Objective To investigate the proportion of high-risk population for cardiovascular diseases(CVD)among residents at ages of 35 to 79 in Nanjing City,and establish a prediction model of high-risk population for CVD.Meth-ods Residents at ages of 35 to 79 years were selected from Nanjing City using a multi-stage stratified cluster random sampling method from 2020 to 2021.Participants'demographic information,characteristics of lifestyle and blood biochem-ical index were collected using questionnaire surveys,physical examination and laboratory testing.The high-risk popula-tion for CVD were determined according to the Chinese Guidelines for Cardiovascular Disease Risk Assessment and Management,and the Chinese Guidelines for the Prevention and Treatment of Adult Dyslipidemia(2016 Revision).Pre-dictive factors for high-risk population for CVD were screened using a multivariable logistic regression model.A nomo-gram was established and verified with receiver operation characteristic(ROC)curve.Hosmer-Lemeshow goodness of fit test was used to evaluate the fitting effect and Bootstrap method was used for internal verification.Results A total of 38 428 individuals were surveyed,including 17 970 males(46.76％)and 20 458 females(53.24％),and 25 714 individ-uals aged 35 to 59 years.There were 8 905 high-risk population for CVD,with a detection rate of 23.17％.Multivari-able logistic regression analysis identified 9 factors affecting high-risk population for CVD.A prediction model was es-tablished for ln[P/(1-P)]=-7.305+2.107×age-0.366×gender+0.299×marital status-0.297×educational level+0.631×body mass index+0.013×sleep duration+0.096×edible salt intake+0.444×smoke-0.069×alcohol consumption.The area under ROC curve was 0.799(95％CI:0.794-0.805),the sensitivity and specificity were 0.731 and 0.753,indicating good differentia-tion.The nomogram based on the above factors indicated good calibration and stability.Conclusion The nomogram con-structed by age,gender,marital status,educational level,body mass index,sleep duration,edible salt intake,smoking and alcohol consumption can be used to predict high-risk population for CVD.
万方数据
维普
