在手术前预测外科手术期间中风的紧机器学习模型:一项回顾性队列研究
Mi-Young Oh1, Hee-Soo Kim2, Young Mi Jung3
1Department of Neurology, Sejong General Hospital, Bucheon-si, Republic of Korea.
Scientific reports
|November 19, 2025
概括
一个新的机器学习 (ML) 模型准确地预测了外科手术期间的中风风险,超过了现有的心血管分数. 这种先进的工具通过提高中风风险分层的准确性和可用性来提高患者的安全性.
科学领域:
- 神经外科 神经外科
- 心脏病学 心脏病学
- 人工智能的人工智能
背景情况:
- 术后中风是一种影响患者结果的重大并发症.
- 目前用于预测外科手术期间中风风险的方法既不准确,也不适合临床使用.
研究的目的:
- 开发一种机器学习 (ML) 模型,以提高预测外科手术期间中风的准确性和可用性.
- 将ML模型的性能与已确定的心血管风险得分进行比较.
主要方法:
- 利用了36502名患者的内部验证和404名患者的外部验证数据.
- 开发和比较各种ML模型,包括一个具有前10个特性的紧型模型.
- 在手术后30天内定义的术后中风是缺血性脑梗塞.
主要成果:
- 基于CatBoost的ML模型在高风险患者中表现出优越的区分能力.
- 在外部验证中,ML模型显著优于心血管分数 (修订心脏指数和CHA2DS2VASc) (AUC 0.867与0.528和0.706对比).
- 一个紧的ML模型也显示出更好的性能 (AUC 0.875).
结论:
- 开发的基于ML的模型为外科手术期间的中风预测提供了更高的准确性和临床可用性.
- 这种新的方法有可能提高患者的安全性,并优化手术决策.
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