Artificial intelligence-enabled electrocardiogram model for predicting heart failure with preserved ejection fraction: a single-center study

Heart failure with preserved ejection fraction (HFpEF) is difficult to diagnose due to the lack of a definitive diagnostic marker; multiple tests are required, including advanced evaluations. This study aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model for predicting HFpEF.

This retrospective cohort study included patients from a single tertiary centre who underwent echocardiography, N-terminal prohormone of B-type natriuretic peptide measurement, and ECG within a defined timeframe. Patients were classified as HFpEF (HFA-PEFF score ≥5) or control (HFA-PEFF score <5). Patients were divided into training, validation, and test subsets at a 7:1:2 ratio for model development and validation. Using the collected ECGs, a convolutional neural network was trained to predict HFpEF; its performance was assessed using the area under the receiver operating characteristic curve (AUROC). Among the 13 081 patients included, 5795 (44.3%) were classified as HFpEF and 7286 (55.7%) were classified as control. The AI-enabled ECG model demonstrated good discriminative performance [AUROC 0.81; 95% confidence interval (CI) 0.79–0.82]. Subgroup analyses stratified by HFpEF risk factors confirmed consistent model performance. Prognostic evaluation revealed that patients with a positive AI-ECG classification experienced significantly worse outcomes relative to those with a negative classification, including higher risks of cardiac death (1.1% vs. 0.1%; hazard ratio 9.56; 95% CI 1.24–73.53; P = 0.030) and heart failure hospitalization (2.8% vs. 0.6%; hazard ratio 5.91; 95% CI 2.08–16.81; P = 0.001) at 5 year.

The AI-ECG model is a reliable tool for predicting HFpEF, as defined by the HFA-PEFF score, and effectively stratifies patients according to prognosis. Integration of this model into clinical practice may simplify and enhance the diagnostic process for HFpEF.