| Development of a Stroke Prediction Model for Korean |
| 이지성, 박종무a 박태환b 이경복c 이수주d 조용진e 한문구f 배희준f 이준영 |
| 고려대학교 의과대학 의학통계학교실, 을지대학교 을지병원 신경과a, 서울의료원 신경과b, 순천향대학교병원 신경과c, 을지대학교 을지대학병원 신경과d, 인제대학교 일산백병원 신경과e, 서울대학교 의과대학 분당서울대학교병원 신경과f |
| 한국인을 위한 뇌졸중 발생 예측모형 개발 |
| Ji Sung Lee |
| Department of Biostatistics, Korea University College of Medicine, Departments of Neurologya, Eulji General Hospital, Eulji University, Department of Neurologyb, Seoul Medical Center, Department of Neurologyc, Soonchunhyang University Hospital, Seoul, Korea, Department of Neurologyd, Eulji University Hospital, Eulji University, Daejeon, Korea, Department of Neurologye, Ilsan Paik Hospital, Inje University, Goyang, Korea, Department of Neurologyf, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea |
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| Abstract |
Background: Assessing an individual’s risk of stroke can be a starting point for stroke prevention. The aim of this study was to develop a stroke prediction model that can be applied to the Korean population, using the best available current knowledge.
Methods: A sex- and age-specific stroke prediction model that is applicable specifically to Koreans was developed using Gail’s breast cancer prediction model, which is based on competing risk theory.
Results: The relative risks for major stroke risk factors, including hypertension, diabetes, hypercholesterolemia, atrial fibrillation, ischemic heart disease, previous stroke, obesity, and smoking status, were obtained from a recent systematic review of stroke risk factors among Koreans. The results were incorporated into the concept of a proportional hazard regression model. For baseline age- and sex-specific hazard rates for stroke, we employed Jee’s 10-year stroke-risk prediction model with its reference categories for predictor variables. Death-certificate data from the Korea National Statistical Office were used to calculate competing risks of stroke in our model.
Conclusions: Our prediction model for stroke incidence may be useful for predicting an individual’s risk of stroke based on his/her age, sex, and risk factors. This model will contribute to the development of individualized risk-specific guidelines for the prevention of stroke. KeyWords:Korean, Stroke, Risk Prediction |
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