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Electronic Detection and Diagnosis of Health and Illness of Premature Infants

Electronic Detection and Diagnosis of Health and Illness of Premature Infants. Co-Workers. Medical Folks Randall Moorman Pamela Griffin John Kattwinkel Alix Paget-Brown Brooke Vergales Kelley Zagols Andy Bowman Terri Smoot. Quants Statisticians Doug Lake George Stukenborg

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Electronic Detection and Diagnosis of Health and Illness of Premature Infants

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  1. Electronic Detection and Diagnosis of Health and Illness of Premature Infants

  2. Co-Workers Medical Folks Randall Moorman Pamela Griffin John Kattwinkel Alix Paget-Brown Brooke Vergales Kelley Zagols Andy Bowman Terri Smoot Quants Statisticians Doug Lake George Stukenborg Chemical Engineers John Hudson Matthew Clark Craig Rusin Lauren Guin Physicists Abigail Flower Hoshik Lee John Delos Supported by NIH.

  3. Overview What can we Quants contribute? Statistical measures Signal Analysis Pattern recognition methods Dynamical theories Medical Issues: Apnea of Prematurity Neonatal Sepsis Observations: Electronic Monitoring of Heart, Respiration

  4. Outline Sepsis medical issue heart rate monitoring statistical measures randomized clinical trial pattern recognition physiological modeling Apnea medical issue chest impedance – cardiac artifact filtering, signal analysis examples future work Conclusion

  5. Sepsis the presence of bacteria, virus, fungus, or other organism in the blood or other tissues and the toxins associated with the invasion. Of 4 million births each year, 56,000 are VLBW (<1.5 Kg). For them the risk of sepsis is high (25-40%) Significant mortality and morbidity (doubled risk of death in VLBW infants; increased length of NICU stay; high cost). The diagnosis of neonatal sepsis is difficult, with a high rate of false negatives Physicians administer antibiotics early and often. Can heart rate monitoring give early warning of sepsis?(The invading organisms, or the immune response, may affect the pacemaking system.) Randall Moorman

  6. Does heart rate give warning of illness?Plot (Time Between Beats) vs (Beat Number) ms ms beat number beat number

  7. Does heart rate give warning of illness?Plot (Time Between Beats) vs (Beat Number) Reduced variability Repeated decelerations pathologic normal vs. pathologic pathologic expand

  8. Statistical Measures of RR Interval Data • Standard Deviation and Sample Entropy: Variability in the signal. • Sample Asymmetry: Prevalence of decelerations over accelerations implies a skew, or asymmetry, in the data which we can detect statistically.

  9. Find correlation of those measures with illness, and report correlation in terms of “fold increase of risk of sepsis”: Take any random moment. Examine a window of 24 hours (plus 18 hours or minus 6 hours) around that moment. On average, 1.8% of the infants in our first study had a sepsis event within that 24 hour window. If we report a “five-fold increase of risk of sepsis”, it means that about 10% of the infants showing those heart rate characteristics had a sepsis event within that 24 hour window.

  10. Medical Predictive Science Corporation has developed and is marketing a system for Heart Rate Characteristics monitoring in the NICU. A computer beside each NICU bed continuously collects ECG data, extracts times of R peaks, tracks RR intervals, and provides the following Heart Rate Observation (HeRO). This system is installed in several NICUs in the US, and a large randomized clinical trial has just been completed.

  11. HRC rises before illness score

  12. Conclusion 1: New quantitative analysis of noninvasive, electronically-measured heart rate characteristics – standard deviation, asymmetry, and sample entropy – provides an early noninvasive warning of sepsis events.

  13. Randomized Clinical Trial 8 Hospitals UVa, Wake Forest, UAl (birmingham), Vanderbilt, Umiami, Greenville SC, Palmer (Orlando) Penn State Control Save HeRO data but do not display it 152 deaths/1489, 10.2% 133 deaths/757, 17.6% Sample Display HeRO Score (but do not tell clinicians what to do) 2989 VLBW (<1.5 Kg) 1513 ELBW (<1 Kg)

  14. Randomized Clinical Trial 8 Hospitals UVa, Wake Forest, UAl (birmingham), Vanderbilt, Umiami, Greenville SC, Palmer (Orlando) Penn State Control Save HeRO data but do not display it 152 deaths/1489, 10.2% 133 deaths/757, 17.6% Sample Display HeRO Score (but do not tell clinicians what to do) 122 deaths/1500, 8.1% 100 deaths/756, 13.2% 2989 VLBW Δ = 2.1% absolute, 22% relative 1513 ELBW Δ = 4.4% absolute, 33% relative

  15. Conclusion 2: New quantitative analysis of noninvasive, electronically-measured heart rate characteristics – standard deviation, asymmetry, and sample entropy – saves lives.

  16. New Question: Would direct measures of decelerations provide additional information?

  17. Pattern Recognition for detecting decelerations Abigail Flower

  18. Result: “Storms” of Decelerations are Highly Predictive of Sepsis

  19. Statistical measures

  20. Conclusion 2 Counting and measuring decelerations gives a second method for early warning of sepsis. Also an important finding was that HR decelerations are surprisingly similar in infants.

