脉冲波速率的估计与患有缺血性中风的危急病患者的全因死亡率之间的关联:基于机器学习的回顾性队列研究和预测模型建立
Shuangmei Zhao1, Chang Zhu2, Yu Guo3
1Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
BMC neurology
|September 27, 2025
概括
较高的估计脉冲波速度 (ePWV) 表明严重病情缺血性中风 (IS) 患者的死亡风险增加. 使用ePWV的机器学习模型与传统得分相比,提高了风险预测.
科学领域:
- 心脏病学 心脏病学
- 神经学 神经学
- 医疗信息学 医疗信息学
背景情况:
- 估计的脉冲波速度 (ePWV) 是动脉硬和死亡风险的验证标记.
- 在危急性缺血性中风 (IS) 患者中,ePWV的预后价值尚未得到充分确立.
- 本研究调查了ePWV与这种特定患者群体的不良结果之间的关联.
研究的目的:
- 评估EPWV水平与严重病情IS患者的死亡率之间的关系.
- 将包含ePWV的机器学习模型的预测性能与传统的临床风险得分进行比较.
- 评估ePWV对短期和长期死亡的预后价值.
主要方法:
- 使用MIMIC-IV数据库 (n=1,337) 的回顾性队列研究,按ePWV四分位数分层.
- 卡普兰-梅尔曲线,考克斯模型和受限立方线分析了ePWV和全因死亡率.
- 四个机器学习算法 (LR,RF,XGBoost,NB) 和SHAP分析被用于风险建模和可解释性.
主要成果:
- 较高的ePWV四分位数与30天,90天和1年死亡率显著增加有关.
- 限制性立方线分析显示,ePWV与死亡风险之间存在剂量依赖的关系.
- 确定了ePWV作为一个关键预测因素;XGBoost在90天和1年死亡率预测方面表现出卓越的表现.
结论:
- 升高的ePWV是严重病情的IS患者死亡率的重要预后指标.
- 集成ePWV的机器学习模型为急性中风管理提供了增强的风险分层能力.
- ePWV具有改善IS临床决策和患者结果的潜力.
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