Monitoring and Prediction of Cardiac Arrest in Pediatric ICU Patients with Machine Learning

Cardiac arrest is a leading cause of mortality within pediatric intensive care units (PICU), causing ~40% of pediatric CA in the US every year. To enable early detection of children at risk, we employed machine learning (ML) techniques to predict in-hospital cardiac arrests (IHCA) up to five hours in advance. Our data encompasses 240-Hz electrocardiogram (ECG), 60-Hz photoplethysmography (PPG), 0.5-Hz physiological time series, medication, demographics, and precursor events (respiratory failure) from 73 patients (n_IHCA = 14) admitted to the PICU at the Johns Hopkins Hospital. The developed ML models achieve and demonstrate actionable early warning of impending IHCA in pediatric patients using multimodal signals and electronic health record data collected routinely in the PICU.