Predicting cardiac arrest in the emergency department

Abstract

Abstract In‐hospital cardiac arrest remains a leading cause of death: roughly 300,000 in‐hospital cardiac arrests occur each year in the United States, ≈10% of which occur in the emergency department. ED‐based cardiac arrest may represent a subset of in‐hospital cardiac arrest with a higher proportion of reversible etiologies and a higher potential for neurologically intact survival. Patients presenting to the ED have become increasingly complex, have a high burden of critical illness, and face crowded departments with thinly stretched resources. As a result, patients in the ED are vulnerable to unrecognized clinical deterioration that may lead to ED‐based cardiac arrest. Efforts to identify patients who may progress to ED‐based cardiac arrest have traditionally been approached through identification of critically ill patients at triage and the identification of patients who unexpectedly deteriorate during their stay in the ED. Interventions to facilitate appropriate triage and resource allocation, as well as earlier identification of patients at risk of deterioration in the ED, could potentially allow for both prevention of cardiac arrest and optimization of outcomes from ED‐based cardiac arrest. This review will discuss the epidemiology of ED‐based cardiac arrest, as well as commonly used approaches to predict ED‐based cardiac arrest and highlight areas that require further research to improve outcomes for this population.

Keywords

• cardiac arrest
• deterioration
• early warning scores
• machine learning
• prediction
• quality improvement