中国卒中杂志2025，Vol.20Issue(7)：809-818,10.DOI:10.3969/j.issn.1673-5765.2025.07.003
急性卒中院前诊断识别研究进展
Research Progress in Prehospital Diagnosis and Identification of Acute Stroke
摘要
Abstract
Stroke is the second leading cause of death and the primary cause of disability worldwide,and its treatment outcomes are highly dependent on early recognition and timely therapeutic intervention.Currently,prehospital stroke prediction tools mainly include three categories:traditional scales,machine learning models,and biomarkers,each with distinct characteristics but significant limitations.Traditional scales(such as FAST and Cincinnati prehospital stroke scale),due to their operational simplicity,serve as primary screening tools at the grassroots level but are insufficient in identifying posterior circulation strokes.Specialized scales for large vessel occlusion(such as the Los Angeles motor scale and rapid arterial occlusion evaluation scale)show relatively high specificity but still face challenges with high false positive rates.Machine learning models(such as extreme gradient boosting and random forest)exhibit superior performance in stroke subtyping and large vessel occlusion prediction,but are constrained by insufficient data dimensions and clinical translation barriers.Biomarkers(such as glial fibrillary acidic protein and S100 calcium-binding protein B)exhibit significant potential in differentiating stroke subtypes,but their prehospital application remains limited due to complex detection techniques and lack of standardization.Future advancements require optimized scale design,integration of multimodal data,development of portable detection technologies,and enhanced prehospital to in-hospital coordination to improve the accuracy of prehospital stroke prediction,so as to guide early prehospital intervention and improve patient prognosis.关键词
卒中预测/院前急救/大血管闭塞/机器学习模型/生物标志物Key words
Stroke prediction/Prehospital emergency care/Large vessel occlusion/Machine learning model/Biomarker分类
医药卫生引用本文复制引用
王荣,何松,岗瑞娟,王琪,唐宇杰,刘飞凤,杨杰,李刚,林亚鹏..急性卒中院前诊断识别研究进展[J].中国卒中杂志,2025,20(7):809-818,10.基金项目
国家自然科学基金面上项目(82171295)四川省科技计划项目重点研发计划(2023YFS0042) （82171295）
