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

Predictive models can help identify patients at risk for chronic kidney disease (CKD) progression and enable early intervention.</p> <p>We retrospectively analyzed 649 patients who underwent radical nephrectomy at eight medical centers. The primary cohort from the Affiliated Hospital of Qingdao University comprised 329 patients, randomly divided into training sets (<i>n</i> = 229) and internal validation sets (<i>n</i> = 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.</p> <p>224 (34.51%) patients experienced postoperative CKD stage progression within three years.