Almutairi Areej, Wirawan Fadila, Lloyd Adam, Moullaali Tom, Clegg Gareth
Usher Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
Department of Emergency Medical Services, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia.
PLoS One. 2025 Aug 13;20(8):e0327653. doi: 10.1371/journal.pone.0327653. eCollection 2025.
Accurately identifying strokes during ambulance calls remains challenging, leading to low diagnostic accuracy and delays in dispatching appropriate services. Limited evidence exists regarding methods for improving call handlers' stroke recognition. This scoping review explores methods for enhancing stroke identification during emergency calls in ambulance control centres (ACCs).
We conducted a scoping review following the methodology of the Joanna Briggs Institute and adhered to PRISMA-ScR guidelines. A systematic search was performed across five databases: Embase, Medline, Scopus, Web of Science, and CINAHL, also grey literature sources, covering publications from January 1964 to July 2024. We included studies that examined methods to improve stroke identification during emergency calls in ACCs. To assess the effectiveness of these methods, eligible studies must evaluate at least one of the following outcomes: accuracy of stroke diagnosis, time to diagnosis, effectiveness of staff training, and acceptability of identification techniques. Two reviewers independently screened the studies, extracted the data, and conducted an inductive thematic analysis to identify common themes.
Of the 3,619 studies identified, seven met the inclusion criteria. Included studies focused on technology and algorithms (n = 3), training and educational programs (n = 2), and improved triage tools (n = 2) to enhance stroke identification during emergency calls to ACCs. Studies on technology and algorithms have reported increased stroke identification sensitivity and positive predictive value (PPV) when using new algorithms compared to standard protocols. Training programs have led to improved dispatcher sensitivity in stroke recognition. Improved triage tools also reduce time-to-diagnosis and facilitate quicker emergency responses.
This review highlights several methods for improving stroke identification in ACCs. Despite improvements in PPV, sensitivity, and diagnosis time, the lack of generalised standards, single-centre studies, and various population characteristics hinder broader impact. Future research should prioritise well-designed studies with standardised benchmarks to determine effectiveness, enabling effective prehospital stroke identification strategies.
在急救电话中准确识别中风仍然具有挑战性,导致诊断准确性低以及派遣适当服务的延迟。关于改善呼叫处理人员中风识别方法的证据有限。本范围综述探讨了在救护车控制中心(ACC)的紧急呼叫期间增强中风识别的方法。
我们按照乔安娜·布里格斯研究所的方法进行了范围综述,并遵循PRISMA-ScR指南。在五个数据库中进行了系统检索:Embase、Medline、Scopus、Web of Science和CINAHL,以及灰色文献来源,涵盖1964年1月至2024年7月的出版物。我们纳入了研究在ACC的紧急呼叫期间改善中风识别方法的研究。为了评估这些方法的有效性,符合条件的研究必须评估以下至少一项结果:中风诊断的准确性、诊断时间、工作人员培训的有效性以及识别技术的可接受性。两名评审员独立筛选研究、提取数据并进行归纳主题分析以识别共同主题。
在识别出的3619项研究中,有7项符合纳入标准。纳入的研究集中在技术和算法(n = 3)、培训和教育计划(n = 2)以及改进的分诊工具(n = 2),以在拨打给ACC的紧急呼叫期间增强中风识别。关于技术和算法的研究报告称,与标准协议相比,使用新算法时中风识别的敏感性和阳性预测值(PPV)有所提高。培训计划提高了调度员对中风识别的敏感性。改进的分诊工具也减少了诊断时间并促进了更快的应急响应。
本综述强调了几种在ACC中改善中风识别的方法。尽管PPV、敏感性和诊断时间有所改善,但缺乏通用标准、单中心研究以及各种人群特征阻碍了更广泛的影响。未来的研究应优先进行具有标准化基准的精心设计的研究,以确定有效性,从而制定有效的院前中风识别策略。