# Predictive Informatics Monitoring in the Neonatal Intensive Care Unit

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2021 · $629,494

## Abstract

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.

## Key facts

- **NIH application ID:** 10225559
- **Project number:** 5R01HD072071-07
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** KAREN D FAIRCHILD
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $629,494
- **Award type:** 5
- **Project period:** 2014-07-10 → 2022-07-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10225559

## Citation

> US National Institutes of Health, RePORTER application 10225559, Predictive Informatics Monitoring in the Neonatal Intensive Care Unit (5R01HD072071-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10225559. Licensed CC0.

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