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Stroke Mimics in the Acute Setting: Role of Multimodal CT Protocol

Overview
Specialty Neurology
Date 2021 Dec 31
PMID 34969667
Citations 2
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Abstract

Background And Purpose: Ischemic stroke can be mimicked by nonischemic conditions. Due to emphasis on the rapid treatment of acute ischemic stroke, it is crucial to identify these conditions to avoid unnecessary therapies and potential complications. We investigated the performance of the multimodal CT protocol (unenhanced brain CT, CTA, and CTP) to discriminate stroke mimics from acute ischemic stroke.

Materials And Methods: We retrospectively selected multimodal CT studies performed for clinical suspicion of acute ischemic stroke in our center in a 24-month period, including patients with at least 1 follow-up imaging study (brain CT or MR imaging). Hemorrhagic strokes were excluded. We measured the performance of multimodal CT, comparing the original diagnostic results with the final clinical diagnosis at discharge.

Results: Among 401 patients, a stroke mimic condition was diagnosed in 89 (22%), including seizures (34.8%), migraine with aura attack (12.4%), conversion disorder (12.4%), infection (7.9%), brain tumor (7.9%), acute metabolic condition (6.7%), peripheral vertigo (5.6%), syncope (5.6%), transient global amnesia (3.4%), subdural hematoma (1.1%), cervical epidural hematoma (1.1%), and dural AVF (1.1%). Multimodal CT sensitivity, specificity, and accuracy were 24.7%, 99.7%, and 83%. Multimodal CT revealed peri-ictal changes in 13/31 seizures and diagnosed 7/7 brain tumors, 1/1 dural AVF, and 1/1 subdural hematoma. CT perfusion played a pivotal diagnostic role.

Conclusions: Multimodal CT demonstrated low sensitivity but high specificity in the diagnosis of stroke mimics in the acute setting. The high specificity of multimodal CT allows ruling out stroke and thereby avoiding unnecessary revascularization treatment in patients with diagnosis of a stroke mimic.

Citing Articles

Development, assessment and validation of a novel nomogram model for predicting stroke mimics in stroke center:A single-center observational study.

Chen X, Zhang S Heliyon. 2024; 10(19):e38602.

PMID: 39403531 PMC: 11472074. DOI: 10.1016/j.heliyon.2024.e38602.


Predicting Hypoperfusion Lesion and Target Mismatch in Stroke from Diffusion-weighted MRI Using Deep Learning.

Yu Y, Christensen S, Ouyang J, Scalzo F, Liebeskind D, Lansberg M Radiology. 2022; 307(1):e220882.

PMID: 36472536 PMC: 10068889. DOI: 10.1148/radiol.220882.

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