Electrocardiogram-based Artificial Intelligence to Predict Incident Heart Failure With Preserved vs Reduced Ejection Fraction.

Authors

Shaan Khurshid, Valentina D'Souza, Samuel F Friedman, Athar Roshandelpoor, Pedro H Ribeiro, Jonathan W Cunningham, Emily S Lau, Abdulla Shahid, Shinwan Kany, Jordan B Strom, Jonathan W Waks, Mahnaz Maddah, Patrick T Ellinor, Jennifer E Ho

Published in

Journal of cardiac failure. Volume 32. Issue 11. Pages 1889-1901.

Abstract

Heart failure is classically divided into 2 subtypes based on the ejection fraction (EF): HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), each with varying risk-mitigation strategies. Electrocardiogram-based artificial intelligence (AI) may facilitate HF subtype-specific risk prediction, enabling tailored prevention.
Based on a previously validated HF-prediction architecture, we developed an ECG-based AI model to predict 10-year risk of incident HFrEF (LVEF <50%) vs HFpEF (LVEF ≥50%) (Electrocardiogram to Heart Failure Subtype, ECG2HF-Subtype). ECG2HF-Subtype was developed in 98,190 individuals at Massachusetts General Hospital (MGH) and validated in 3 test sets: MGH, Brigham and Women's Hospital (BWH), and Beth Israel Deaconess Medical Center (BIDMC) among individuals without prior HF. HF was identified by using a validated natural language processing model, and subtypes were classified by LVEF proximate to incident HF. Discrimination was quantified using time-dependent area under the receiver operating characteristic curve (AUROC) and average precision (AP), with comparisons to age and sex, and a multinomial logistic regression classifier, including variables in the Predicting Risk of cardiovascular disease EVENTs (PREVENT-HF) equations. In aggregate, models were evaluated in MGH (n = 16,739, ages 56 ± 17, 54% women), BWH (n = 75,195, 56 ± 16, 59%), and BIDMC (n = 30,133, 56 ± 17, 54%). ECG2HF-Subtype consistently discriminated incident HFrEF (MGH 10-year AUROC 0.89 [0.86-0.91], 10-year AP: 0.11 [95%CI 0.09-0.15], cumulative incidence 1.73%; BWH: 0.88 [0.87-0.89], 0.13 [0.11-0.14], 2.23%; BIDMC: 0.88 [0.86-0.89], 0.14 [0.12-0.17], 2.19%), and HFpEF events (MGH 10-year AUROC 0.83 [0.81-0.85], 10-year AP: 0.10 [95% CI 0.09-0.13], cumulative incidence 3.17%; BWH: 0.79 [0.77-0.80], 0.06 [0.06-0.07], 2.75%; BIDMC: 0.81 [0.80-0.82], 0.10 [0.09-0.12], 3.49%), with consistently greater AUROC vs age and sex. When compared to PREVENT-HF variables, ECG2HF-Subtypes had favorable discrimination of HFrEF (eg, 10-year AUROC in MGH+BWH 0.86 [0.84-0.88] vs 0.78 [0.76-0.80]), and modestly lower for HFpEF (10-year AUROC in MGH+BWH 0.78 [0.76-0.80] vs 0.81 [0.79-0.82]).
By predicting the risk of HFpEF and HFrEF, AI-enabled ECG analysis may enable targeted prevention to reduce HF-related morbidity.

PMID:
43009242
Bibliographic data and abstract were imported from PubMed on 11 Nov 2026.

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