AimsTo develop a nomogram for incident chronic kidney disease (CKD) risk evaluation among community residents with high cardiovascular disease (CVD) risk.MethodsIn this retrospective cohort study, 5730 non-CKD residents with high CVD risk participating the National Basic Public Health Service between January 2015 and December 2020 in Guangzhou were included. Endpoint was incident CKD defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m2 during the follow-up period. The entire cohorts were randomly (2:1) assigned to a development cohort and a validation cohort. Predictors of incident CKD were selected by multivariable Cox regression and stepwise approach. A nomogram based on these predictors was developed and evaluated with concordance index (C-index) and area under curve (AUC).ResultsDuring the median follow-up period of 4.22 years, the incidence of CKD was 19.09% (n=1094) in the entire cohort, 19.03% (727 patients) in the development cohort and 19.21% (367 patients) in the validation cohort. Age, body mass index, eGFR 60–89 mL/min/1.73 m2, diabetes and hypertension were selected as predictors. The nomogram demonstrated a good discriminative power with C-index of 0.778 and 0.785 in the development and validation cohort. The 3-year, 4-year and 5-year AUCs were 0.817, 0.814 and 0.834 in the development cohort, and 0.830, 0.847 and 0.839 in the validation cohort.ConclusionOur nomogram based on five readily available predictors is a reliable tool to identify high-CVD risk patients at risk of incident CKD. This prediction model may help improving the healthcare strategies in primary care.
Background To develop a simple model to predict risk of rapid kidney function decline (RKFD) in population at risk of cardiovascular disease. Methods 8455 subjects aged ≥ 65 years or complicated diabetes or hypertension undergoing community annual health examinations between January 2015 and December 2020 were included. All participants were randomly assigned to a development cohort and a validation cohort in a 2:1 ratio. Rapid kidney function decline was defined as the reduction of estimated glomerular filtration rate (eGFR)≥40% during follow-up period. Cox regression analysis and stepwise approach were used to identify the risk factors. A nomogram based on these predictors was then developed, and discrimination, calibration and decision curve analysis were assessed. Results During the median follow-up period of 3.72 years, the incidence of rapid kidney function decline 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 for RKFD. The nomogram demonstrated a good discriminative power with the 5-year AUCs of 0.763 and 0.740 in the development and the validation cohort, respectively. Calibration plots also demonstrated a good fitness between the observed and predicted risk in both cohorts. Conclusions Risk stratification for rapid kidney function decline is achievable using a risk prediction nomogram based on clinical factors that are readily accessible in primary care. The utility of this nomogram in identifying individuals at high risk of RKFD in the community needs further investigation.
Background To develop a reliable model to predict rapid kidney function decline (RKFD) among population at risk of cardiovascular disease. Methods In this retrospective study, key monitoring residents including the elderly, and patients with hypertension or diabetes of China National Basic Public Health Service who underwent community annual physical examinations from January 2015 to December 2020 were included. Healthy records were extracted from regional chronic disease management platform. RKFD was defined as the reduction of estimated glomerular filtration rate (eGFR) ≥ 40% during follow-up period. The entire cohort were randomly assigned to a development cohort and a validation cohort in a 2:1 ratio. Cox regression analysis was used to identify the independent predictors. A nomogram was established based on the development cohort. The concordance index (C-index) and calibration plots were calculated. Decision curve analysis was applied to evaluate the clinical utility. Results A total of 8455 subjects were included. 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 for RKFD. Good discriminating performance was observed in both the development (C-index, 0.73) and the validation (C-index, 0.71) cohorts, and the AUCs for predicting 5-years RKFD was 0.763 and 0.740 in the development and the validation cohort, respectively. Decision curve analysis further confirmed the clinical utility of the nomogram. Conclusions Our nomogram based on five readily accessible variables (age, eGFR, hemoglobin, systolic blood pressure, and diabetes) is a useful tool to identify high risk patients for RKFD among population at risk of cardiovascular disease in primary care. Whereas, further external validations are needed before clinical generalization.
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