Clinical Triage of Individuals With Warning Symptoms of Imminent Cardiac Arrest.

BACKGROUND: At least 50% of individuals who suffer sudden cardiac arrest (SCA) have warning symptoms before their SCA, but these are not sufficient to predict imminent SCA (ISCA). We hypothesized that combining symptoms with clinical profile could effectively predict ISCA. METHODS: In 2 community-based studies of SCA in Oregon and California, survivors of SCA who had experienced warning symptoms and called 911 (n=364) were compared with control subjects (n=313) who also called 911 for similar symptoms but did not have SCA. Symptom information was obtained directly from interviews as well as prehospital emergency records. We used machine learning to identify predictive combinations of symptoms and clinical features in this case-control study. The area under the receiver operating characteristic curve (AUC) and 5-fold cross-validation were used to assess model performance and stability. RESULTS: Warning symptom-clinical profile combinations were successfully identified. The combination of chest pain and known heart failure predicted ISCA among males, while chest pain and known coronary artery disease predicted ISCA among females (AUC=0.788). With dyspnea, both history of coronary artery disease and heart failure were predictors (AUC=0.745). In the absence of chest pain or dyspnea, seizure-like symptoms, coronary artery disease, and heart failure were predictors (AUC, 0.775). 5-fold cross-validation produced consistent results. CONCLUSIONS: Combinations of warning symptoms and clinical features distinguished individuals with SCA from individuals without SCA with good accuracy (AUCs, 0.745-0.788). These findings have implications for clinical triage of ISCA warning symptoms in emergency settings and warrant investigation in larger studies.