| Objectives and significance:Chronic kidney disease(CKD)and rapid kidney function decline(RKFD)were the independent risk factors of the cardiovascular disease(CVD),they were associated with the poor prognosis of atherosclerotic cardiovascular disease(ASCVD).Meanwhile,high CVD risk individuals are often combined with a variety of risk factors leading to kidney dysfunction,often need to take hypoglycemic drugs or anticoagulants and other drugs,increasing the burden on the kidney.Early warning of kidney dysfunction in high-risk population of CVD could be helpful to guide drugs therapy and provide personalized risk factors control strategies.However,because of the asymptomatic characteristic,it is expected to identify high-risk individuals by establishing a risk prediction model.However,the existing models have some problems,such as incomplete coverage of the elderly population,many prediction factors,inconvenient use and so on.This study intends to construct reliable and available prediction models of kidney dysfunction based on the cohort of high CVD risk population,which may be helpful to early identify high-risk individuals,reduce the occurrence of kidney dysfunction,and thus reduce the incidence of ASCVD events.Methods:An observational,ambispective cohort study was conducted among participants who underwent annual community physical examination in Zengcheng district,Guangzhou,Guangdong between January 2015 and December 2020.The health examination data of residents from January 2015 to December 2019 were collected retrospectively,and the high-risk population of cardiovascular disease in this area were followed up and prospectively collected in 2020.Endpoints were the incident chronic kidney disease or rapid kidney function decline.CKD was defined as estimated glomerular filtration rate(eGFR)<60mL/min/1.73 m2 during follow-up period.RKFD was defined as the reduction of eGFR≥40%during follow-up period.Cox regression analysis and stepwise approach were used to identify the risk factors.Nomograms based on predictors selected by multivariate COX regression analysis were developed,and discrimination and calibration were assessed.For validation,a bootstrap method(1000 times)was performed.And decision curve analysis was used to evaluate the clinic efficacy.Results:1.Risk prediction for incident CKD including 7723 participants.During the median follow-up period of 4.37 years,the incident of CKD was 16.860%(n=1302)in the entire cohort,16.87%(870 patients)in the development cohort and 16.84%(432 patients)in the validation cohort.Age,SBP,eGFR 60-89 mL/min/1.73m2,hemoglobin and diabetes were selected as predictors of CKD.The nomogram demonstrated a good discriminative power with AUCs of 0.841,0.853 and 0.873 in the development cohort for the 3-year,4-year,and 5-year,respectively.And the AUCs of the validation cohort were 0.842,0.846 and 0.834,respectively.Calibration plots demonstrated a good fitness between the observed and predicted risk in both cohorts.DCA for the CKD risk prediction model also shown a good clinical application value.The C-index of external validation cohort is 0.797.2.During the median follow-up period of 3.72 years,the incidence of RKFD was 11.96%(n=1011),11.98%(n=676)and 11.92%(n=335)in the entire cohort,development cohort and validation cohort,respectively.Age,eGFR,hemoglobin,systolic blood pressure,and diabetes were identified as predictors of significant KFD.The nomogram demonstrated a good discriminative power with the 5-year AUCs of 0.763 and 0.740 in the development and the validation cohor,respectively.Calibration plots demonstrated a good fitness between the observed and predicted risk in both cohorts.Decision curve analysis for the CKD risk prediction model also shown a good clinical application value.Conclusions:Nomograms to predict the risk of incident CKD or RKFD in the high CVD risk population were developed by using the readily available data in primary care.The prediction tool may help improving the health care strategies of these population. |
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