Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population

医学 动脉粥样硬化性心血管疾病 心脏病学 内科学 疾病 人口 环境卫生
作者
Xueli Yang,Jianxin Li,Dongsheng Hu,Jichun Chen,Ying Li,Jianfeng Huang,Xiaoqing Liu,Fangchao Liu,Jie Cao,Chong Shen,Ling Yu,Fanghong Lu,Xianping Wu,Liancheng Zhao,Xigui Wu,Dongfeng Gu
出处
期刊:Circulation [Lippincott Williams & Wilkins]
卷期号:134 (19): 1430-1440 被引量:688
标识
DOI:10.1161/circulationaha.116.022367
摘要

Background: The accurate assessment of individual risk can be of great value to guiding and facilitating the prevention of atherosclerotic cardiovascular disease (ASCVD). However, prediction models in common use were formulated primarily in white populations. The China-PAR project (Prediction for ASCVD Risk in China) is aimed at developing and validating 10-year risk prediction equations for ASCVD from 4 contemporary Chinese cohorts. Methods: Two prospective studies followed up together with a unified protocol were used as the derivation cohort to develop 10-year ASCVD risk equations in 21 320 Chinese participants. The external validation was evaluated in 2 independent Chinese cohorts with 14 123 and 70 838 participants. Furthermore, model performance was compared with the Pooled Cohort Equations reported in the American College of Cardiology/American Heart Association guideline. Results: Over 12 years of follow-up in the derivation cohort with 21 320 Chinese participants, 1048 subjects developed a first ASCVD event. Sex-specific equations had C statistics of 0.794 (95% confidence interval, 0.775–0.814) for men and 0.811 (95% confidence interval, 0.787–0.835) for women. The predicted rates were similar to the observed rates, as indicated by a calibration χ 2 of 13.1 for men ( P =0.16) and 12.8 for women ( P =0.17). Good internal and external validations of our equations were achieved in subsequent analyses. Compared with the Chinese equations, the Pooled Cohort Equations had lower C statistics and much higher calibration χ 2 values in men. Conclusions: Our project developed effective tools with good performance for 10-year ASCVD risk prediction among a Chinese population that will help to improve the primary prevention and management of cardiovascular disease.
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