Emerging Detection Techniques for Large Vessel Occlusion Stroke: A Scoping Review.
Emerging Detection Techniques for Large Vessel Occlusion Stroke: A Scoping Review.
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DOI：
10.3389/fneur.2021.780324
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发表时间：
2021
影响因子：
3.4
通讯作者：
Chung EML
中科院分区：
文献类型：
--
作者：
Nicholls JK;Ince J;Minhas JS;Chung EML
背景：大血管闭塞（LVO）是大脑近端大动脉的阻塞，当包括A2和P2段（来自大脑前后动脉）时，可占急性缺血性卒中（AIS）的46%。它是至关重要的，及时认识到LVO提供及时和有效的急性脑卒中管理。这篇综述的目的是扩大最近的文献，以确定新的新兴检测技术的LVO。在本综述中，常用的美国国立卫生研究院卒中量表（NIHSS）作为一个很好的比较指标，在临界值≥11时，据报道其对LVO的敏感性为86%，特异性为60%。方法：系统检索四个电子数据库（Medline via OVID， CINAHL， Scopus和Web of Science）和使用OpenGrey的灰色文献，检索2015年至2021年报告的关于LVO检测方法发展的已发表文献。搜索协议在开放科学框架（10.17605/OSF.IO/A98KN）上发布。两名独立研究人员筛选了文章的标题、摘要和全文，评估其入选资格。结果：检索确定了5,082篇文章，其中筛选了2,265篇文章以评估其资格。62项研究继续进行全文筛选。LVO检测技术分为5组：卒中量表（n = 30）、成像和生理方法（n = 15）、算法和机器学习方法（n = 9）、身体症状（n = 5）和生物标志物（n = 3）。结论：本综述探讨了LVO检测方法的新颖和进步的文献。这篇综述的结果强调了LVO检测技术，如中风量表和生物标志物，具有良好的灵敏度和特异性，同时也显示了支持现有LVO验证方法的进步，如神经成像。
Background: Large vessel occlusion (LVO) is the obstruction of large, proximal cerebral arteries and can account for up to 46% of acute ischaemic stroke (AIS) when both the A2 and P2 segments are included (from the anterior and posterior cerebral arteries). It is of paramount importance that LVO is promptly recognised to provide timely and effective acute stroke management. This review aims to scope recent literature to identify new emerging detection techniques for LVO. As a good comparator throughout this review, the commonly used National Institutes of Health Stroke Scale (NIHSS), at a cut-off of ≥11, has been reported to have a sensitivity of 86% and a specificity of 60% for LVO. Methods: Four electronic databases (Medline via OVID, CINAHL, Scopus, and Web of Science), and grey literature using OpenGrey, were systematically searched for published literature investigating developments in detection methods for LVO, reported from 2015 to 2021. The protocol for the search was published with the Open Science Framework (10.17605/OSF.IO/A98KN). Two independent researchers screened the titles, abstracts, and full texts of the articles, assessing their eligibility for inclusion. Results: The search identified 5,082 articles, in which 2,265 articles were screened to assess their eligibility. Sixty-two studies remained following full-text screening. LVO detection techniques were categorised into 5 groups: stroke scales (n = 30), imaging and physiological methods (n = 15), algorithmic and machine learning approaches (n = 9), physical symptoms (n = 5), and biomarkers (n = 3). Conclusions: This scoping review has explored literature on novel and advancements in pre-existing detection methods for LVO. The results of this review highlight LVO detection techniques, such as stroke scales and biomarkers, with good sensitivity and specificity performance, whilst also showing advancements to support existing LVO confirmatory methods, such as neuroimaging.
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DOI：
10.1016/j.jstrokecerebrovasdis.2017.10.018
发表时间：
2018-03-01
影响因子：
2.5
作者：
Gropen, Toby I.;Boehme, Amelia;Levine, Steven R.
通讯作者：
Levine, Steven R.
DOI：
10.1016/j.jstrokecerebrovasdis.2016.08.032
发表时间：
2017-01-01
影响因子：
2.5
作者：
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Perez de la Ossa, Natalia
影响因子：
48
作者：
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通讯作者：
Roozenbeek, Bob
DOI：
10.1016/j.jstrokecerebrovasdis.2017.12.035
发表时间：
2018-06-01
影响因子：
2.5
作者：
Attenhofer, Kevin S.;Hailey, Lovella;Meyer, Brett C.
通讯作者：
Meyer, Brett C.
影响因子：
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作者：
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通讯作者：
Fiorella, David