Introduction

Introduction: There are only scarce models developed for stratifying the risk of cardiac arrest from COVID-19 patients presenting to the ED with suspected pneumonia. By using the machine learning (ML) approach, we aimed to develop and validate the ML models to predict in-hospital cardiac arrest (IHCA) in patients admitted from the ED. Hypothesis: We hypothesized that ML approach can serve as a valuable tool in identifying patients at risk of IHCA in a timely fashion. Methods: We included the COVID-19 patients admitted from the EDs of five hospitals in Texas between March and November 2020. All adult (≥ 18 years) patients were included if they had positive RT-PCR for SARS-CoV-2 and also received CXR examination for suspected pneumonia. Patients' demographic, past medical history, vital signs at ED triage, CXR findings, and laboratory results were retrieved from the EMR system. The primary outcome (IHCA) was identified via a resuscitation code. Patients presented as OHCA or without any blood testing were excluded. Nonrandom splitting strategy based on different location was used to divide the dataset into the training (one urban and two suburban hospitals) and testing cohort (one urban and one suburban hospital) at around 2-to-1 ratio. Three supervised ML models were trained and performances were evaluated and compared with the National Early Warning Score (NEWS) by the area under the receiver operating characteristic curve (AUC). Results: We included 1,485 records for analysis. Of them, 190 (12.8%) developed IHCA. Of the constructed ML models, Random Forest outperformed the others with the best AUC result (0.930, 95% CI: 0.896-0.958), followed by Gradient Boosting (0.929, 95% CI: 0.891-0.959) and Extra Trees classifier (0.909, 95% CI: 0.875-0.943). All constructed ML models performed significantly better than by using the NEWS scoring system (AUC: 0.787, 95% CI: 0.725-0.840). The top six important features selected were age, oxygen saturation at triage, and lab data of APTT, lactic acid, and LDH. Conclusions: The ML approach showed excellent discriminatory performance to identify IHCA for patients with COVID-19 and suspected pneumonia. It has the potential to save more life or provide end-of-life decision making if successfully implemented in the EMR system.