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院前量表在救护车环境中预测大血管前闭塞的比较。

Comparison of Prehospital Scales for Predicting Large Anterior Vessel Occlusion in the Ambulance Setting.

机构信息

Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands.

Department of Neurology, Haaglanden Medical Center, The Hague, the Netherlands.

出版信息

JAMA Neurol. 2021 Feb 1;78(2):157-164. doi: 10.1001/jamaneurol.2020.4418.

Abstract

IMPORTANCE

The efficacy of endovascular thrombectomy (EVT) for symptomatic large anterior vessel occlusion (sLAVO) sharply decreases with time. Because EVT is restricted to comprehensive stroke centers, prehospital triage of patients with acute stroke codes for sLAVO is crucial, and although several prediction scales are already in use, external validation, head-to-head comparison, and feasibility data are lacking.

OBJECTIVE

To conduct external validation and head-to-head comparisons of 7 sLAVO prediction scales in the emergency medical service (EMS) setting and to assess scale feasibility by EMS paramedics.

DESIGN, SETTING, AND PARTICIPANTS: This prospective cohort study was conducted between July 2018 and October 2019 in a large urban center in the Netherlands with a population of approximately 2 million people and included 2 EMSs, 3 comprehensive stroke centers, and 4 primary stroke centers. Participants were consecutive patients aged 18 years or older for whom an EMS-initiated acute stroke code was activated. Of 2812 acute stroke codes, 805 (28.6%) were excluded, because no application was used or no clinical data were available, leaving 2007 patients included in the analyses.

EXPOSURES

Applications with clinical observations filled in by EMS paramedics for each acute stroke code enabling reconstruction of the following 7 prediction scales: Los Angeles Motor Scale (LAMS); Rapid Arterial Occlusion Evaluation (RACE); Cincinnati Stroke Triage Assessment Tool; Prehospital Acute Stroke Severity (PASS); gaze-face-arm-speech-time; Field Assessment Stroke Triage for Emergency Destination; and gaze, facial asymmetry, level of consciousness, extinction/inattention.

MAIN OUTCOMES AND MEASURES

Planned primary and secondary outcomes were sLAVO and feasibility rates (ie, the proportion of acute stroke codes for which the prehospital scale could be reconstructed). Predictive performance measures included accuracy, sensitivity, specificity, the Youden index, and predictive values.

RESULTS

Of 2007 patients who received acute stroke codes (mean [SD] age, 71.1 [14.9] years; 1021 [50.9%] male), 158 (7.9%) had sLAVO. Accuracy of the scales ranged from 0.79 to 0.89, with LAMS and RACE scales yielding the highest scores. Sensitivity of the scales ranged from 38% to 62%, and specificity from 80% to 93%. Scale feasibility rates ranged from 78% to 88%, with the highest rate for the PASS scale.

CONCLUSIONS AND RELEVANCE

This study found that all 7 prediction scales had good accuracy, high specificity, and low sensitivity, with LAMS and RACE being the highest scoring scales. Feasibility rates ranged between 78% and 88% and should be taken into account before implementing a scale.

摘要

重要性

血管内血栓切除术(EVT)治疗症状性大血管前闭塞(sLAVO)的疗效随时间急剧下降。由于 EVT 仅限于综合卒中中心,因此对急性卒中代码为 sLAVO 的患者进行院前分诊至关重要,尽管已经有几个预测量表在使用,但缺乏外部验证、头对头比较和可行性数据。

目的

在急诊医疗服务(EMS)环境中对 7 种 sLAVO 预测量表进行外部验证和头对头比较,并由 EMS 护理人员评估量表的可行性。

设计、地点和参与者:这是一项前瞻性队列研究,于 2018 年 7 月至 2019 年 10 月在荷兰一个拥有约 200 万人口的大城市中心进行,包括 2 个 EMS、3 个综合卒中中心和 4 个初级卒中中心。研究对象为年龄在 18 岁或以上的连续患者,这些患者的 EMS 发起了急性卒中代码。在 2812 个急性卒中代码中,有 805 个(28.6%)被排除在外,因为没有应用程序或没有临床数据可用,剩下的 2007 名患者被纳入分析。

暴露因素

使用临床观察由 EMS 护理人员填写的应用程序,对每个急性卒中代码进行重建,从而可以重建以下 7 种预测量表:洛杉矶运动量表(LAMS);快速动脉闭塞评估(RACE);辛辛那提卒中分诊评估工具;院前急性卒中严重程度(PASS);凝视-面部-手臂-言语-时间;现场评估卒中分诊用于紧急目的地;凝视、面部不对称、意识水平、消失/注意力不集中。

主要结局和测量指标

计划的主要和次要结局为 sLAVO 和可行性率(即,可在院前重建预测量表的急性卒中代码的比例)。预测性能指标包括准确性、敏感性、特异性、Youden 指数和预测值。

结果

在接受急性卒中代码的 2007 名患者中(平均[标准差]年龄,71.1[14.9]岁;1021[50.9%]为男性),有 158 名(7.9%)患有 sLAVO。这些量表的准确性范围为 0.79 至 0.89,LAMS 和 RACE 量表得分最高。这些量表的敏感性范围为 38%至 62%,特异性范围为 80%至 93%。量表的可行性率范围为 78%至 88%,其中 PASS 量表的可行性率最高。

结论和相关性

本研究发现,所有 7 种预测量表的准确性均较好,特异性和敏感性均较高,其中 LAMS 和 RACE 量表得分最高。可行性率在 78%至 88%之间,在实施量表之前应考虑这些因素。

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