人工智能增强心电图对急诊室患者的预后意义

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Updated: Sep 11, 2025

10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

907

Yu-Te Su¹, Sy-Jou Chen^1,2, Chin Lin^3,4,5,6

¹Department of Emergency Medicine, Tri-Service General Hospital, National Defense Medical University, Taipei 11490, Taiwan.

Diagnostics (Basel, Switzerland)

|August 14, 2025

概括

人工智能 (AI) 对连续心电图 (ECG) 的分析可以改善放电后风险预测. 在出院前的后续心电图提供了比急诊室患者的初始心电图更好的死亡风险分层.

科学领域:

心脏病学心脏病学
人工智能的人工智能
医疗信息学医疗信息学

背景情况:

使用人工智能 (AI) 支持的心电图 (ECG) 分析的客观风险分层可以帮助临床决策.
序列心电图的预后价值,特别是出院前后续心电图,用于预测患者的结果仍然未得到充分探索.
人工智能预测的心电图风险得分的动态变化可能会提高出院后不良事件的预测.

研究的目的:

评估AI支持的电脑心电图风险评分的预后效用,这些评分来自连续的电脑心电图.
确定AI-ECG风险评分的动态变化是否有助于改善释放后结果预测.
评估退院前随访心电图在急诊室 (ED) 患者风险分层中的价值.

主要方法:

11,508次ED访问的回顾性队列研究,直接释放和两个ECG.
基于初始和后续心电图,使用人工智能支持的心电图模型来分层死亡风险 (低,中,高).
曲线下的面积 (AUC) 为预测性能计算;卡普兰-梅尔曲线和考克斯比例危险模型分析了90天死亡率.

主要成果:

人工智能支持的ECG风险预测模型显示了初始和后续ECG之间的动态风险过渡.
预测死亡风险的AUC为初始心电图的78.6%和随访心电图的83.3%.
在从低风险过渡到中高风险的患者中观察到90天死亡率的显著增加 (HR:6.01).

结论:

与ED患者的初始心电图相比,AI启用的预出院心电图提供了优越的死亡风险分层.
在出院时被确定为中等或高风险的患者需要更密切的临床评估.
在动态的ED环境中,AI-ECG作为一种有价值的工具来支持临床判断,并增强安全的出院决策.

关键词:

人工智能的人工智能是人工智能.电心电图 (ECG) 是一种心电图.紧急情况部门的急救部门.死亡率死亡率

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