Artificial intelligence analysis of the single-lead ECG predicts long-term clinical outcomes.

Abstract

Aims: Artificial intelligence (AI) applied to a single-lead electrocardiogram (AI-ECG) can detect impaired left ventricular systolic dysfunction [LVSD: left ventricular ejection fraction (LVEF) ≤ 40%]. This study aimed to determine if AI-ECG can also predict the two-year risk of major adverse cardiovascular events (MACE) and all-cause mortality independent of LVSD.

Methods and results: Clinical outcomes after two-year follow-up were collected on patients who attended for routine echocardiography and received simultaneous single-lead-ECG recording for AI-ECG analysis. MACE and all-cause mortality were compared by Cox regression, measured against the classification of LVEF > or ≤40%. A subgroup analysis was performed on patients with echocardiographic LVEF ≥ 50%. With previously established thresholds, 'positive' AI-ECG was defined as an LVEF-predicted ≤40%, and negative AI-ECG signified an LVEF-predicted >40%; 1007 patients were included for analysis (mean age, 62.3 years; 52.4% male). 339 (33.7%) had an AI-ECG-predicted LVEF ≤ 40% and had a higher MACE rate (LVEF ≤ 40% vs. >40%: 34.2% vs.11.9%; adjusted hazard ratio (aHR) 1.93; 95% CI, 1.39-2.69; P < 0.001), primarily driven by increased mortality (23% vs. 9.6%; P < 0.001; aHR 1.56; 95% CI, 1.06-2.29; P = 0.0239). In patients with echocardiographic LVEF ≥ 50%, there was a higher incidence of MACE in those with an AI-ECG 'false positive' prediction of LVEF ≤ 40% (27.2% vs.11.9%; P < 0.001; aHR 1.71 and 95% CI, 1.11-2.47) and all-cause mortality (20.4% vs. 9.6%; P < 0.001; aHR 1.59, 95% CI, 1.09-2.42).

Conclusion: An AI-ECG algorithm designed to detect LVEF ≤ 40% can also identify patients at risk of MACE and all-cause mortality from single-lead ECG recording-independent of actual LVEF on echo. This requires further evaluation as a point-of-care risk stratification tool.