A model to predict risk of stroke in middle-aged adults with type 2 diabetes generated from a nationwide population-based cohort study in Korea. (May 2020)

• Record Type: Journal Article
• Authors: Kim, Mee-Kyoung; Han, Kyungdo; Cho, Jae-Hyoung; Kwon, Hyuk-Sang; Yoon, Kun-Ho; Lee, Seung-Hwan
• Is Part Of: Diabetes research and clinical practice. Volume 163(2020)
• Publication Date: 2020-05
• Subjects: AF atrial fibrillation -- BMI body mass index -- CHD coronary heart disease -- CVD cardiovascular disease -- DM diabetes mellitus -- FBG fasting blood glucose -- ICD International Classification of Diseases -- MI myocardial infarctions -- NHIS National Health Insurance System -- SBP systolic blood pressure; Diabetes -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462
• DOI: 10.1016/j.diabres.2020.108157
• Languages: English
• ISSNs: 0168-8227

Abstract

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.