European heart journal. Digital health · 2026
Associations between echocardiographic traits and artificial intelligence-enabled electrocardiography predictions of heart failure.
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Stenhede Elias, Orstad Eivind Bjørkan, Omland Torbjørn, Schirmer Henrik, Ranjbar Arian
Аннотация
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 ρ quantified associations between echocardiographic parameters and the AI-ECG HF prediction score. Subgroup analyses were performed by sex and LVEF. External replication of shared echocardiographic associations included 36 286 ECG-echocardiography pairs from Columbia University Irving Medical Center. Global longitudinal strain (GLS) showed the strongest correlation (ρ = 0.57), followed by mitral annular plane systolic excursion (MAPSE; ρ = -0.49) and LVEF (ρ = -0.45). In patients with LVEF ≥50%, correlations remained substantial for GLS, MAPSE, and diastolic-related parameters. Volumetric left ventricular indices correlated less strongly in women, whereas diastolic indices showed stronger correlations in women than in men.
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.
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 ρ quantified associations between echocardiographic parameters and the AI-ECG HF prediction score. Subgroup analyses were performed by sex and LVEF. External replication of shared echocardiographic associations included 36 286 ECG-echocardiography pairs from Columbia University Irving Medical Center. Global longitudinal strain (GLS) showed the strongest correlation (ρ = 0.57), followed by mitral annular plane systolic excursion (MAPSE; ρ = -0.49) and LVEF (ρ = -0.45). In patients with LVEF ≥50%, correlations remained substantial for GLS, MAPSE, and diastolic-related parameters. Volumetric left ventricular indices correlated less strongly in women, whereas diastolic indices showed stronger correlations in women than in men.
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.