Evidence map›Paper›PMID 42808223›Full record

ArticleAnnals of medicine2026

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

Xinning Wang, Tianwei Zhang, Jingxian Wang, Yongchao Yan, Haoyuan Wang, Kefan Song, Yongbo Yu, Zhilei Zhang, Benkui Zou, Changlei Yao and 8 more

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
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Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

18 authors.

Xinning WangDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Tianwei ZhangDepartment of Urology, Graduate School of Medicine, Juntendo University, Tokyo, Japan.
Jingxian WangDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Yongchao YanDepartment of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Haoyuan WangDepartment of Urology, Qilu Hospital of Shandong University, Jinan, China.
Kefan SongDepartment of Urology, Qilu Hospital of Shandong University, Jinan, China.
Yongbo YuDepartment of Urology, Weifang People's Hospital, Weifang, China.
Zhilei ZhangDepartment of Urology, Weifang People's Hospital, Weifang, China.
Benkui ZouDepartment of Urology, Cancer Hospital of Shandong First Medical University, Jinan, China.
Changlei YaoDepartment of Urology, People's Hospital of Rizhao, Rizhao, China.
Jianbo ZhengDepartment of Urology, Zibo Central Hospital, Zibo, China.
Jie LiuDepartment of Urology, Linyi People's Hospital, Linyi, China.
Hongli LiuSchool of Clinical Medicine, Shandong Second Medical University, Weifang, China.
Xuefeng ZhangDepartment of Urology, Weihai Central Hospital, Weihai, China.
Chen GuanDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Lingyu XuDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Ningxin ZhangDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Wei JiaoDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.ORCID 0000-0001-9102-4478

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLong-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.

methodsWe 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 (

results224 (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.

conclusionsWe 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.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsMachine LearningNephrectomyPostoperative ComplicationsRenal Insufficiency, ChronicAgedBoosting Machine Learning AlgorithmsChinaDisease ProgressionFemaleGlomerular Filtration RateHumansLogistic ModelsMaleMiddle Agedchronic kidney diseasemachine learningmulticenter studyradical nephrectomyRenal cell carcinomaretrospective study

Identifiers

PMID42808223
PMCPMC13629762

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