AI心电图预测心衰风险与超声心动图指标高度相关，尤其反映心肌收缩功能。
Associations between echocardiographic traits and AI-ECG predictions of heart failure
- 用人工智能分析心电图，关联超声心动图指标判断心衰
- 全球纵向应变（GLS）相关性最强（ρ=0.57），其次为瓣环运动幅度
- 对射血分数正常患者的心衰风险预测有重要参考价值
人工智能心电图（AI-ECG）可检测心衰，包括不依赖左室射血分数（LVEF）的类型，但其预测背后的心脏表型尚不明确。本研究回顾性分析了2023年1月1日至2025年6月1日期间在阿克什斯大学医院接受心电图与超声心动图检查（间隔≤3天）的8147名患者数据。使用已验证的AI-ECG心衰预测模型处理所有心电图，通过斯皮尔曼等级相关系数（ρ）评估超声参数与AI-ECG风险的相关性。分性别及LVEF亚组分析显示，全球纵向应变（GLS）相关性最强（ρ=0.57），其次为二尖瓣环平面收缩运动（MAPSE）（ρ=-0.49）和LVEF（ρ=-0.45）。在LVEF>50%患者中，GLS、MAPSE及舒张功能相关参数仍呈显著相关。女性患者中，容积相关指标相关性较弱，而舒张功能指标相关性更强。外部验证包含哥伦比亚大学欧文医学中心的36,286对心电图-超声配对数据。结果表明，AI-ECG心衰风险预测主要与收缩功能指标（尤其是GLS）一致，同时也能捕捉射血分数保留患者中的舒张异常。该方法有助于提升模型临床可解释性，并指导模型优化。
原文摘要 · Abstract (English)
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 model predictions remain unclear. We therefore investigated whether AI-ECG-predicted HF risk aligns with established echocardiographic measures of myocardial dysfunction, remodelling, and filling pressures. We retrospectively analysed ECG and echocardiography data from 8147 patients who underwent both examinations within three days at Akershus University Hospital between 1 January 2023 and 1 June 2025. A previously validated AI-ECG model for HF detection was applied to all ECGs. Spearman's rank correlation $ρ$ quantified associations between echocardiographic parameters and AI-ECG risk. Subgroup analyses were performed by sex and left ventricular ejection fraction (LVEF). External validation 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. Physiological validation showed that AI-ECG HF risk predictions align primarily with measures of systolic function, particularly global longitudinal strain, while also capturing diastolic-related abnormalities in patients with preserved LVEF. This approach may improve clinical interpretability and identify opportunities for model refinement.
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