Development and validation of a 2-year new-onset stroke risk prediction model for people over age 45 in China.

Multiple factors, including increasing incidence, poor knowledge of stroke and lack of effective, noninvasive and convenient stroke risk prediction tools, make it more difficult for precautions against stroke in China. Effective prediction models for stroke may assist to establish better risk awareness and management, healthier lifestyle, and lower stroke incidence for people. The China Health and Retirement Longitudinal Survey was the development cohort. Logistic regression was applied to model's development, in which the candidate variables with statistically significant coefficient were included in the prediction model. The area under receiver operating characteristic curve (AUC) and 10-times cross-validation were used for internal validation. Cutoff point of high-risk group was measured by Youden index. The China Health and Nutrition Survey was the validation cohort. The development cohort and the validation cohort included 16557 and 5065 participants, and the incidence density was 358.207/100,000 person-year and 350.701/100,000 person-year, respectively. The model for 2-year new-onset stroke risk prediction included age, hypertension, diabetes, heart disease, and smoking. The AUC and cross-validation AUC were 0.707 (95% confidence interval[CI]: 0.664, 0.750) and the 0.710 (95% CI: 0.650, 0.736). The sensitivity, specificity and accuracy of the cutoff point were 0.774, 0.545, and 0.319. The AUC and cross-validation AUC were 0.800 (95% CI: 0.744, 0.856) and 0.811(95% CI:0.714, 0.847), and the sensitivity, specificity and accuracy of cutoff point being 0.857,0.569, and 0.426 in external validation. A simple prediction tool using 5 noninvasive and easily accessible factors can assist in 2-year new-onset stroke risk prediction in Chinese people over 45 years old, which is believed to be applicable in identifying high-risk individuals and health management in China.