Artificial Intelligence ECG to Detect Left Ventricular Dysfunction in COVID-19 - A Case Series

Attia, Zachi I.; Kapa, Suraj; Noseworthy, Peter A.; Lopez-Jimenez, Francisco; Friedman, Paul A.

Coronavirus disease 2019 (COVID-19) can result in deterioration of cardiac function, which is associated with high mortality. A simple point-of-care diagnostic test to screen for ventricular dysfunction would be clinically useful to guide management. We sought to review the clinical experience with an artificial intelligence ECG (AI ECG) to screen for ventricular dysfunction in patients with documented COVID-19. We examined all patients in the Mayo Clinic system who underwent clinically indicated electrocardiography and echocardiography within 2 weeks following a positive COVID-19 and had permitted use of their data for research were included. Of the 27 patients who met the inclusion criteria, one had a history of normal ventricular function who developed COVID-19 myocarditis with rapid clinical decline. The initial AI ECG in this patient indicated normal ventricular function. Repeat AI-ECG demonstrated a probability of ejection fraction (EF) < =40 % of 90.2%, corroborated with an echocardiogrpahic EF of 35%. One other patients had a pre -existing EF <=40%, accurately detected by the algorithm before and after COVID diagnosis, and another was found to have a low EF by AI ECG and echocardiography with the COVID diagnosis. The AUC for detection of EF < =40% was 0.95. This case series suggests that the AI ECG, previously demonstrated to detect ventricular dysfunction in a large general population, may be useful as a screening tool for the detection of cardiac dysfunction in patients with COVID-19.