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AFAIS的二次预防:部署传统的回归,机器学习和深度学习模型来验证和更新CHA2DS2-VASc以实现90天的复发

Jenny Simon^1,2, Łukasz Kraiński³, Michał Karliński⁴

¹Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, 02-091 Warsaw, Poland.

Journal of clinical medicine

|October 29, 2025

概括

这项研究验证和更新了CHA2DS2-VASc评分,用于预测心房动相关的缺血性中风复发. 机器学习模型旨在改善AF患者的二次预防风险分层.

科学领域:

神经学神经学
心脏病学心脏病学
数据科学数据科学数据科学

背景情况:

心房动 (AF) 显著增加缺血性中风风险,二次预防患者的复发率更高.
目前的指导方针建议CHA2DS2-VASc评分用于口服抗凝药,但其对复发的预测价值尚未得到充分确立.
报告质量差以及机器学习 (ML) 的挑战凸显了需要透明,以验证为导向的临床预测规则 (CPR) 协议的需要.

研究的目的:

验证和更新CHA2DS2-VASc评分,用于预测AF相关急性缺血性中风 (AFAIS) 患者90天复发风险.
为AFAIS中二次预防制定改进的临床预测规则 (CPR).
加强对CPR开发和验证的系统和透明报告.

主要方法:

利用虚拟国际中风试验档案 (VISTA) 来自2763名AFAIS患者和7809名非AF AIS患者的数据.
通过使用歧视,校准和临床实用性指标,对90天复发和二次结果进行验证的CHA2DS2-VASc.
训练后勤回归,XGBoost和多层感知子 (MLP) 模型与嵌套交叉验证,为MLP模型采用转移学习.

主要成果:

模型将在使用AUC,校准曲线和F1分数的保留测试集上进行评估.
SHAP值将解释模型,并为更新的CPRs的构建提供信息.
将新CPR的性能与CHA2DS2-VASc和默认策略进行比较.

结论:

该研究旨在提供更新的CPR,以更好地捕捉AFAIS患者90天复发风险.
根据歧视,校准和临床效用来评估CPR将指导选择.
权衡分析将平衡开发的CPR的易用性和临床实用性.

关键词:

心房动是心房动的一种.临床预测规则是临床预测规则.口服抗凝剂是一种口服抗凝剂.二次预防二次预防.预测中风风险预测中风风险

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