Associations between echocardiographic traits and artificial intelligence-enabled electrocardiography predictions of heart failure.

Aims: Artificial intelligence-enabled electrocardiography (AI-ECG) can detect heart failure (HF), including disease not captured by left ventricular ejection fraction (LVEF), but the cardiac phenotypes underlying AI-ECG HF prediction remain unclear. We therefore investigated whether an AI-ECG HF prediction score aligns with established echocardiographic measures of myocardial dysfunction, remodelling, and filling pressures. Methods and results: We retrospectively analysed ECG and echocardiography data from 8147 patients who underwent both examinations within 3 days at Akershus University Hospital between 1 January 2023 and 1 June 2025. A previously developed AI-ECG model, pragmatically trained using ICD-10 HF codes and N-terminal pro-B-type natriuretic peptide thresholds, was applied to all electrocardiograms. Spearman's rank correlation Conclusion: Echocardiographic trait characterization showed that the AI-ECG HF prediction score aligned primarily with measures of systolic function, particularly GLS, while also being associated with diastolic-related abnormalities in patients with preserved LVEF. This approach may inform future studies of model interpretability and refinement.