title: A Novel Risk Score to the Prediction of 10-year Risk for Coronary Artery Disease Among the Elderly in Beijing Based on Competing Risk Model.
abstract: The study aimed to construct a risk prediction model for coronary artery disease (CAD) based on competing risk model among the elderly in Beijing and develop a user-friendly CAD risk score tool. We used competing risk model to evaluate the risk of developing a first CAD event. On the basis of the risk factors that were included in the competing risk model, we constructed the CAD risk prediction model with Cox proportional hazard model. Time-dependent receiver operating characteristic (ROC) curve and time-dependent area under the ROC curve (AUC) were used to evaluate the discrimination ability of the both methods. Calibration plots were applied to assess the calibration ability and adjusted for the competing risk of non-CAD death. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to quantify the improvement contributed by the new risk factors. Internal validation of predictive accuracy was performed using 1000 times of bootstrap re-sampling. Of the 1775 participants without CAD at baseline, 473 incident cases of CAD were documented for a 20-year follow-up. Time-dependent AUCs for men and women at t = 10 years were 0.841 [95% confidence interval (95% CI): 0.806-0.877], 0.804 (95% CI: 0.768-0.839) in Fine and Gray model, 0.784 (95% CI: 0.738-0.830), 0.733 (95% CI: 0.692-0.775) in Cox proportional hazard model. The competing risk model was significantly superior to Cox proportional hazard model on discrimination and calibration. The cut-off values of the risk score that marked the difference between low-risk and high-risk patients were 34 points for men and 30 points for women, which have good sensitivity and specificity. A sex-specific multivariable risk factor algorithm-based competing risk model has been developed on the basis of an elderly Chinese cohort, which could be applied to predict an individual's risk and provide a useful guide to identify the groups at a high risk for CAD among the Chinese adults over 55 years old.
authors.affiliation: From the Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China (LL, YL, JG, HL, XL, LT, AY, XG); Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China (LL, YL, JG, HL, XL, LT, XG); Xuan Wu Hospital, Capital Medical University, Beijing, China (ZT); The Graduate Entry Medical School, University of Limerick, Limerick, Ireland (XL); and Beijing Municipal Science and Technology Commission, Beijing, China (AY).
authors.forename: Long
authors.initials: L
authors.lastname: Liu
authors.forename: Zhe
authors.initials: Z
authors.lastname: Tang
authors.forename: Xia
authors.initials: X
authors.lastname: Li
authors.forename: Yanxia
authors.initials: Y
authors.lastname: Luo
authors.forename: Jin
authors.initials: J
authors.lastname: Guo
authors.forename: Haibin
authors.initials: H
authors.lastname: Li
authors.forename: Xiangtong
authors.initials: X
authors.lastname: Liu
authors.forename: Lixin
authors.initials: L
authors.lastname: Tao
authors.forename: Aoshuang
authors.initials: A
authors.lastname: Yan
authors.forename: Xiuhua
authors.initials: X
authors.lastname: Guo
mesh_terms: D000368:Aged; D019540:Area Under Curve; D000068476:Beijing / Q000453:epidemiology; D003324:Coronary Artery Disease / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity