2026-10-13T05:36:44Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34022434 2026-09-29T10:00:02Z category_7 category_734 category_931 category_811 category_62 category_64 category_134 portal_87 item_type_6 month_year_09_2026

Identification and validation of a chronic kidney disease prediction model after radical nephrectomy: a multicenter cohort retrospective study Xinning Wang (2591230) Tianwei Zhang (169823) Jingxian Wang (4708384) Yongchao Yan (5714462) Haoyuan Wang (4179157) Kefan Song (16618731) Yongbo Yu (402271) Zhilei Zhang (1801453) Benkui Zou (21810722) Changlei Yao (9418069) Jianbo Zheng (502464) Jie Liu (15128) Hongli Liu (493057) Xuefeng Zhang (336127) Chen Guan (3108252) Lingyu Xu (8845568) Ningxin Zhang (366392) Wei Jiao (280619) Medicine Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Marine Biology Cancer Virology Renal cell carcinoma radical nephrectomy chronic kidney disease machine learning multicenter study retrospective study <p>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.</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. 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.</p> <p>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.</p> 2026-09-29T10:00:02Z Text Journal contribution 10.6084/m9.figshare.34022434.v1 https://figshare.com/articles/journal_contribution/Identification_and_validation_of_a_chronic_kidney_disease_prediction_model_after_radical_nephrectomy_a_multicenter_cohort_retrospective_study/34022434 CC BY 4.0