基于临床大数据的急性缺血性中风 (AIS) 一年复发预测模型
Wenle Li1,2, Zhendong Ding3, Liangqun Rong2,4
1The State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China.
Heliyon
|June 17, 2024
概括
这项研究开发了一个名图,以预测急性缺血性中风 (AIS) 在一年内复发. 该模型结合了诸如同类半氨酸和中风严重程度等因素,显示出高准确性和医疗保健专业人员的临床实用性.
科学领域:
- 神经学 神经学
- 心血管医学 心血管医学
- 生物统计学 生物统计学
背景情况:
- 急性缺血性中风 (AIS) 构成复发的重大风险,需要准确的预测模型.
- 早期识别患有AIS复发高风险的患者对于及时干预和改善结果至关重要.
研究的目的:
- 开发和验证一个名目录预测模型,用于预测AIS在一年内复发的情况.
- 确定与AIS复发相关的独立风险因素,以便纳入预测模型.
主要方法:
- 分析了645名AIS患者的队列,收集了有关人口统计,风险因素,实验室测试,病因类型和MRI特征的数据.
- 考克斯回归分析被用来确定名图的显著预测因子.
- 用接收器操作特征 (ROC) 曲线,决策曲线分析,校准曲线和C指数来评估模型性能.
主要成果:
- AIS复发的关键独立风险因素包括半球的一侧,同型半素 (HCY),C反应蛋白 (CRP) 和中风严重程度 (SS).
- 开发的诺米图实现了0.872的高C指数和0.900的ROC曲线下的面积,用于一年的复发.
- 该模型在校准和决策曲线分析中表现良好,其复发风险截止分数为1.73.
结论:
- 开发的诺米图是预测一年内AIS复发的高性能工具.
- 这个名图可以作为医疗保健专业人员在管理AIS患者和指导治疗策略方面提供有价值的临床辅助.
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