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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.

Introduction: Little is known about in-hospital cardiac arrest (IHCA) in the U.S. emergency department (ED). This study aimed to describe the incidence and mortality of ED-based IHCA visits and to investigate factors associated with higher incidence and poor outcome of IHCA. Hypothesis: We hypothesized that ED-based IHCA contributes to a stable proportion of ED visits and remains a higher mortality rate after the event. Methods: Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS) during 2010 to 2018. Adult ED visits with IHCA were identified using the cardiopulmonary resuscitation code, excluding those with out-of-hospital cardiac arrest. Analysis used descriptive statistics and multivariable logistic regression accounting for NHAMCS's complex survey design. Results: Over the 9-year study period, there was an estimated 1,114,000 ED visits with IHCA and the proportion of IHCA visits in the entire ED population (1.8 per 1,000 ED visits) appeared stable. The mean age of the IHCA visits was 60 years, and 65% were men. Older age, male sex, arrival by ambulance, and being uninsured independently predicted a higher incidence of ED-based IHCA. About 51% of ED-based IHCA died in the ED, and the trend remained stable. Arrival by ambulance, nighttime or weekend arrival, and being in the non-Northeast location were independently associated with a higher mortality rate after IHCA. Conclusions: The high burden of ED visits with IHCA persisted and the outcome remained poor. Some patients were disproportionately affected and certain contextual factors were associated with a poorer outcome.