Abstract
INTRODUCTION: While research has begun to understand emergency department-based cardiac arrest (EDCA), consensus on what exactly constitutes EDCA remains unknown. In this study we aimed to explore the grouping of EDCA by using an unsupervised machine-learning algorithm and to investigate how these underlying clusters related to patient outcomes. METHODS: We retrieved electronic health record data from an ED in a tertiary medical center. The EDCAs were identified via the cardiopulmonary resuscitation log. We used k-means cluster analysis to group EDCAs and t-distributed stochastic neighbor embedding (t-SNE) for visualization. Primary outcomes were ED mortality and ED length of stay (LOS). The analyses were repeated using an independent ED data set, the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) dataset. RESULTS: From 2019 to 2022, there were 366 EDCA events. Cluster analysis identified three distinct clusters (Cluster 1 or immediate risk, n=54 [15%]; Cluster 2 or early risk, n=274 [75%]; Cluster 3 or late risk, n=38 [10%]). Cluster 1 patients had the shortest median time to EDCA (< 1 hour), followed by Cluster 2 (3 hours) and Cluster 3 (81 hours). Near cardiac arrest at triage was the most common cause of EDCA in Cluster 1, while respiratory illnesses and sepsis were more common in Cluster 3. The causes of EDCA in Cluster 2 were diverse, with predominantly cardiovascular and neurologic emergencies. The t-SNE revealed farther distances from Cluster 1 to the other two clusters, suggesting its most critical nature. Cluster 3 had the highest mortality (58%), followed by Clusters 1 (48%) and 2 (35%) (P = .01). Cluster 1 had the shortest median LOS (median, 4 hours), while Cluster 3 had the longest LOS (81 hours) (P < .001). In the independent data set, Cluster 1 remained, but Clusters 2 and 3 appeared to merge due to a shorter ED LOS overall. CONCLUSION: We identified three novel clusters (immediate, early, and late risk) with distinct patterns in clinical presentation, putative causes of ED-based cardiac arrest, and ED outcomes. Understanding these clinical phenotypes may help develop cluster-specific interventions to prevent EDCA or intervene most appropriately. Cluster 1 patients may benefit from resuscitation efforts, and Clusters 2 or 3 patients can benefit from timely interventions for cardiac, respiratory, and neurologic emergencies. In addition, for patients with prolonged ED boarding, periodic monitoring with an early warning system may prevent a cardiac arrest event.