BACKGROUND: Identifying patients with low left ventricular ejection fraction (LVEF) in the emergency department using an electrocardiogram (ECG) may optimize acute heart failure (AHF) management. We aimed to assess the efficacy of 527 automated 12‑lead ECG features for estimating LVEF among patients with AHF. METHOD: Medical records of patients >
18 years old and AHF-related ICD codes, demographics, LVEF %, comorbidities, and medication were analyzed. Least Absolute Shrinkage and Selection Operator (LASSO) identified important ECG features and evaluated performance. RESULTS: Among 851 patients, the mean age was 74 years (IQR:11), male 56 % (n = 478), and the median body mass index was 29 kg/m CONCLUSIONS: An explainable machine learning model with physiologically feasible predictors may help screen patients with low LVEF in AHF.