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大血管闭塞性脑卒中急救预测量表研究进展
刘宇嘉1,2,陈玮琪3,王伊龙3*
0
(1. 北京航空航天大学生物与医学工程学院, 北京 100191;
2. 解放军总医院医院管理研究所所长办公室, 北京 100036;
3. 首都医科大学附属北京天坛医院神经内科, 北京 100070
*通信作者)
摘要:
脑卒中是我国患者死亡的首要原因,其中大血管闭塞性脑卒中是病死率、致残率最高的缺血性脑卒中类型。指南推荐应尽早开展静脉溶栓、桥接治疗或血管内治疗以挽救缺血的脑组织。因此,及早识别大血管闭塞性脑卒中对院前急救及转诊至关重要。目前已研发出众多院前预测大血管闭塞性脑卒中的评估工具,如 PAST 量表、ELVO 量表、CPSSS、EMSA 量表、LARIO 量表等。本文对近几年研发的用于院前预测大血管闭塞性脑卒中的评估工具及其预测效能进行综述,以更好地应用于临床实践。
关键词:  脑卒中  大血管闭塞  量表  预测效能
DOI:10.16781/j.CN31-2187/R.20230622
投稿时间:2023-11-12修订日期:2024-02-20
基金项目:国家自然科学基金杰出青年科学基金(81825007).
First-aid prediction scale for large vessel occlusion stroke: research progress
LIU Yujia1,2,CHEN Weiqi3,WANG Yilong3*
(1. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China;
2. Office of the Director, Hospital Management Research Institute, Chinese PLA General Hospital, Beijing 100036, China;
3. Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
*Corresponding author)
Abstract:
Stroke is the leading cause of death in China, and large vessel occlusion (LVO) stroke is the type of ischemic stroke with the highest mortality and disability rate. Clinical guidelines recommend early interventions through intravenous thrombolysis, bridging therapy, or endovascular treatment to salvage ischemic brain tissue. Therefore, early identification of LVO stroke is of vital importance for prehospital emergency and referrals. Currently, numerous assessment tools for predicting LVO stroke have been developed, such as the prehospital acute stroke triage (PAST) scale, emergent large vessel occlusion screen (ELVO) scale, Cincinnati prehospital stroke severity scale (CPSSS), emergency medical stroke assessment (EMSA) scale and large artery intracranial occlusion stroke (LARIO) scale. This article focuses on the content and prediction efficiency of these assessment tools developed in recent years for prehospital prediction of LVO stroke, aiming to facilitate their applications in clinical practice.
Key words:  stroke  large vessel occlusion  scale  prediction efficiency