Predictive Informatics Monitoring in the Neonatal Intensive Care Unit

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): 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 sub-acute potentially catastrophic illnesses such as infectio, 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%. The overall conceptual framework is that some sub-acute potentially catastrophic illnesses have subclinical prodromes with abnormal physiologic signatures. This is based on ideas about the systemic inflammatory response syndrome and the cholinergic anti-inflammatory pathway that link inflammation to abnormal signal transduction and autonomic nervous system activity. The result is that illness, even in early stages, leads to uncoupling of organs and abnormal control of heart and respiratory rhythms that can be detected using mathematical algorithms tailored to clinical insights. Achieving our goals of predictive informatics monitoring requires a large database of relevant clinical information and monitor data from the University of Virginia NICU, including vita signs and waveforms. The team of clinicians and mathematicians - a collaboration of University of Virginia and Columbia University - will discover phenotypes of abnormal physiology and develop algorithms to detect them. The large-scale computing capability for this work is in daily use, and the group will be poised 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.