Deep learning analysis of single-lead electrocardiograms enables pragmatic heart failure risk assessment in the general population.

Abstract

Aims: Effective heart failure (HF) prevention requires early identification of high-risk individuals, yet population-wide stratification remains difficult. We evaluated whether deep learning using single-lead (lead I) electrocardiograms (ECGs), obtainable from medical systems and wearables, enables population-scale risk assessment. We developed AI-HF to estimate incident clinical HF risk using UK Biobank (UKB) data, validating in the prospective SHIP-START and SHIP-TREND cohorts.

Methods and results: The analysis included 31 740 UKB participants (median age 64, 5.2 year follow-up, 243 events), 3025 SHIP-START participants (age 50, 15 year follow-up, 166 events), and 1342 SHIP-TREND participants (age 51, 9 year follow-up, 84 events). Participants with prevalent HF were excluded. Performance was evaluated at a harmonized 5-year prediction horizon. C-indices for incident clinical HF were 0.693 [95% confidence interval (CI) 0.654-0.732] in UKB, 0.715 (0.652-0.777) in SHIP-START, and 0.791 (0.749-0.833) in SHIP-TREND. Hazard ratios per standard deviation increase in AI-HF output were 1.67 (1.56-1.79), 1.43 (1.25-1.65), and 1.46 (1.34-1.59), respectively (all P < 0.001). Adding biometric variables improved discrimination modestly (C-indices: 0.714, 0.718, and 0.77).

Conclusion: Across cohorts, AI-HF identified individuals at elevated 5-year incident clinical HF risk using single-lead ECGs. Given the ubiquity of wearables, this method may enable population-scale assessment to support targeted prevention and early intervention.