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单线心电图的人工智能分析预测了长期的临床结果

Abdullah Alrumayh^1,2,3, Patrik Bächtiger^1,2, Arunashis Sau^1,2

¹National Heart and Lung Institute, Imperial College London, Du Cane Road, London W12 0HS, UK.

European heart journal. Digital health

|July 24, 2025

概括

人工智能电心电图 (AI-ECG) 可以预测主要不良心血管事件 (MACE) 和死亡风险,即使在没有减少左心室缩功能障碍 (LVSD) 的患者中也是如此. 这种人工智能工具显示了风险分层的潜力.

科学领域:

心脏病学心脏病学
人工智能在医学中的应用
预测分析是一种预测分析.

背景情况:

应用于单导电心电图 (AI-ECG) 的人工智能 (AI) 可以检测左心室缩功能障碍 (LVSD),定义为左心室喷射率 (LVEF) ≤40%.
在预测未来的心血管事件中AI-ECG的预后价值独立于LVSD状态并未得到充分确立.

研究的目的:

确定AI-ECG能否预测两年内发生重大心血管不良事件 (MACE) 和全因死亡率的风险.
评估这种预测能力是否独立于通过心声学测量的实际左心室喷射率 (LVEF).

主要方法:

一组1007名患者接受了常规心电回声学和AI-ECG分析的同时单线心电图记录,并被跟踪了两年.
AI-ECG将患者分类为预测的LVEF≤40% ("阳性") 或>40% ("阴性").
考克斯回归分析对AI-ECG组之间的MACE和全因死亡率进行了比较,其中包括一组具有心声回声LVEF≥50%的子组.

主要成果:

与"负面"预测的患者相比,具有"积极"AI-ECG预测 (LVEF ≤ 40%) 的患者的MACE率 (34.2%对11.9%) 和全因死亡率 (23%对9.6%) 显着更高.
在对混因子进行调整后,AI-ECG阳性和不良结果之间的关联仍然显著 (MACE的aHR为1.93;死亡率的aHR为1.56).
在正常LVEF (≥50%) 的患者中",假阳性"AI-ECG预测LVEF≤40%与MACE (27.2%对11.9%) 和死亡率 (20.4%对9.6%) 的增加有关.

结论:

设计用于检测低LVEF的AI-ECG算法可以识别患有MACE和全因死亡风险增加的患者,无论其实际LVEF如何.
AI-ECG显示出作为心血管事件的临床风险分层工具的潜力.
需要进一步的验证研究来证实其临床实用性.

关键词:

人工智能的人工智能心脏病学心脏病学数字健康数字健康公共卫生公共卫生

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