Purpose: To develop and validate a web-based nomogram for predicting new incident chronic kidney disease (CKD) within 4 years in a cohort undergoing routine physical examination from two health examination centers.
Methods: One center was utilized for training and internal validation of a nomogram model involving 6515 patients, while a separate center was employed for external validation with 3152 patients. Sixteen candidate predictors, including patient demographics, medical histories, physical examination, and laboratory test data, were included in this study to ascertain factors linked to new incident CKD. A nomogram was created to predict CKD risks using a logistic model. The nomogram's performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis.
Results: Out of the 9667 healthy individuals included in the study with mean age of 46 years, sex ratio (male/female) of 1.69 (6075/3592), 118 (2.59%), 51 (2.61%), and 60 (1.90%) individuals developed CKD in the training (n = 4563), internal validation (n = 1952), and external validation (n = 3152) datasets, respectively. Age, history of diabetes mellitus, systolic blood pressure, serum creatinine, albumin, and triglyceride levels were used to build the nomogram, which yielded excellent discrimination ability (training cohort, AUC = 0.8806, 95% confidence interval [CI] 0.8472-0.9141; internal validation cohort, AUC = 0.8506, 95% CI 0.7856-0.9156; external validation cohort, AUC = 0.9183, 95% CI 0.8698-0.9669). We further developed a web-based calculator for convenient application (https://luochuxuan.shinyapps.io/dynnomapp/).
Conclusion: Our web-based nomogram accurately predicted CKD risks in Chinese health individuals and can be easily used in clinical settings.
Keywords: Chronic kidney disease; early detection; health examination; nomogram; prediction model.