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多模式深度学习预测非心脏手术后的主要心脏和脑血管不良事件

Hyun-Kyu Yoon^1,2, Jang Ho Ahn¹, Byeol Yi Kim¹

¹Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, South Korea.

International journal of surgery (London, England)

|August 27, 2025

概括

一个新的深度学习模型使用心电图和最小的临床数据准确预测非心脏手术后的主要心血管和脑血管不良事件 (MACCE). 这种方法改善了风险分层,从而改善了患者的治疗结果.

科学领域:

心脏病学
医疗信息学
医疗保健中的人工智能

背景情况:

非心脏手术后的主要心血管和脑血管不良事件 (MACCE) 显著增加了发病率,死亡率和医疗费用.
准确和快速的风险预测对于有效的术后管理至关重要.
目前的风险指数可能无法完全捕捉个体患者的风险概况.

研究的目的:

开发和验证一种多模式深度学习模型,用于预测非心脏手术后的30天MACCE.
整合人口数据,ICD-10程序代码和原始的12导电图波形.
将模型的表现与已建立的风险指数进行比较.

主要方法:

在高等学术中心进行回顾性队列研究 (2006-2020年).
基于变压器的深度神经网络用于心电图信号处理.
渐变增强机 (GBM) 集成了心电图特征,人口统计和ICD-10代码.

主要成果:

多模式 GBM 模型实现了 0. 902 的 AUROC,超过了基线 GBM (0. 842),修订心脏风险指数 (0. 813) 和 ASA 类 (0. 759).
该模型在预测30天MACCE方面表现优异.
这项研究包括165577名患者,30天MACCE的发病率为0. 6%.

结论:

综合原始心电图和最小临床数据的多式深度学习模型提供了优异的30天MACCE风险预测.
这种方法增强了外科手术风险分层.
该模型的最低数据要求支持广泛的临床采用.

关键词:

深度学习心电图其他非心脏手术术后并发症预测算法

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