Identification and validation of a chronic kidney disease prediction model after radical nephrectomy: a multicenter cohort retrospective study

INTRODUCTION: Long-term renal function is an important follow-up after radical nephrectomy for renal carcinoma. Predictive models can help identify patients at risk for chronic kidney disease (CKD) progression and enable early intervention. METHODS: = 100) at a 7:3 ratio. An additional 320 patients from seven other centers constituted a multicenter external evaluation cohort. Five machine learning models were developed, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), confusion matrices, and calibration curves. RESULTS: 224 (34.51%) patients experienced postoperative CKD stage progression within three years. LightGBM achieved the highest discriminative performance among the five evaluated algorithms, with an AUC of 0.7508 (95% CI, 0.6399-0.8617) and an accuracy of 0.7200 in the internal validation set, and an AUC of 0.7549 (95% CI, 0.7022-0.8076) and an accuracy of 0.6813 in the external evaluation set. SHAP analysis identified preoperative eGFR, tumor size, post-to-preoperative serum creatinine ratio, preoperative serum creatinine, and age as the five most influential predictors. CONCLUSIONS: We developed and externally evaluated machine-learning models for predicting CKD stage progression after radical nephrectomy. Long-term decline in renal function is an important complication that requires urologists' attention and early prevention during follow-up.