A pilot study to predict cardiac arrest in the pediatric intensive care unit. (April 2023)

• Record Type: Journal Article
• Authors: Kenet, Adam L.; Pemmaraju, Rahul; Ghate, Sejal; Raghunath, Shreeya; Zhang, Yifan; Yuan, Mordred; Wei, Tony Y.; Desman, Jacob M.; Greenstein, Joseph L.; Taylor, Casey O.; Ruchti, Timothy; Fackler, James; Bergmann, Jules
• Is Part Of: Resuscitation. Volume 185(2023)
• Publication Date: 2023-04
• Subjects: Cardiac arrest -- Machine learning -- Predictive modeling -- High frequency waveform data -- Heart rate variability -- Biomedical engineering -- Computational medicine -- Pediatric intensive care unit -- Critical care medicine; Resuscitation -- Periodicals; Resuscitation -- Periodicals; Réanimation -- Périodiques; Electronic journals; 616.025
• DOI: 10.1016/j.resuscitation.2023.109740
• Languages: English
• ISSNs: 0300-9572

Abstract

Abstract: Background: Cardiac arrest is a leading cause of mortality prior to discharge for children admitted to the pediatric intensive care unit. To address this problem, we used machine learning to predict cardiac arrest up to three hours in advance. Methods: Our data consists of 240 Hz ECG waveform data, 0.5 Hz physiological time series data, medications, and demographics from 1, 145 patients in the pediatric intensive care unit at the Johns Hopkins Hospital, 15 of whom experienced a cardiac arrest. The data were divided into training, validating, and testing sets, and features were generated every five minutes. 23 heart rate variability (HRV) metrics were determined from ECG waveforms. 96 summary statistics were calculated for 12 vital signs, such as respiratory rate and blood pressure. Medications were classified into 42 therapeutic drug classes. Binary features were generated to indicate the administration of these different drugs. Next, six machine learning models were evaluated: logistic regression, support vector machine, random forest, XGBoost, LightGBM, and a soft voting ensemble. Results: XGBoost performed the best, with 0.971 auROC, 0.797 auPRC, 99.5% sensitivity, and 69.6% specificity on an independent test set. Conclusion: We have created high-performing models that identify signatures of in-hospital cardiac arrest (IHCA) that may not be evident to clinicians. These signatures include a combination of heart rate variability metrics, vital signs data, and therapeutic drug classes. These machine learning models can predict IHCA up to three hours prior to onset with high performance, allowing clinicians to intervene earlier, improving patient outcomes.