# Predictive informatics monitoring in the Neonatal Intensive Care Unit

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2024 · $653,486

## Abstract

PROJECT SUMMARY/ABSTRACT
Significance: Premature very low birth weight (VLBW) infants in the Neonatal ICU continue to suffer morbidity
and mortality from sepsis and necrotizing enterocolitis (NEC), and early detection and treatment of these
illnesses has been shown to improve survival and outcomes. Continuously monitored vital signs contain subtle
changes in the early stage of sepsis and NEC, but these physiological markers are invisible with current
technology, even for the most sophisticated monitors and experienced clinicians. Our group continues to build
on experience and collaboration to develop early warning systems through advanced time series and machine
learning data analytics that will improve outcomes for premature infants. Progress: Our first such early warning
system, the HeRO monitor, alerts clinicians to abnormal heart rate characteristics and was shown to reduce
sepsis-associated mortality by 40% in a randomized trial of 3003 VLBW infants. The past 7 years of NIH
support produced several important discoveries, including 1) Adding pulse oximetry oxygenation data (SpO2)
to electrocardiogram data improves sepsis and NEC prediction; 2) Center-specific differences in patient
demographics and care practices impact algorithm performance; 3) High cross-correlation of heart rate and
SpO2 reflects increased apnea and sepsis risk across multiple sites. Innovation: The proposed work
represents a paradigm shift in patient care – monitors that report trends of development of health and illness
rather than fleeting values, leading to improved outcomes of preterm infants in the NICU. Approach: In the
current proposal we are adding a fourth large NICU to accomplish the following aims: Aim 1: Use advanced
time series analytics and machine learning to refine and expand predictive monitoring algorithms for sepsis
and NEC; Aim 2: Determine the impact of demographics and center on outcomes and algorithm performance;
Aim 3: Share multi-center data and analytics globally by building on an existing platform. Investigators: Co-PI's
Fairchild and Moorman have a longstanding collaboration and have led successful multicenter clinical and
analytical research for the life of this grant and have strengthened the team by adding new collaborators and
centers. Environment: The centers involved in this proposal are unique in their ability to collect and analyze
large vital sign and clinical data sets, made possible by robust institutional research and computing support.

## Key facts

- **NIH application ID:** 10831515
- **Project number:** 5R01HD072071-10
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** KAREN D FAIRCHILD
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $653,486
- **Award type:** 5
- **Project period:** 2014-07-10 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10831515

## Citation

> US National Institutes of Health, RePORTER application 10831515, Predictive informatics monitoring in the Neonatal Intensive Care Unit (5R01HD072071-10). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10831515. Licensed CC0.

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