使用临床数据将ESUS患者重新分类为大动脉动脉样硬化或心血管栓塞性中风机制
Lauren Klein-Murrey1, David L Tirschwell1, Daniel S Hippe2
1Department of Neurology, Harborview Medical Center, University of Washington School of Medicine, 325 Ninth Avenue, Seattle, WA, USA.
Journal of neurology
|December 21, 2024
概括
机器学习可以使用临床数据将源不明的栓塞性中风 (ESUS) 重归类为心血管栓塞性或大动脉动脉样硬化等类别. 这种方法有助于确定中风的原因,并指导未来的心血管事件的预防.
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:
- 源不明的栓塞性中风 (ESUS) 占缺血性中风的10-25%.
- 精确的ESUS分类对于有针对性的治疗和二次预防至关重要.
研究的目的:
- 确定机器学习 (ML) 是否可以将ESUS重新分类为心血管 (CE) 或大动脉动脉样硬化 (LAA) 类别.
- 用传统的临床数据来开发ML模型.
主要方法:
- 追溯收集CE和LAA中风患者的临床,成像,心脏和血清数据.
- 在单变量分析中使用p < 0.2的因子开发ML预测工具.
- 将ML工具应用于ESUS案例,重新分类的概率>=75%.
主要成果:
- 在区分CE和LAA方面,ML模型实现了0.88的c统计值.
- 对于CE的关键预测指标包括左心房体积指数和BNP.
- 对LAA的关键预测因素包括非化狭窄症.
- 该模型重新分类了40%的ESUS案件 (11到CE,9到LAA).
- 心血管事件在1/4的CE重新分类和3/16模两可的ESUS病例中观察到.
结论:
- 使用标准临床生物标志物的机器学习工具可能会重新分类ESUS患者.
- 这种重新分类有助于为更好的中风管理分配病因.
- 进一步的研究可以改进ML模型以改进ESUS亚型.
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