Chhabra Nikita, English Stephen W, Butterfield Richard J, Zhang Nan, Hanus Abigail E, Basharath Rida, Miller Monet, Demaerschalk Bart M
Department of Neurology, Mayo Clinic College of Medicine and Science, Phoenix, AZ, USA.
Department of Neurology, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA.
J Telemed Telecare. 2025 Oct;31(9):1278-1284. doi: 10.1177/1357633X241273762. Epub 2024 Aug 19.
IntroductionTelestroke enables timely and remote evaluation of patients with acute stroke syndromes. However, stroke mimics represent more than 30% of this population. Given the resources required for the management of suspected acute ischemic stroke, several scales have been developed to help identify stroke mimics. Our objective was to externally validate four mimic scales (Khan Score (KS), TeleStroke Mimic Score (TS), simplified FABS (sFABS), and FABS) in a large, academic telestroke network.MethodsThis is a retrospective, Institutional Review Board-exempt study of all patients who presented with suspected acute stroke syndromes and underwent video evaluation between 2019 and 2020 at a large academic telestroke network. Detailed chart review was conducted to extract both the variables needed to apply the mimic scales, the final diagnosis confirmed by final imaging, and discharge diagnosis (cerebral ischemic vs stroke mimic). Overall score performance was assessed by calculating the area under curve (AUC). Youden cutpoint was established for each scale and used to calculate sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy.ResultsA total of 1043 patients were included in the final analysis. Final diagnosis of cerebral ischemia was made in 63.5% of all patients, and stroke mimic was diagnosed in 381 patients (36.5%). To predict stroke mimic, TS had the highest AUC (68.3), sensitivity (99.2%), and NPV (77.3%); KS had the highest accuracy (67.5%); FABS had the highest specificity (55.1%), and PPV (72.5%).ConclusionsWhile each scale offers unique strengths, none was able to identify stroke mimics effectively enough to confidently apply in clinical practice. There remains a need for significant clinical judgment to determine the likelihood of stroke mimic at presentation.
引言
远程卒中能够对急性卒中综合征患者进行及时且远程的评估。然而，卒中疑似病例在这一群体中占比超过30%。鉴于疑似急性缺血性卒中管理所需的资源，已开发了多种量表以帮助识别卒中疑似病例。我们的目的是在一个大型学术远程卒中网络中对四种疑似病例量表（汗氏评分（KS）、远程卒中疑似病例评分（TS）、简化FABS（sFABS）和FABS）进行外部验证。
方法
这是一项回顾性研究，无需经过机构审查委员会批准，研究对象为2019年至2020年期间在一个大型学术远程卒中网络中出现疑似急性卒中综合征并接受视频评估的所有患者。进行详细的病历审查，以提取应用疑似病例量表所需的变量、最终影像学检查确认的最终诊断以及出院诊断（脑缺血与卒中疑似病例）。通过计算曲线下面积（AUC）评估总体评分表现。为每个量表确定尤登切点，并用于计算敏感性、特异性、阴性预测值（NPV）、阳性预测值（PPV）和准确性。
结果
最终分析纳入了1043例患者。所有患者中63.5%最终诊断为脑缺血，381例患者（36.5%）被诊断为卒中疑似病例。为预测卒中疑似病例，TS的AUC最高（68.3）、敏感性最高（99.2%）和NPV最高（77.3%）；KS的准确性最高（67.5%）；FABS的特异性最高（55.1%）和PPV最高（72.5%）。
结论
虽然每个量表都有独特的优势，但没有一个量表能够有效地识别卒中疑似病例，从而自信地应用于临床实践。在判断就诊时卒中疑似病例的可能性方面，仍需要大量的临床判断。
