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

Abstract

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.

Development of rapid and effective risk prediction models for stroke in the Chinese population: a cross-sectional study

“…We have provided more details of similar studies in recent years in table 5. Compared with other studies,14 19 20 37–41 the models we used have stronger prediction and discrimination performances with higher AUCs, which may be due to the inclusion of more variables in this study. Many machine learning models are sensitive to imbalanced data.…”

The Association between Physical Activity and Cognitive Function: Data from the China Health and Nutrition Survey

“…In the present study, occupational activity was not statistically associated with the cognitive function in older people; this may be because the CNHS database collected the data of individuals aged 45 and above [ 32 ]. In our study, we included the participants with the age ≥ 55 years old and most of them may be retired and those people were not engaged in the occupational activities.…”

Development and Validation of a Risk Prediction Model to Estimate the Risk of Stroke Among Hypertensive Patients in University of Gondar Comprehensive Specialized Hospital, Gondar, 2012 to 2022

“…In a study of adults over 45 years of age in China, 26 the development performance for a 2-year new-onset stroke risk prediction model was AUROC 0.707 and the validation performance was 0.710. Even if the model was valid, it performed poorly in differentiating between low and high risk, whereas our model performed very well with sensitivity, specificity, and positive and negative predictive values of 92.79%, 93.51%, 71.53%, and 98.66%, respectively.…”

“… 23 , 24 The Framingham Stroke Risk Score (FSRS) and Ischemic Cardiovascular Disease model (ICVD) were developed in 1994 and 2006, respectively, and use age, sex, smoking, blood pressure (BP), total cholesterol, diabetes mellitus, heart disease, and body mass index (BMI) to predict risk with an AUC of 0.588. 25 , 26 However, these two models have a limited capacity to predict stroke risk, and their ability to predict a single stroke result is unknown. Furthermore, assessing shifting risk variables and their consequences on stroke is out of date.…”