J Korean Neurol Assoc.
2010 Feb;28(1):13-21.
Development of a Stroke Prediction Model for Korean
- Affiliations
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- 1Department of Biostatistics, Korea University College of Medicine, Seoul, Korea. jyleeuf@korea.ac.kr
- 2Department of Neurology, Eulji General Hospital, Eulji University, Seoul, Korea.
- 3Department of Neurology, Seoul Medical Center, Seoul, Korea.
- 4Department of Neurology, Soonchunhyang University Hospital, Seoul, Korea.
- 5Department of Neurology, Eulji University Hospital, Eulji University, Daejeon, Korea.
- 6Department of Neurology, Ilsan Paik Hospital, Inje University, Goyang, Korea.
- 7Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
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.
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