MRI-based Algorithm for Acute Ischemic Stroke Subtype Classification

• Main Authors: Youngchai Ko, SooJoo Lee, Jong-Won Chung, Moon-Ku Han, Jong-Moo Park, Kyusik Kang, Tai Hwan Park, Sang-Soon Park, Yong-Jin Cho, Keun-Sik Hong, Kyung Bok Lee, Jun Lee, Dong-Eog Kim, Dae-Hyun Kim, Jae-Kwan Cha, Joon-Tae Kim, Jay Chol Choi, Dong-Ick Shin, Ji Sung Lee, Juneyoung Lee, Kyung-Ho Yu, Byung-Chul Lee, Hee-Joon Bae
• Format: Article
• Language: English
• Published: Korean Stroke Society 2014-09-01
• Series: Journal of Stroke
• Subjects: stroke; magnetic resonance imaging; algorithm; classification
• Online Access: http://www.j-stroke.org/upload/pdf/jos-16-161.pdf

Background and PurposeIn 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).MethodsWe 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.ResultsThe 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, 14.6%). 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%).ConclusionsDespite several limitations, this study shows that the MAGIC system is feasible and may be helpful to classify stroke subtype in the clinic.