Caffarelli Mauro, Wood Andrew J, Crowe Remle P, Amorim Edilberto, Kamel Hooman, Kim Anthony S, Guterman Elan L
Department of Pediatrics, University of California, San Francisco. San Francisco, CA.
Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco. San Francisco, CA.
medRxiv. 2024 Sep 27:2024.09.25.24314407. doi: 10.1101/2024.09.25.24314407.
Emergency Medical Services (EMS) clinicians are front-line in evaluating patients with stroke in the community. Their ability to correctly identify stroke influences downstream management decisions. We sought to use a large national database of prehospital clinical data to determine risk factors associated with missed EMS stroke identification.
Retrospective study examining EMS evaluation of adults with Emergency Department (ED) stroke diagnosis. We leveraged the ESO Data Collaborative research dataset containing EHR data from 2019-2022 that has a subset of encounters with linked hospital diagnostic codes. Our primary outcome was the presence of an EMS diagnosis of stroke. We evaluated the association between demographic and clinical variables with EMS stroke identification using Pearson χ2 test for demographic variables and multivariable GLM for clinical variables with adjustment for demographic variables.
We identified 34,504 EMS encounters for patients with ED stroke diagnosis. Of these, 11,077 (32.1%) strokes had missed EMS stroke identification and instead had an EMS impression of "Generalized Weakness" (25.9%), "Altered Level of Consciousness" (24.9%), and "Dizziness" (7.2%). Patients more likely to have missed prehospital stroke identification were of Black race (p=0.0001) and Hispanic ethnicity (p=0.0001). Clinical variables associated with higher risk of missed EMS stroke identification were suspected alcohol or drug use (RR 1.48, 95% CI 1.37-1.59), low GCS (RR 1.17, 95% CI 1.10-1.24), tachycardia (RR 1.05, 95% CI 1.01-1.09), and hypotension (RR 1.47, 95% CI 1.34-1.61).
Approximately 1-in-3 patients transported by EMS did not have their stroke identified in the prehospital setting. Factors associated with lower odds of missed EMS stroke identification provide a starting point for future performance improvement initiatives.
紧急医疗服务（EMS）临床医生是社区中评估中风患者的一线人员。他们正确识别中风的能力会影响下游的管理决策。我们试图利用一个大型的全国院前临床数据库来确定与EMS漏诊中风相关的风险因素。
回顾性研究，检查EMS对急诊科（ED）诊断为中风的成年人的评估情况。我们利用了ESO数据合作研究数据集，该数据集包含2019年至2022年的电子健康记录（EHR）数据，其中有一部分病例与医院诊断代码相关联。我们的主要结局是EMS诊断为中风。我们使用Pearson卡方检验评估人口统计学变量与EMS中风识别之间的关联，对于临床变量，使用多变量广义线性模型，并对人口统计学变量进行调整。
我们确定了34504例ED诊断为中风患者的EMS病例。其中，11077例（32.1%）中风在EMS中被漏诊，取而代之的是EMS诊断为“全身无力”（25.9%）、“意识水平改变”（24.9%）和“头晕”（7.2%）。院前中风识别漏诊可能性较高的患者为黑人（p=0.0001）和西班牙裔（p=0.0001）。与EMS漏诊中风识别风险较高相关的临床变量包括疑似酒精或药物使用（风险比[RR]1.48，95%置信区间[CI]1.37-1.59）、格拉斯哥昏迷量表（GCS）评分低（RR 1.17，95% CI 1.10-1.24）、心动过速（RR 1.05，95% CI 1.01-1.09）和低血压（RR 1.47，95% CI 1.34-1.61）。
由EMS转运的患者中，约三分之一在院前未被识别出中风。与EMS漏诊中风识别几率较低相关的因素为未来的绩效改进举措提供了一个起点。
