Objectives Chronic kidney disease (CKD) poses a significant public health burden. This study aimed to evaluate the associations between clinical laboratory indices and CKD and to develop prediction and prognostic models for CKD risk assessment and disease progression. Design & Methods Between January 2008 and June 2018, we enrolled 500 healthy controls, 445 patients with early-stage CKD (G1–G2), and 527 patients with CKD G5 at the First Hospital of China Medical University. Logistic regression analyses were performed to identify independent predictors for the presence of CKD and progression to advanced disease, which were subsequently incorporated into visual nomograms. Model performance was evaluated using area under the receiver operating characteristic curves (AUC) and calibration plots. Clinical utility was assessed using decision curve analysis (DCA) and clinical impact curves (CIC). Results The early-stage CKD prediction nomogram achieved an AUC of 0.981 in the training set and 0.969 in the validation set. The progression nomogram demonstrated AUC values of 0.984 and 0.972 in the training and validation sets, respectively. DCA and CIC analyses further confirmed the clinical relevance and potential applicability of both models. Conclusions We developed and validated early-stage prediction and progression assessment for CKD, demonstrating high discriminative ability, good calibration, and significant clinical utility. These models may facilitate early detection and dynamic risk assessment in CKD management.
Wan et al. (Mon,) studied this question.