机器学习驱动的风险预测延迟大脑缺血症在动脉瘤下关节出血后使用外周炎症标志物
Yuanyuan Liu1, Chengchen Li1, Honglin Wang2
1Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Frontiers in neurology
|December 29, 2025
概括
机器学习使用炎症标志物和临床数据准确预测神经瘤下关节出血 (aSAH) 后的延迟脑缺血 (DCI). 该工具有助于早期风险评估,以改善患者的治疗结果.
科学领域:
- 神经学 神经学
- 计算生物学 计算生物学
- 生物统计学 生物统计学
背景情况:
- 延迟大脑缺血症 (DCI) 是动脉瘤下arachnoid出血 (aSAH) 后死亡和残疾的主要原因.
- 系统性炎症在DCI的发病过程中起着关键作用,外周炎症标志物显示出预测潜力.
- 个别生物标志物具有有限的预测能力,需要像机器学习这样的综合方法.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于在aSAH后预测DCI风险.
- 整合各种炎症和临床变量,以改善个性化风险预测.
- 创建一个临床上可解释的,用于DCI风险的手术前决策支持工具.
主要方法:
- 对562名aSAH患者的回顾性分析.
- 使用Boruta算法进行特征选择.
- 开发和比较六个ML模型 (逻辑回归,神经网络,随机森林,SVM,GBM,XGBoost).
- 使用AUC,灵敏度,特异性,F1得分,校准曲线和DCA的性能评估.
主要成果:
- 神经网络模型实现了最佳性能 (AUC 0.826训练,0.808测试).
- 关键预测因素包括格拉斯哥昏迷量表 (GCS),亨特-赫斯等级,修改后的费舍尔分数,PNI,NAR,NLPR,CLR和前素.
- 在SHAP分析中,亨特-赫斯等级和公素被确定为最重要的贡献者.
结论:
- 使用常规数据开发了一种基于ML的强大DCI风险预测工具,用于aSAH后的DCI.
- 该模型表现出强大的区分和校准性能.
- 建议对临床转换进行进一步的前性多中心验证.
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