Deep Learning of ECG Waveforms for Diagnosis of Heart Failure With a Reduced Left Ventricular Ejection Fraction

The performance and clinical implications of the deep learning aided algorithm using electrocardiogram of heart failure (HF) with reduced ejection fraction (DeepECG-HFrEF) were evaluated in patients with acute HF. The DeepECG-HFrEF algorithm was trained to identify left ventricular systolic dysfunction (LVSD), defined by an ejection fraction (EF) <40%. Symptomatic HF patients admitted at Seoul National University Hospital between 2011 and 2014 were included. The performance of DeepECG-HFrEF was determined using the area under the receiver operating characteristic curve (AUC) values. The 5-year mortality according to DeepECG-HFrEF results was analyzed using the Kaplan-Meier method. A total of 690 patients with contributing 18,449 ECGs were included (mean age 67.8±14.4 years; men, 56%). HFrEF (+) identified an EF <40% and HFrEF (-) identified EF ≥40%. The AUC value was 0.844 for identifying HFrEF among patients with acute HF. Those classified as HFrEF (+) showed worse survival than HFrEF (-) (log-rank p<0.001). The DeepECG-HFrEF algorithm can discriminate HFrEF in a real-world HF cohort with excellent performance. HFrEF (+) was associated with higher mortality. The DeepECG-HFrEF algorithm may help the identification of LVSD and of patients at risk of worse survival in resource-limited settings.