Premature infants in the Neonatal ICU require hospitalization until they reach physiological maturity, an average of 60 days. While in the hospital, though, they are at risk of subacute potentially catastrophic illnesses such as infection, respiratory decompensation leading to urgent unplanned intubation, and intracranial bleeding. These illnesses are common and deadly. In each case, early diagnosis has the promise to improve outcome through early intervention. The long-term goal of our group is to develop such novel predictive monitoring strategies as early warning systems through advanced mathematical and statistical analysis of waveforms and other informatics data from the bedside monitor. This kind of approach recently led the group and its colleagues at 7 other centers to complete a NICHD- sponsored randomized clinical trial in 3000 premature infants, the largest ever conducted in this population. The result was very important - simply showing the results of a predictive monitor to clinicians reduced the death rate by more than 20%. This predictive tool, though, requires ICU level monitoring with chest leads for EKG and breathing signals. Many more infants could be helped if there were strategies for using just the ubiquitous pulse oximeter, which provides heart rate and O2 saturation data every 1 or 2 seconds. Deriving predictive algorithms that use this small data stream requires large databases of relevant clinical information and monitor data, including vital signs and waveforms, from many infants at multiple sites. The team of clinicians and mathematicians ? a collaboration of University of Virginia, Washington University ? St Louis, and Columbia University ? will discover oximetry-based phenotypes of abnormal physiology and develop algorithms to detect them. The large-scale databases and computing capability for this work is in daily use, the UVa-Columbia collaboration has been productive in the first years of this award, and this competitive renewal proposal will leave them in the position to undertake randomized clinical trials to test the impact of the new monitoring. This represents a paradigm shift in patient care ? monitors that report trends of development of health and illness rather than fleeting values.

Public Health Relevance

Moorman JR, Fairchild KD Predictive informatics monitoring in the Neonatal Intensive Care Unit Project narrative Premature infants in the Neonatal Intensive Care Unit are vulnerable to subacute potentially catastrophic illnesses for which early warning might allow life-saving treatments. The proposed study will result in improved bedside monitoring to give doctors and nurses this early warning through advanced mathematical analysis of physiologic waveforms and vital sign data. The University of Virginia group has successfully achieved this goal using heart rate analysis for early diagnosis of infection, and joins Columbia University and Washington University with their clinical and quantitative expertise.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Research Project (R01)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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Higgins, Rosemary
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University of Virginia
Internal Medicine/Medicine
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United States
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Fairchild, Karen D; Nagraj, V Peter; Sullivan, Brynne A et al. (2018) Oxygen desaturations in the early neonatal period predict development of bronchopulmonary dysplasia. Pediatr Res :
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Sullivan, Brynne A; McClure, Christina; Hicks, Jamie et al. (2016) Early Heart Rate Characteristics Predict Death and Morbidities inĀ PretermĀ Infants. J Pediatr 174:57-62

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