Abstract 346: Deep Learning Approach for Predicting Good Neurological Outcome for Out-of-Hospital Cardiac Arrest Patients

Abstract

Background: Cardiac arrest continues to pose a significant public health burden, with over 350,000 cases of out-of-hospital cardiac arrests (OHCA) occurring each year in the United States, and nearly 90% of them being fatal. The objective of this work was to develop a deep learning model that can accurately predict the Cerebral Performance Category (CPC) in adult OHCA cases. Methods: Adult OHCA cases at an urban academic emergency department were enrolled between 2018-2023. We evaluated data collected post return of spontaneous circulation (ROSC) at the first hour. Six different groups of data were tested for the prediction of CPC score: (1) Post-Rosc Vitals, (2) Pre-hospital & ED data points, and (3) Hospital Admission data points. The second group of exploratory research variables include: (4) ultrasound variables, (5) biomarkers and (6) Sex steroid hormones. The figure below illustrates the prediction framework for cardiac arrest patients, post ROSC, admitted to the hospital. Results: Of the total of 109 cases that were enrolled, 45% were female and 48% were Black. More than one-third (35%) were discharged alive but only 20% had a CPC of 1-2. While the base model with clinical and demographic variables had an AUC of 0.54, addition of subsequent variables improved the AUC substantially. The AUC improved to 0.59, 0.67 and 0.78 when hormones, ultrasound and biomarker variables were added, respectively, to the base model. However, the optimal model included all variables, resulting in a noteworthy AUC score of 0.861. Conclusion: Our findings emphasize the significance of incorporating novel variables to comprehensively evaluate the outcomes of cardiac arrest patients so that better prediction models can be developed, potentially aiding in modifying procedures and any necessary measures to shift the outcome in favor of preserving patients' lives after OHCA.