Mee-Kyoung Kim1, Kyungdo Han2, Jae-Hyoung Cho3, Hyuk-Sang Kwon1, Kun-Ho Yoon4, Seung-Hwan Lee5. 1. Division of Endocrinology and Metabolism, Department of Internal Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea. 2. Department of Statistics and Actuarial Science, Soongsil University, Seoul 06978, Republic of Korea. 3. Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea. 4. Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea. 5. Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea. Electronic address: hwanx2@catholic.ac.kr.
Abstract
AIMS: The incidence of stroke differs between Asians and Caucasians, and between people with or without diabetes mellitus (DM). This study aimed to develop a model to predict the risk of stroke in middle-aged patients with type 2 DM. METHODS: Using the National Health Insurance Database in Korea, data from patients aged 40-64 years with type 2 DM who received a health examination from 2009 to 2012 (n = 1,297,131) were analyzed as development (n = 907,992) and validation (n = 389,139) cohorts. Cox proportional-hazards regression model was used to derive a risk-scoring system, and 13 predictive variables were selected. A risk score nomogram based on the risk prediction model was created to estimate the 5-year risk of stroke. RESULTS: In patients with type 2 DM, significant predictors for the development of stroke were older age, being male or a current smoker, lack of exercise, low body mass index, low estimated glomerular filtration rate, presence of coronary heart disease, longer duration of DM, insulin or multiple oral hypoglycemic agents use, low (<100 mg/dL) or high (≥140 mg/dL) fasting blood glucose, high systolic blood pressure, high total cholesterol, and presence of atrial fibrillation. The concordance indexes for stroke prediction were 0.703 (95% confidence interval [CI] 0.700-0.707) in the development cohort and 0.703 (95% CI 0.698-0.708) in the validation cohort. CONCLUSIONS: We developed a risk model using various clinical parameters to predict stroke in patients with type 2 DM. This model may provide helpful information for identifying high-risk patients and guide prevention of stroke in this specific population.
AIMS: The incidence of stroke differs between Asians and Caucasians, and between people with or without diabetes mellitus (DM). This study aimed to develop a model to predict the risk of stroke in middle-aged patients with type 2 DM. METHODS: Using the National Health Insurance Database in Korea, data from patients aged 40-64 years with type 2 DM who received a health examination from 2009 to 2012 (n = 1,297,131) were analyzed as development (n = 907,992) and validation (n = 389,139) cohorts. Cox proportional-hazards regression model was used to derive a risk-scoring system, and 13 predictive variables were selected. A risk score nomogram based on the risk prediction model was created to estimate the 5-year risk of stroke. RESULTS: In patients with type 2 DM, significant predictors for the development of stroke were older age, being male or a current smoker, lack of exercise, low body mass index, low estimated glomerular filtration rate, presence of coronary heart disease, longer duration of DM, insulin or multiple oral hypoglycemic agents use, low (<100 mg/dL) or high (≥140 mg/dL) fasting blood glucose, high systolic blood pressure, high total cholesterol, and presence of atrial fibrillation. The concordance indexes for stroke prediction were 0.703 (95% confidence interval [CI] 0.700-0.707) in the development cohort and 0.703 (95% CI 0.698-0.708) in the validation cohort. CONCLUSIONS: We developed a risk model using various clinical parameters to predict stroke in patients with type 2 DM. This model may provide helpful information for identifying high-risk patients and guide prevention of stroke in this specific population.
Authors: Mónica Enguita-Germán; Ibai Tamayo; Arkaitz Galbete; Julián Librero; Koldo Cambra; Berta Ibáñez-Beroiz Journal: Int J Environ Res Public Health Date: 2021-11-24 Impact factor: 3.390