Artificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms
Authors: Lovedeep S. Dhingra, Arya Aminorroaya, Aline F. Pedroso, Akshay Khunte, Veer Sangha, Daniel McIntyre, Clara K. Chow, Folkert W. Asselbergs, Luisa C. C. Brant, Sandhi M. Barreto, Antonio Luiz P. Ribeiro, Harlan M. Krumholz, Evangelos K. Oikonomou, Rohan Khera
Journal: JAMA Cardiology (2025)

Importance Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) may enable large-scale community-based risk assessment. Objective To evaluate whether an artificial intelligence (AI) algorithm can predict HF risk from noisy single-lead ECGs. Design, Setting, and Participants A retrospective cohort study of individuals without HF at baseline was conducted among individuals with conventionally obtained outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of the UK Biobank (UKB) and the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Data analysis was performed from September 2023 to February 2025. Exposure AI-ECG–defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures Among individuals with ECGs, lead I ECGs were isolated and a noise-adapted AI-ECG model (to simulate ECG signals from wearable devices) trained to identify LVSD was deployed. The association of the model probability with new-onset HF, defined as the first HF hospitalization, was evaluated. The discrimination of AI-ECG was compared against 2 risk scores for new-onset HF (Pooled Cohort Equations to Prevent Heart Failure [PCP-HF] and Predicting Risk of Cardiovascular Disease Events [PREVENT] equations) using the Harrel C statistic, integrated discrimination improvement, and net reclassification improvement. Results There were 192 667 YNHHS patients (median [IQR] age, 56 [41-69] years; 111 181 women [57.7%]), 42 141 UKB participants (median [IQR] age, 65 [59-71] years; 21 795 women [51.7%]), and 13 454 ELSA-Brasil participants (median [IQR] age, 51 [45-58] years; 7348 women [54.6%]) with baseline ECGs. A total of 3697 (1.9%) developed HF in YNHHS over a median (IQR) of 4.6 (2.8-6.6) years, 46 (0.1%) in UKB over a median (IQR) of 3.1 (2.1-4.5) years, and 31 (0.2%) in ELSA-Brasil over a median (IQR) of 4.2 (3.7-4.5) years. A positive AI-ECG screening result for LVSD was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability was associated with a 27% to 65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG’s discrimination for new-onset HF was 0.723 (95% CI, 0.694-0.752) in YNHHS, 0.736 (95% CI, 0.606-0.867) in UKB, and 0.828 (95% CI, 0.692-0.964) in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions alongside PCP-HF and PREVENT equations was associated with a higher Harrel C statistic (difference in addition to PCP-HF, 0.080-0.107; difference in addition to PREVENT, 0.069-0.094). AI-ECG had an integrated discrimination improvement of 0.091 to 0.205 vs PCP-HF and 0.068 to 0.192 vs PREVENT; it had a net reclassification improvement of 18.2% to 47.2% vs PCP-HF and 11.8% to 47.5% vs PREVENT. Conclusions and Relevance Across multinational cohorts, a noise-adapted AI-ECG model estimated HF risk using lead I ECGs, suggesting a potential HF risk-stratification strategy requiring prospective study using wearable and portable ECG devices.