Neuroscience Institute, Geisinger Health System, Danville, PA 17822, USA; Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA.
Geisinger Commonwealth School of Medicine, Scranton, PA 18510, USA.
J Neurol Sci. 2022 Nov 15;442:120423. doi: 10.1016/j.jns.2022.120423. Epub 2022 Sep 26.
Stroke screening tools should have good diagnostic performance for early diagnosis and a proper therapeutic plan. This paper describes and compares various diagnostic tools used to identify stroke in emergency departments and prehospital setting.
The meta-analysis was conducted according to the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. The PubMed and Scopus databases were searched until December 31, 2021, for studies published on stroke screening tools. These tools' diagnostic performance (sensitivity and specificity) was pooled using a bivariate random-effects model whenever appropriate.
Eleven screening tools for stroke were identified in 29 different studies. The various tools had a wide range of sensitivity and specificity in different studies. In the meta-analysis, the Cincinnati Pre-hospital Stroke Scale, Face Arm Speech Test, and Recognition of Stroke in the Emergency Room (ROSIER) had sensitivity (between 83 and 91%) but poor specificity (all below 64%). When comparing all the tools, ROSIER had the highest sensitivity 90.5%. Los Angeles Pre-hospital Stroke Screen performed best in terms of specificity 88.7% but had low sensitivity (73.9%). Melbourne Ambulance Stroke Screen had a balanced performance in terms of sensitivity (86%) and specificity (76%). Sensitivity analysis consisting of only prospective studies showed a similar range of sensitivity and specificity.
All the stroke screening tools included in the review were comparable, but no clear superior screening tool could be identified. Simple screening tools like Cincinnati prehospital stroke scale (CPSS) have similar performance compared to more complex tools.
中风筛查工具应有良好的诊断性能，以便早期诊断和制定适当的治疗计划。本文描述并比较了在急诊科和院前环境中用于识别中风的各种诊断工具。
按照系统评价和诊断测试准确性研究的首选报告项目（PRISMA-DTA）指南进行荟萃分析。在 2021 年 12 月 31 日之前，在 PubMed 和 Scopus 数据库中搜索了关于中风筛查工具的研究。只要合适，就使用双变量随机效应模型汇总这些工具的诊断性能（敏感性和特异性）。
在 29 项不同的研究中确定了 11 种中风筛查工具。不同的工具在不同的研究中具有广泛的敏感性和特异性。在荟萃分析中，辛辛那提院前中风量表、面臂言语测试和急诊室中风识别量表（ROSIER）的敏感性（83%至 91%）较高，但特异性（均低于 64%）较差。当比较所有工具时，ROSIER 的敏感性最高，为 90.5%。洛杉矶院前中风筛查在特异性方面表现最好，为 88.7%，但敏感性较低（73.9%）。墨尔本救护车中风筛查在敏感性（86%）和特异性（76%）方面具有平衡的性能。仅包含前瞻性研究的敏感性分析显示出类似的敏感性和特异性范围。
本综述中纳入的所有中风筛查工具都具有可比性，但没有明确的优势筛查工具。与更复杂的工具相比，辛辛那提院前中风量表（CPSS）等简单的筛查工具具有相似的性能。
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