Woo-Keun Seo1, Sung-Hoon Kang2, Jin-Man Jung3, Jeong-Yoon Choi3, Kyungmi Oh2. 1. Department of Neurology, College of Medicine, Korea University Guro Hospital, Korea University, Seoul, Korea. Electronic address: nukseo@korea.ac.kr. 2. Department of Neurology, College of Medicine, Korea University Guro Hospital, Korea University, Seoul, Korea. 3. Department of Neurology, College of Medicine, Korea University Ansan Hospital, Korea University, Seoul, Korea.
Abstract
BACKGROUND AND PURPOSE: Identification of high risk population for atrial fibrillation among acute stroke patients is a center of attention. The objective of the present study was to construct a model that can predict the presence of atrial fibrillation in ischemic stroke patients and to validate the model. METHODS: From a prospectively collected hospital-based stroke registry participated by two hospital, we selected data of patients who were admitted within 24 h after the onset of symptoms. Using a dataset of 1355 acute ischemic stroke patients, a model to predict the presence of atrial fibrillation was constructed and the probability of the presence of atrial fibrillation (AF-probability) was calculated. The patients were classified into low-risk, moderate-risk, and high-risk groups according to AF-probability. The performance of the model to predict atrial fibrillation among acute stroke patients was investigated and validated. RESULTS: Seven factors were selected as constituents of the model including age, left atrial size, free fatty acid level, triglyceride level, susceptibility vessel sign, hemorrhagic transformation, and cortical involvement. The performance of the model was excellent, with a C-statistic of 0.908 (95% confidence interval 0.887-0.930). According to risk group, true positivity for atrial fibrillation was 4.3%, 36.5%, 91.2% in the low-risk, moderate-risk, and high-risk groups, respectively. The internal and external validation test showed stable consistency of the model. CONCLUSION: The model constructed in this study could stratify stroke patients according to their risk of AF and may be helpful for selecting candidates who need extensive cardiac monitoring.
BACKGROUND AND PURPOSE: Identification of high risk population for atrial fibrillation among acute strokepatients is a center of attention. The objective of the present study was to construct a model that can predict the presence of atrial fibrillation in ischemic strokepatients and to validate the model. METHODS: From a prospectively collected hospital-based stroke registry participated by two hospital, we selected data of patients who were admitted within 24 h after the onset of symptoms. Using a dataset of 1355 acute ischemic strokepatients, a model to predict the presence of atrial fibrillation was constructed and the probability of the presence of atrial fibrillation (AF-probability) was calculated. The patients were classified into low-risk, moderate-risk, and high-risk groups according to AF-probability. The performance of the model to predict atrial fibrillation among acute strokepatients was investigated and validated. RESULTS: Seven factors were selected as constituents of the model including age, left atrial size, free fatty acid level, triglyceride level, susceptibility vessel sign, hemorrhagic transformation, and cortical involvement. The performance of the model was excellent, with a C-statistic of 0.908 (95% confidence interval 0.887-0.930). According to risk group, true positivity for atrial fibrillation was 4.3%, 36.5%, 91.2% in the low-risk, moderate-risk, and high-risk groups, respectively. The internal and external validation test showed stable consistency of the model. CONCLUSION: The model constructed in this study could stratify strokepatients according to their risk of AF and may be helpful for selecting candidates who need extensive cardiac monitoring.
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