# Multi-Site Validation Study of the HLHS Arrest Predictor

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2020 · $574,855

## Abstract

Each year in the US, there are thousands of children who are born with a severe congenital
deformation, where one of the two ventricles in the heart is severely underdeveloped. If they
survive infancy, these children can go on to live full and normal lives. The mortality rate for this
condition is 15%, and 63% of these deaths are due to cardio-respiratory arrests. This alarming
rate of arrest events persists despite vigilant ICU care with the best available intensive
monitoring equipment. Our overall goal is to improve current patient monitoring systems by
developing machine learning algorithms that can predict the onset of an arrest event, hours
before it occurs. This early warning indication can be provided to nurses and doctors who can
intervene to prevent these life-threatening events from occurring, improving outcomes for these
critically ill children. Preliminary studies at Texas Children's Hospital have resulted in a
computer algorithm that can estimate the odds of arrest in single ventricle children, 1-2 hours
prior to overt symptoms. The algorithm is based on a logistic regression risk model, and was
developed using over 55,000 hours of vital sign observations. The specific aims of the proposed
research are: (1) To test the hypothesis that this computer algorithm can provide an early
warning of arrest, with sufficient accuracy for clinical use across different clinical centers; (2) To
understand the relationship between the risk score provided by this algorithm and other post-
surgical complications that commonly occur in these children during the their hospitalization.
Aim 1 is a multi-center study of this algorithm on a large, prospective, and independent cohort,
in order to measure its true predictive performance. Performance metrics to be measured will be
the ROC area and positive and negative likelihood ratios. This will help us determine the optimal
threshold for the detection of an arrest event. Aims 2 focus on relating the risk of arrest to
outcomes such as mechanical circulatory support, re-operation, arrhythmia, and necrotizing
enterocolitis. Successful completion of these aims will result in the first clinically validated, real-
time early warning system for anticipating acute arrest events in children with single ventricle
physiology. The techniques and technologies developed in this work are immediately
translatable to other diseases and conditions for both adults and children.

## Key facts

- **NIH application ID:** 9996775
- **Project number:** 5R01HL142994-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Craig G Rusin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $574,855
- **Award type:** 5
- **Project period:** 2018-07-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9996775

## Citation

> US National Institutes of Health, RePORTER application 9996775, Multi-Site Validation Study of the HLHS Arrest Predictor (5R01HL142994-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9996775. Licensed CC0.

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