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改良院前急性卒中严重程度评分(mPASS)预测急诊大动脉闭塞。

Modified Prehospital Acute Stroke Severity (mPASS) Scale to Predict Emergent Large Arterial Occlusion.

机构信息

Department of Neurology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.

Department of Neurology, Hangzhou Geriatric Hospital, Hangzhou, China.

出版信息

Biomed Res Int. 2021 Jul 19;2021:5568696. doi: 10.1155/2021/5568696. eCollection 2021.

Abstract

INTRODUCTION

To date, identifying emergent large vessel occlusion (ELVO) patients in the prehospital stage is important but still challenging. In this present study, we aimed to design a modified prehospital acute stroke severity (mPASS) scale to identify ELVO patients and compared the scale to the PASS scale which has been published.

METHODS

We retrospectively evaluated a consecutive cohort of acute ischemic stroke (AIS) in our stroke unit who visited the emergercy department. These patients underwent CT angiography (CTA), MR angiography (MRA), or digital subtraction angiography (DSA) at admission. The mPASS scale was calculated based on the National Institutes of Health Stroke Scale (NIHSS) items retrospectively, including the level of consciousness commands, gaze, arm weakness, and aphasia/dysarthria. Receiver operating characteristic (ROC) analysis was used to obtain the area under the curve (AUC) of the mPASS scale, NIHSS, and PASS scale. -statistics was used to compare the AUC of the mPASS scale to the NIHSS and PASS scale.

RESULTS

A total of 382 AIS patients were enrolled. The AUC and specificity of the mPASS scale (0.92, 84.4) were all higher than those of the PASS scale. Cortical symptoms such as gaze palsy and consciousness disorder were more specific indicators for ELVO than motor deficits.

CONCLUSIONS

The mPASS scale had a better discrimination for identifying ELVO than the PASS scale in our retrospective cohort. It might predict ELVO in an effective and simple way for paramedics in the prehospital triage stage or emergency stage. Moreover, cortical symptoms might have relatively high specificities to predict ELVO on their own.

摘要

简介

迄今为止,在院前阶段识别出紧急大血管闭塞(ELVO)患者很重要,但仍然具有挑战性。在本研究中,我们旨在设计一种改良的院前急性卒中严重程度(mPASS)量表来识别 ELVO 患者,并将其与已发表的 PASS 量表进行比较。

方法

我们回顾性评估了我院卒中单元连续收治的急性缺血性卒中(AIS)患者,这些患者在入院时接受了 CT 血管造影(CTA)、磁共振血管造影(MRA)或数字减影血管造影(DSA)。mPASS 量表是基于 NIHSS 项目(包括意识命令、凝视、手臂无力和失语症/构音障碍)进行回顾性计算的。采用受试者工作特征(ROC)分析获得 mPASS 量表、NIHSS 和 PASS 量表的曲线下面积(AUC)。使用卡方检验(-statistics)比较 mPASS 量表与 NIHSS 和 PASS 量表的 AUC。

结果

共纳入 382 例 AIS 患者。mPASS 量表的 AUC 和特异性(0.92、84.4)均高于 PASS 量表。与运动缺陷相比,凝视麻痹和意识障碍等皮质症状是 ELVO 的更特异指标。

结论

在我们的回顾性队列中,mPASS 量表在识别 ELVO 方面优于 PASS 量表。它可能为院前分诊或急诊阶段的护理人员提供一种有效且简单的方法来预测 ELVO。此外,皮质症状本身可能具有相对较高的特异性来预测 ELVO。

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