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KMID : 1142120140160030161
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Journal of Stroke
2014 Volume.16 No. 3 p.161 ~ p.172
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MRI-based Algorithm for Acute Ischemic Stroke Subtype Classification
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Ko Young-Chai
Lee Soo-Joo
Chung Jong-Won
Han Moon-Ku
Park Jong-Moo
Kang Kyu-Sik
Park Tai-Hwan
Park Sang-Soon
Cho Yong-Jin
Hong Keun-Sik
Lee Kyung-Bok
Lee Jun
Kim Dong-Eog
Kim Dae-Hyun
Cha Jae-Kwan
Kim Joon-Tae
Choi Jay-Chol
Shin Dong-Ick
Lee Ji-Sung
Lee June-Young
Yu Kyung-Ho
Lee Byung-Chul
Bae Hee-Joon
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Abstract
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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, 14.6%). 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%).
Conclusions: Despite several limitations, this study shows that the MAGIC system is feasible and may be helpful to classify stroke subtype in the clinic.
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KEYWORD
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Stroke, Magnetic resonance imaging, Algorithm, Classification
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