  21. Discovery We found that, within extended clusters of decelerations, there were sometimes shorter intervals of time, lasting up to several hours, in which the decelerations showed remarkable periodicity.

  22. For six infants in our study population we identified deceleration clusters in which periodicity was maintained for at least ten minutes. In these periodic bursts of decelerations, the time to the next deceleration was about 15 sec.

  23. New Question:What Causes Periodic Decelerations?Can we develop a model capable of producing decelerations like those observed in neonatal HR records? Such models can guide future observations or experiments on animals.

  24. Partial Answers A. A mathematical model with minimal physiological assumptions: a noisy Hopf bifurcation • Physiologically-based models The pacemaker? The baroreflex loop? Periodic apneas?

  25. HR jumps from small fluctuations [“steady-state”] to periodic decelerations [above-threshold oscillations].

  26. Hopf Bifurcationsthe most common way that a rest point changes to a cycle Hard (Subcritical) Hopf bifurcation:

  27. Small excerpt of real data and simulation. large periodic decels data small periodic fluctuations simulation Hard Hopf oscillations noise-induced subthreshold oscillations

  28. Conclusion Output of a Noisy Hopf Bifurcation Model Resembles Observed Periodic Decelerations Thus we have a mathematical model with minimal physiological assumptions: a noisy Hopf bifurcation. Is there a physiologically-based model?

  29. Pacemaker cells • Sino-Atrial node cells are the natural pacemaker of the heart. Can they go into “FM” mode with period ~15 seconds? • Baroreflex loop • The feedback loop connecting blood pressure and heart rate can go into oscillation with period ~14 seconds in infants. Does this resemble the observations? • Periodic apneas • Apneas produce decelerations of the heart. Sometimes they occur periodically, and the period is commonly ~15 seconds.

  30. Mayer (low freq) arrhythmia RR HR BP BP • Oscillation with period ~14 s in infants. • Feedback loop with time delay. • Many models connect Mayer waves with a Hopf bifurcation. • However, measurements indicate that under physiological conditions in adults, the feedback loop is stable (no oscillations) • Two studies (DeBoer et al. and Chapuis et al.) propose that observed Mayer waves are noise-driven subthreshold oscillations

  31. Hypothesis: Noisy Precursors are Familiar Mayer Waves. There MUST be a nearby threshold. Decelerations are result of a parameter going above the bifurcation.

  32. Rate of change of = Rate in from heart – blood volume in arteries Rate out through capillaries to veins dV / dt = heart rate x blood volume per beat  - [P(arteries) - P(veins) ] / [Resistance] P(arteries) = V(arteries) / [compliance = ] Similar formulas for veins Rate of change of = Neg(t) a negative term caused by high BP heart rate + Pos(t -τ ) a positive term caused by low BP which however is delayed A model of the baroreflex loop J. Ottesen, Roskilde University, Denmark

  33. A problem in nonlinear dynamics Do these differential equations have a bifurcation that causes heart rate and blood pressure to oscillate?

  34. A problem in nonlinear dynamics Do these differential equations have a bifurcation that causes heart rate and blood pressure to oscillate? YES!! What is the period ??

  35. A problem in nonlinear dynamics Do these differential equations have a bifurcation that causes heart rate and blood pressure to oscillate? YES!! What is the period ?? Depending on parameters, can be ~ 15 seconds. Do they resemble the observed oscillations?

  36. A problem in nonlinear dynamics Do these differential equations have a bifurcation that causes heart rate and blood pressure to oscillate? YES!! What is the period ?? Depending on parameters, can be ~ 15 seconds. Do they resemble the observed oscillations? Nope. Do measurements of HR and BP in infants show the large correlated oscillations?

  37. A problem in nonlinear dynamics Do these differential equations have a bifurcation that causes heart rate and blood pressure to oscillate? YES!! What is the period ?? Depending on parameters, can be ~ 15 seconds. Do they resemble the observed oscillations? Nope. Do measurements of HR and BP in infants show the large correlated oscillations? Nope.

  38. The Future: Incoming Data Streams • 1. Electronic diagnosis of infectious disease? Can we identify invading organisms by HR monitoring? • Preliminary evidence: • Reduced variability Gram-positive bacteria (e.g. Streptococci, Staphylococci) • vancomycin • Clusters of decels Gram-negative bacteria (e.g. E. coli, Pseudomonas) • gentamicin, cefotaxime • If this preliminary result holds up, we have the first example of continuous, noninvasive, purely electronic monitoring that gives early warning of infectious disease and also gives partial diagnosis, thereby identifying the recommended therapy. • (A medical tricorder)

  39. The Future: Experiments proposed or discussed 2. Quadriplegics are subject to infections that they do not feel. Can heart rate monitoring provide early warning? 3. Can large decelerations be induced in animals? Models suggest that increasing the time delay in the baroreflex loop pushes the system above bifurcation. Can this be done by chemical means (beta blockers)? Or electrical means (cut the sympathetic nerve and use an electrical time delay circuit)? What happens when corresponding experiments are done on newborn or premature animals?

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