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

ABSTRACT:

Aims

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.

Methods and results

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

SUBMITTER: Hong D 

PROVIDER: S-EPMC12450526 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature