Prediction models and nomograms of 3-year risk of chronic kidney disease in China: a study from the Shanghai Suburban Adult Cohort and Biobank (2016–2020)

BACKGROUND: Chronic kidney disease (CKD) is a serious public health problem in China that requires the development and verification of sex-specific 3-year risk prediction models and nomograms of CKD to further guide personalized care. METHODS: A 3-year community-based observational cohort study of 10,049 Chinese participants without CKD was begun in 2016 and participants were followed until August 2020. Stepwise multivariable-adjusted Cox regression analyses were conducted to select the candidate variables, including demographics and clinical parameters such as blood urea nitrogen (BUN) and estimated glomerular filtration rate (eGFR), into the prediction model. We used the C-statistic to evaluate discrimination, and the Brier score for calibration. A 10-fold cross-validation was conducted for internal validation to assess the model’s stability. RESULTS: The cumulative incidence of CKD was 4.25% (male: 3.81%, female: 4.55%). The eGFR, HbA1c variability, uric acid (UA), UA variability, BUN, albumin, and Hb were significant predictors for both sexes. In the female model, age, triglycerides and age at menarche were additional predictors. The models showed C-statistics of 0.934/0.951 (male/female). The model calibrated well across the deciles of predicted risk, with a Brier score of 0.007/0.009 (male/female). CONCLUSIONS: In this study, we fitted the CKD 3-year risk prediction models with an accuracy rate of >90%. At the same time, we developed two nomograms to facilitate routine CKD risk prediction to provide individualized care in preventing or delaying CKD.