W Oliver Tobin, Joseph G Hentz, Bentley J Bobrow, Bart M Demaerschalk
{"title":"Identification of stroke mimics in the emergency department setting.","authors":"W Oliver Tobin, Joseph G Hentz, Bentley J Bobrow, Bart M Demaerschalk","doi":"10.4137/jcnsd.s2280","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and purpose: </strong>Previous studies have shown a stroke mimic rate of 9%-31%. We aimed to establish the proportion of stroke mimics amongst suspected acute strokes, to clarify the aetiology of stroke mimic and to develop a prediction model to identify stroke mimics.</p><p><strong>Methods: </strong>This was a retrospective cohort observational study. Consecutive \"stroke alert\" patients were identified over nine months in a primary stroke centre. 31 variables were collected. Final diagnosis was defined as \"stroke\" or \"stroke mimic\". Multivariable regression analysis was used to define clinical predictors of stroke mimic.</p><p><strong>Results: </strong>206 patients were reviewed. 22% were classified as stroke mimics. Multivariable scoring did not help in identification of stroke mimics. 99.5% of patients had a neurological diagnosis at final diagnosis.</p><p><strong>Discussion: </strong>22% of patients with suspected acute stroke had a stroke mimic. The aetiology of stroke mimics was varied, with seizure, encephalopathy, syncope and migraine being commonest. Multivariable scoring for identification of stroke mimics is not feasible. 99.5% of patients had a neurological diagnosis. This strengthens the case for the involvement of stroke neurologists/stroke physicians in acute stroke care.</p>","PeriodicalId":89798,"journal":{"name":"Journal of brain disease","volume":"1 ","pages":"19-22"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3676321/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of brain disease","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4137/jcnsd.s2280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2009/1/1 0:00:00","PubModel":"Print","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background and purpose: Previous studies have shown a stroke mimic rate of 9%-31%. We aimed to establish the proportion of stroke mimics amongst suspected acute strokes, to clarify the aetiology of stroke mimic and to develop a prediction model to identify stroke mimics.
Methods: This was a retrospective cohort observational study. Consecutive "stroke alert" patients were identified over nine months in a primary stroke centre. 31 variables were collected. Final diagnosis was defined as "stroke" or "stroke mimic". Multivariable regression analysis was used to define clinical predictors of stroke mimic.
Results: 206 patients were reviewed. 22% were classified as stroke mimics. Multivariable scoring did not help in identification of stroke mimics. 99.5% of patients had a neurological diagnosis at final diagnosis.
Discussion: 22% of patients with suspected acute stroke had a stroke mimic. The aetiology of stroke mimics was varied, with seizure, encephalopathy, syncope and migraine being commonest. Multivariable scoring for identification of stroke mimics is not feasible. 99.5% of patients had a neurological diagnosis. This strengthens the case for the involvement of stroke neurologists/stroke physicians in acute stroke care.