Journal
JOURNAL OF THE AMERICAN HEART ASSOCIATION
Volume 3, Issue 3, Pages -Publisher
WILEY-BLACKWELL
DOI: 10.1161/JAHA.114.000838
Keywords
cerebrovascular disease; stroke mimics; telestroke; thrombolysis
Categories
Funding
- Principal Investigator of an National Institute of Neurological Disorders and Stroke
- Health Resource Services Administration Requisition [09-HRS9923-AB]
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Background-Up to 30% of acute stroke evaluations are deemed stroke mimics (SM). As telestroke consultation expands across the world, increasing numbers of SM patients are likely being evaluated via Telestroke. We developed a model to prospectively identify ischemic SMs during Telestroke evaluation. Methods and Results-We analyzed 829 consecutive patients from January 2004 to April 2013 in our internal New England-based Partners TeleStroke Network for a derivation cohort, and 332 cases for internal validation. External validation was performed on 226 cases from January 2008 to August 2012 in the Partners National TeleStroke Network. A predictive score was developed using stepwise logistic regression, and its performance was assessed using receiver-operating characteristic (ROC) curve analysis. There were 23% SM in the derivation, 24% in the internal, and 22% in external validation cohorts based on final clinical diagnosis. Compared to those with ischemic cerebrovascular disease (iCVD), SM had lower mean age, fewer vascular risk factors, more frequent prior seizure, and a different profile of presenting symptoms. The TeleStroke Mimic Score (TM-Score) was based on factors independently associated with SM status including age, medical history (atrial fibrillation, hypertension, seizures), facial weakness, and National Institutes of Health Stroke Scale >14. The TM-Score performed well on ROC curve analysis (derivation cohort AUC=0.75, internal validation AUC=0.71, external validation AUC=0.77). Conclusions-SMs differ substantially from their iCVD counterparts in their vascular risk profiles and other characteristics. Decision-support tools based on predictive models, such as our TM Score, may help clinicians consider alternate diagnosis and potentially detect SMs during complex, time-critical telestroke evaluations.
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