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Research Description
Our goal is to develop predictive monitoring for early diagnosis of subacute, potentially catastrophic illness. To date, our focus has been on premature infants in an intensive care unit.
The clinical problem that we first addressed is neonatal sepsis, a bacterial infection of the bloodstream in premature newborn infants in the intensive care unit. This illness is common and adds greatly to morbidity and mortality of these fragile patients, and is difficult to diagnose in its early and most treatable stages. We reasoned that continuous monitoring of a sensitive physiological measure like heart rate variability might show changes before obvious signs of a critical illness so advanced that therapy would not help.
We began by inspecting heart rate records over prolonged periods in infants at risk of sepsis. We found obvious changes in the heart rate characteristics of septic infants even before they were clinically ill, with reduced variability and transient decelerations similar to the findings in fetal distress. Since there were no measures available to detect these findings, we developed new mathematical techniques of sample entropy and sample asymmetry. We did clinical studies to develop predictive multivariate statistical models based on our new measures, and we validated them at a second medical center. We recently completed a multicenter randomized clinical trial of 3000 very low birthweight infants into to test the hypothesis that our monitoring system improves infants’ outcomes. Indeed it does – monitored infants had a more than 20% reduction in mortality.
Our goal is to develop predictive monitoring for early diagnosis of subacute, potentially catastrophic illness. To date, our focus has been on premature infants in an intensive care unit.
The clinical problem that we first addressed is neonatal sepsis, a bacterial infection of the bloodstream in premature newborn infants in the intensive care unit. This illness is common and adds greatly to morbidity and mortality of these fragile patients, and is difficult to diagnose in its early and most treatable stages. We reasoned that continuous monitoring of a sensitive physiological measure like heart rate variability might show changes before obvious signs of a critical illness so advanced that therapy would not help.
We began by inspecting heart rate records over prolonged periods in infants at risk of sepsis. We found obvious changes in the heart rate characteristics of septic infants even before they were clinically ill, with reduced variability and transient decelerations similar to the findings in fetal distress. Since there were no measures available to detect these findings, we developed new mathematical techniques of sample entropy and sample asymmetry. We did clinical studies to develop predictive multivariate statistical models based on our new measures, and we validated them at a second medical center. We recently completed a multicenter randomized clinical trial of 3000 very low birthweight infants into to test the hypothesis that our monitoring system improves infants’ outcomes. Indeed it does – monitored infants had a more than 20% reduction in mortality.
研究兴趣
论文共 364 篇作者统计合作学者相似作者
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The journal of pediatrics/The Journal of pediatricspp.114042-114042, (2024)
medrxiv(2024)
Valerie E. Cyphers, Swet M. Patel, Brendan D. McNamara,William B Ashe,Sarah J. Ratcliffe,J. Randall Moorman,Jessica Keim-Malpass,Shrirang M. Gadrey,Sherry L. Kausch
medrxiv(2024)
Physiological measurementno. 6 (2024)
Pediatric Researchno. 4 (2024): 1060-1069
medrxiv(2024)
JAMA network openno. 5 (2023)
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作者统计
#Papers: 362
#Citation: 19903
H-Index: 57
G-Index: 138
Sociability: 7
Diversity: 3
Activity: 56
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