Artificial intelligence analysis of the single-lead ECG predicts long-term clinical outcomes
File(s)
Published version
Author(s)
Type
Journal Article
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.
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.
Date Issued
2025-07-01
Date Acceptance
2025-04-02
Citation
European Heart Journal: Digital Health, 2025, 6 (4), pp.635-644
ISSN
2634-3916
Publisher
Oxford University Press
Start Page
635
End Page
644
Journal / Book Title
European Heart Journal: Digital Health
Volume
6
Issue
4
Copyright Statement
© The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
10.1093/ehjdh/ztaf057
Publication Status
Published
Date Publish Online
2025-06-09