Scala Irene, Covino Marcello, Rizzo Pier Andrea, Bisegna Maurizio, Marchese Davide, Bellavia Simone, Broccolini Aldobrando, Di Iorio Riccardo, Della Marca Giacomo, Brunetti Valerio, Franceschi Francesco, Monforte Mauro, Calabresi Paolo, Frisullo Giovanni
Department of Neuroscience, Sense Organs, and Thorax, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Catholic University of Sacred Heart, Rome, Italy.
Eur Stroke J. 2025 May 15:23969873251338654. doi: 10.1177/23969873251338654.
Early differential diagnosis between stroke mimics and cerebrovascular events is a major challenge in the Emergency Department (ED). The primary aim of this study was to identify diagnostic predictors of stroke mimics based on parameters acquired during the ED triage of patients with suspected stroke. Secondly, we aimed to develop a diagnostic score for early differential diagnosis. Moreover, we compared the diagnostic accuracy of our score with that of other two validated scores.
We included consecutive patients presenting to the ED of an urban teaching hospital for suspected stroke from 2015 to 2022 in the retrospective derivation cohort and during 2023 in the prospective validation cohort. Cerebrovascular events predictors were identified by logistic regression and were used to develop the Stroke Mimics Score (SMS). The diagnostic performance of SMS was assessed using the area under the receiver operating characteristics curves (AUROC) and the comparison with other diagnostic scores (FABS - Facial droop, Atrial fibrillation, Age, Systolic blood pressure, Seizure, Sensory symptoms- and TMS- TeleStroke Mimic score) was performed through DeLong method and Net Reclassification Index (NRI).
About 8648 patients were included in the study, 6998 in the retrospective cohort, and 1650 in the prospective cohort. In the retrospective cohort, 3266 (46.7%) patients had a final diagnosis of stroke mimic. Several variables collected by triage nurses independently predicted cerebrovascular event over stroke mimic diagnosis. The 10-variable SMS had excellent diagnostic performance in both the derivation and validation cohorts [AUROC 0.777 (95% CI: 0.766-0.788) and 0.774 (95% CI: 0.752-0.797), respectively] and outperformed FABS and TMS in all statistical comparisons.
Several clinical variables elicited by triage nurses in the ED help to differentiate cerebrovascular events from stroke mimics in suspected stroke patients. The SMS is an easy-to-use score that could help selecting the best pathway for such patients.
在急诊科（ED）中，早期鉴别疑似中风患者的类中风症状和脑血管事件是一项重大挑战。本研究的主要目的是基于疑似中风患者在急诊分诊期间获取的参数，确定类中风症状的诊断预测因素。其次，我们旨在开发一种用于早期鉴别诊断的诊断评分。此外，我们将我们的评分与其他两个经过验证的评分的诊断准确性进行了比较。
我们纳入了2015年至2022年期间在一家城市教学医院急诊科因疑似中风就诊的连续患者作为回顾性推导队列，以及2023年期间的前瞻性验证队列。通过逻辑回归确定脑血管事件预测因素，并用于开发类中风症状评分（SMS）。使用受试者操作特征曲线下面积（AUROC）评估SMS的诊断性能，并通过DeLong方法和净重新分类指数（NRI）与其他诊断评分（FABS - 面部下垂、心房颤动、年龄、收缩压、癫痫发作、感觉症状 - 以及TMS - 远程类中风症状评分）进行比较。
该研究共纳入约8648例患者，回顾性队列中有6998例，前瞻性队列中有1650例。在回顾性队列中，3266例（46.7%）患者最终诊断为类中风症状。分诊护士收集的几个变量独立预测了脑血管事件而非类中风症状诊断。10变量的SMS在推导队列和验证队列中均具有出色的诊断性能[AUROC分别为0.777（95%CI：0.766 - 0.788）和0.774（95%CI：0.752 - 0.797）]，并且在所有统计比较中均优于FABS和TMS。
急诊科分诊护士引出的几个临床变量有助于在疑似中风患者中区分脑血管事件和类中风症状。SMS是一种易于使用的评分，可为这类患者选择最佳诊疗路径提供帮助。
