MRI-based Algorithm for Acute Ischemic Stroke Subtype Classification

Title
MRI-based Algorithm for Acute Ischemic Stroke Subtype Classification
Author(s)
이준Youngchai Ko[Youngchai Ko]SooJoo Lee[SooJoo Lee]Moo-Ku Han[Moo-Ku Han]Jong-Moo Park[Jong-Moo Park]Kysik Kang[Kysik Kang]Tai Hwan Park[Tai Hwan Park]Yong-Jin Cho[Yong-Jin Cho]Keun-Sik Hong[Keun-Sik Hong]Hee-Joon Bae[Hee-Joon Bae]
Keywords
CRYPTOGENIC STROKE; RISK-FACTORS; RECURRENCE; TERRITORY; REGISTRY; TOAST; MULTICENTER; MANAGEMENT; MECHANISMS; INFARCTION
Issue Date
201409
Publisher
KOREAN STROKE SOC
Citation
JOURNAL OF STROKE, v.16, no.3, pp.161 - 172
Abstract
Background and Purpose In order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a stroke registry, we developed and implemented a magnetic resonance imaging (MRI)-based algorithm for acute ischemic stroke subtype classification (MAGIC). Methods We enrolled patients who experienced an acute ischemic stroke, were hospitalized in the 14 participating centers within 7 days of onset, and had relevant lesions On MR-diffusion weighted imaging (DWI). MAGIC was designed to reflect recent advances in stroke imaging and thrombolytic therapy. The inter-rater reliability was compared with and without MAGIC to classify the Trial of Org 10172 in Acute Stroke Treatment (TOAST) of each stroke patient. MAGIC was then applied to all stroke patients hospitalized since July 2011, and information about stroke subtypes, other clinical characteristics, and stroke recurrence was collected via a web-based registry database. Results The overall intra-class correlation coefficient (ICC) value was 0.43 (95% CI, 0.31-0.57) for MAGIC and 0.28 (95% CI, 0.18-0.42) for TOAST. Large artery atherosclerosis (LAA) was the most common cause of acute ischemic stroke (38.3%), followed by cardioembolism (CE, 22.8%), undetermined cause (UD, 22.2%), and small-vessel occlusion (SVO, 14.604. One-year stroke recurrence rates were the highest for two or more UDs (11.80%, followed by LAA (7.30%), CE (5.60%), and SVO (2.50%). Conclusions Despite several limitations, this study shows that the MAGIC system is feasible and may be helpful to classify stroke subtype in the clinic.
URI
http://hdl.handle.net/YU.REPOSITORY/30890http://dx.doi.org/10.5853/jos.2014.16.3.161
ISSN
2287-6391
Appears in Collections:
의과대학 > 신경과학교실 > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE