# Use of Predictive Analytics to Quantify Neonatal Hypothermia Burden After Cardiac Surgery

> **NIH NIH F31** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $49,252

## Abstract

ABSTRACT
Neonates (infants ≤ 28 days), especially those with congenital heart disease (CHD), are among the most
vulnerable populations cared for by critical care nurses. Approximately, two out of three CHD neonates
experience unintentional hypothermia after cardiopulmonary bypass (CPB). Unintentional hypothermia impairs
cellular function, which can be linked to poor outcomes frequently reported in this population. To date, there
are no studies examining the association between the burden of unintentional hypothermia and clinical
outcomes in neonates with CHD. This knowledge would render future opportunities to improve nursing care
and prevent avoidable safety events in these vulnerable neonates. To address this gap, we propose to use
retrospective data from CardioAccess (database local to the Children’s Hospital of Philadelphia [CHOP]),
which includes one of the largest multicenter repositories of neonatal cardiac surgery data available to date
(Pediatric Cardiac Critical Care Consortium [PC4]), as well as, the electronic health record. Using data from at
least 432 neonates who have undergone CPB between 2015 and 2019, we will quantify the time course of
hourly temperature trajectories within the initial 24–48 hours after CPB and evaluate their relation to key clinical
outcomes. We will specifically study the temporal trends of unintentional hypothermia burden (temperature
depth and duration), which challenges current practice, where care is based on maintaining a single,
preselected temperature threshold that is driven by consensus, rather than evidence. Single threshold values
are not dynamic representations of the complexity that makes up temperature. A more robust output, such as
an accumulative hypothermia burden index, is needed to assist clinicians with interpretation of this dynamic
indicator of overall health. Our Specific Aims are: 1) Identify distinct temporal temperature patterns in CHD
neonates after CPB using both: a multilevel model for intensive longitudinal data with group-based trajectory
modeling; and an unsupervised machine learning technique using principal component analysis followed by k-
means clustering of longitudinal data. 2) Determine the relationship between hypothermia burden subgroups /
clusters and important clinical outcomes in this population. Our team has a demonstrated expertise in building
clinically relevant and physiologically plausible markers of adverse outcomes in critically ill patients. This study
aligns with the NINR’s priorities of promoting wellness and preventing illness across the lifespan, as well as,
using recent advances in precision medicine. The research conducted under this award will take place at the
University of Pittsburgh School of Nursing, a research-intensive institution (data analysis), and CHOP (data
provision). The personalized training plan outlined in this application, supports the applicant’s career and
academic development goals to become an independent nurse researcher.

## Key facts

- **NIH application ID:** 10415862
- **Project number:** 5F31NR019725-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Stephanie M Helman
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $49,252
- **Award type:** 5
- **Project period:** 2021-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10415862

## Citation

> US National Institutes of Health, RePORTER application 10415862, Use of Predictive Analytics to Quantify Neonatal Hypothermia Burden After Cardiac Surgery (5F31NR019725-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10415862. Licensed CC0.

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