 hours prior to clinical
suspicion. Again, the values for sepsis and sepsis-like illness
groups were not significantly different, but both were different
than the control values (p<0.001).
Clinical Scores
FIG. 3 shows the Score for Neonatal Acute Physiology (SNAP) and
Neonatal Therapeutic Intervention Severity Score (NTISS) scores.
The sepsis and sepsis-like illness groups had higher levels of both
scores throughout, suggesting a greater degree of illness and
intensity of interventions. The NTISS level declined over the 10
day period in the control group, consistent with declining
requirement for therapies and gradual weaning from support systems.
On day 0, the 24 hours prior to (but not including) the clinical
suspicion of sepsis, SNAP rose further in the sepsis and
sepsis-like illness groups (p=0.01, ANOVA) and there was no decline
in NTISS. Over the 24 hours before and after the CRASH, the values
of SNAP and NTISS for sepsis (culture-positive) and sepsis-like
illness (culture-negative) infants were not significantly
different, but both were different than the control values
(p<0.001).
Multivariable Logistic Regression Analysis
Multivariable logistic regression analysis was performed on the
data from the 24 hours prior to the clinical suspicion of sepsis.
HRC were represented by the median values of each of the five
percentiles, and clinical data were represented by both the SNAP
and NTISS scores for this period. The data from the sepsis and
sepsis-like illness groups were pooled. Whether a regression model
could distinguish the pooled data from the control group was
tested. The finding was that the groups were highly significantly
different (p<0.0001, ROC area 0.9). While no single HR
percentile measure made a significantly greater contribution than
the others to the discriminatory ability of the model, SNAP
contributed significantly more than NTISS (p<0.003, Wald
z-test). Both HRC and clinical scores contributed independently to
the final model (p=0.02). Results were similar when only sepsis or
sepsis-like illness infants were analyzed, and for regression
models using HR moments rather than percentiles.
Dynamic Changes in HRC and Illness Severity Scores Early in the
Course of Sepsis
ROC analysis was used to quantify differences among the groups, and
to examine the time course of the differences. The area under the
ROC plot is 0.5 when the groups are not different, and it is 1.0
when the groups are entirely distinct. Panels A and B of FIG. 4
show the results for HR moments and percentiles. For this analysis,
the results after pooling data from the sepsis and sepsis-like
groups are shown, but the results for either group individually
were very similar. While mean and S.D. did not discriminate
controls from sepsis and sepsis-like illness infants, skewness
showed dynamic differences beginning 12 to 24 hours prior to the
diagnosis. For example, there is a significant difference between
the values of p50 measured in the sepsis groups 3 days prior to
sepsis compared with values 6 to 12 hours prior (p<0.05). The
difference then resolved over the two days after diagnosis and
antibiotic therapy. Two of the HR percentiles, p10 and p50, showed
a similar course. SNAP and NTISS also discriminated between the
control group and the sepsis and sepsis-like illness groups, and
SNAP showed an increasing difference for one to two days prior to
the diagnosis. The increasing ROC area for NTISS is due to the
failure of NTISS to decline in the sepsis and sepsis-like illness
infants shown in FIG. 3B.
Multivariable logistic regression models were also used to
distinguish the groups. As shown in FIG. 4D, models that used HRC
alone and clinical scores (SNAP and NTISS together) alone
effectively discriminated the groups, with larger changes at least
one day prior to the diagnosis. A model using both HRC and clinical
scores was superior, in keeping with the finding above that both
HRC and clinical data contributed independently to the model. A
comparison of regression model values for day -3 with subsequent
days showed that the increases on day 0 were statistically
significant for all models (p<0.05).
TABLE 1 Population Characteristics No sepsis Sepsis Sepsis-like
illness (n = 26/29) (n = 40/46) (n = 23/27) Birth weight, g <750
3 20 12 750-999 12 14 8 1000-1499 10 5 2 .gtoreq.1500 1 1 1
Gestational age, wk <26 6 19 9 26-28 10 16 9 29-32 9 4 5 >32
1 1 0 Post-conceptional age at event, wk <26 0 1 4 26-28 0 13 7
29-32 13 20 8 >32 16 12 8 Male sex 15 21 13 Caucasian 21 30 21
KEY: Data are the number of patients for birth weight and
gestational age, and numbers of episodes for post-conceptional age
at event. In the column headings the totals are given as (n =
number of patients, number of episodes).
Example 2
149 consecutive patients in the University of Virginia (UVA) NICU
were studied. There were 110 episodes of sepsis (positive blood
culture) and sepsis-like illness (no proven infection) in 69
patients. A repeated measures multivariable logistic regression
model using parameters of sample entropy, sample asymmetry analysis
and standard deviation showed highly significant association with
impending sepsis and sepsis-like illness (ROC area 0.71,
p<0.001). The results were the same for different values of m
and r in the SampEn calculation, and for several parameters from
the sample asymmetry analysis including left-risk, right risk, and
the two measures in combination, such as their ratio. Similar
results were obtained using k-nearest neighbor analysis, another
multivariable statistical technique, to combine these HRC measures.
HRC added significantly to the predictive information of birth
weight (BW), gestational age (GA), and days of age (p<0.001,
Wald test). A full model using BW, GA, days of age and HRC had ROC
area 0.75 (95% confidence interval 0.68 to 0.76). The potential
clinical usefulness of these findings was evaluated using a
threshold-independent approach: A change in the HRC index from the
25.sup.th percentile to the 75th percentile increased the odds of
sepsis or sepsis-like illness by 4.5-fold.
Example 3
197 patients in the Wake Forest University NICU were studied using
the method set forth in Example 4. There were 71 episodes of sepsis
and sepsis-like illness in 60 of the 197 infants. The predictive
model that was developed at UVA showed highly significant
association with impending sepsis and sepsis-like illness (ROC area
0.71, p<0.001). Again, HRC added significantly to the predictive
information of BW, GA, and days of age (p<0.001, Wald test). A
change in the HRC index from the 25.sup.th percentile to the
75.sup.th percentile increased the odds of sepsis or sepsis-like
illness by 4.2-fold, which illustrate that HRC monitoring is a
valid and useful non-invasive tool in the early diagnosis of sepsis
and sepsis-like illness.
* * * * *