Wagstaff Holden M, Crowe Remle P, Youngquist Scott T, Stoecklein H Hill, Treichel Ali, He Yao, Majersik Jennifer J
Department of Emergency Medicine, University of Utah, Salt Lake City, Utah.
ESO: Emergency Medical Services Software, Austin, Texas.
Prehosp Emerg Care. 2025 Jan 23:1-8. doi: 10.1080/10903127.2024.2430442.
Previous research demonstrated that the numerical Cincinnati Prehospital Stroke Scale (CPSS) identifies large vessel occlusion (LVO) at similar rates compared to dedicated LVO screening tools. We aimed to compare numerical CPSS to additional stroke scales using a national emergency medical services (EMS) database.
Using the ESO Data Collaborative, the largest EMS database with linked hospital data, we retrospectively analyzed prehospital patient records from 2022. Each EMS record was linked to corresponding emergency department (ED) and inpatient records through a data exchange platform. Prehospital CPSS was compared to the Cincinnati Stroke Triage Assessment Tool (C-STAT), the Field Assessment Stroke Triage for Emergency Destination (FAST-ED), and the Balance Eyes Face Arm Speech Time (BE-FAST). The optimal prediction cut points for LVO screening were determined by intersecting the sensitivity and specificity curves for each scale. To compare the discriminative abilities of each scale among those diagnosed with LVO, we used the area under the receiver operating curve (AUROC).
We identified 17,442 prehospital records from 754 EMS agencies with ≥1 documented stroke scale of interest: 30.3% ( = 5,278) had a hospital diagnosis of stroke, of which 71.6% ( = 3,781) were ischemic; of those, 21.6% ( = 817) were diagnosed with LVO. CPSS score ≥2 was found to be predictive of LVO with 76.9% sensitivity, 68.0% specificity, and AUROC 0.787 (95%CI 0.722-0.801). All other tools had similar predictive abilities, with sensitivity/specificity/AUROC of: C-STAT 62.5%/76.5%/0.727 (0.555-0.899); FAST-ED 61.4%/76.1%/0.780 (0.725-0.836); BE-FAST 70.4%/67.1%/0.739 (0.697-0.788).
The less complex CPSS exhibited comparable performance to three frequently employed LVO detection tools. The EMS leadership, medical directors, and stroke system directors should weigh the complexity of stroke severity instruments and the challenges of ensuring consistent and accurate use when choosing which tool to implement. The straightforward and widely adopted CPSS may improve compliance while maintaining accuracy in LVO detection.
先前的研究表明，数字版辛辛那提院前卒中量表（CPSS）识别大血管闭塞（LVO）的比率与专门的LVO筛查工具相似。我们旨在使用国家紧急医疗服务（EMS）数据库，将数字版CPSS与其他卒中量表进行比较。
我们使用了ESO数据协作组织（最大的与医院数据相链接的EMS数据库），对2022年的院前患者记录进行了回顾性分析。每个EMS记录通过一个数据交换平台与相应的急诊科（ED）和住院患者记录相链接。将院前CPSS与辛辛那提卒中分诊评估工具（C-STAT）、急诊目的地现场评估卒中分诊（FAST-ED）以及平衡眼脸面部手臂言语时间（BE-FAST）进行比较。通过交叉每个量表的敏感性和特异性曲线，确定LVO筛查的最佳预测切点。为了比较每个量表在LVO诊断患者中的判别能力，我们使用了受试者工作特征曲线下面积（AUROC）。
我们从754个EMS机构中识别出17442份院前记录，这些记录至少有1份记录了感兴趣的卒中量表：30.3%（n = 5278）的患者在医院被诊断为卒中，其中71.6%（n = 3781）为缺血性卒中；在这些缺血性卒中患者中，21.6%（n = 817）被诊断为LVO。发现CPSS评分≥2对LVO具有预测性，敏感性为76.9%，特异性为68.0%，AUROC为0.787（95%CI 0.722 - 0.801）。所有其他工具具有相似的预测能力，其敏感性/特异性/AUROC分别为：C-STAT 62.5%/76.5%/0.727（0.555 - 0.899）；FAST-ED 61.4%/76.1%/0.780（0.725 - 0.836）；BE-FAST 70.4%/67.1%/0.739（0.697 - 0.788）。
不太复杂的CPSS表现出与三种常用的LVO检测工具相当的性能。EMS领导层、医疗主任和卒中系统主任在选择实施哪种工具时，应权衡卒中严重程度评估工具的复杂性以及确保一致和准确使用所面临的挑战。简单且广泛采用的CPSS在保持LVO检测准确性的同时，可能会提高依从性。
