Application of machine learning models in predicting renal function decline following robot-assisted partial nephrectomy

Li Jing; Wang Linfeng; Zhang Gaojie; Huang Yong; Gao Yingying; Sun Rui; Cao Yang; Li Qiuchen; He Hao; Wei Ziling; Liu Jiayu

doi:10.13406/j.cnki.cyxb.003799

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Application of machine learning models in predicting renal function decline following robot-assisted partial nephrectomy

Neurogenic Lower Urinary Tract Dysfunction and Pelvic Floor Functional Restoration Column | 更新时间：2025-10-22

- Application of machine learning models in predicting renal function decline following robot-assisted partial nephrectomy
- Journal of Chongqing Medical University Vol. 50, Issue 4, Pages: 457-462(2025)
- 作者机构：
  
  1.重庆医科大学第一临床学院，重庆 401331
  2.重庆医科大学附属第一医院泌尿外科，重庆 400016
  3.重庆医科大学附属巴南医院检验科，重庆 401320
- 作者简介：
- 基金信息：
- DOI：10.13406/j.cnki.cyxb.003799
  CLC： R737.11
- Received：19 February 2025，
  
  Revised：2025-03-06，
  
  Accepted：10 March 2025，
  
  Published：28 April 2025
- 稿件说明：
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李静,王林峰,张高杰,等.机器学习模型预测机器人辅助肾部分切除术后肾功能减退[J].重庆医科大学学报,2025,50(4):457-462.

Li Jing,Wang Linfeng,Zhang Gaojie,et al.Application of machine learning models in predicting renal function decline following robot-assisted partial nephrectomy[J].Journal of Chongqing Medical University,2025,50(4):457-462.
李静,王林峰,张高杰,等.机器学习模型预测机器人辅助肾部分切除术后肾功能减退[J].重庆医科大学学报,2025,50(4):457-462. DOI： 10.13406/j.cnki.cyxb.003799.

Li Jing,Wang Linfeng,Zhang Gaojie,et al.Application of machine learning models in predicting renal function decline following robot-assisted partial nephrectomy[J].Journal of Chongqing Medical University,2025,50(4):457-462. DOI： 10.13406/j.cnki.cyxb.003799.

摘要

目的

探讨多种机器学习模型预测机器人辅助肾部分切除术（robot-assisted partial nephrectomy，RAPN）后肾功能减退的效能，为临床风险分层提供依据。

方法

回顾性纳入2019年1月至2023年12月重庆医科大学附属第一医院泌尿外科733例肾细胞癌（renal cell carcinoma，RCC）行RAPN患者的临床数据，整合人口学特征、实验室指标及围手术期参数，构建7种机器学习模型，采用Shapley加性解释（Shapley additive explanations，SHAP）方法解析关键预测因子，并通过受试者工作特征曲线下面积（receiver operating characteristic curve area under the curve，ROC-AUC）评估模型性能。

结果

随机森林模型预测效能最优（AUC=0.84）。SHAP分析显示，中性粒细胞/淋巴细胞比值、肿瘤直径、凝血酶原时间国际标准化比值、白细胞计数及术中出血量等因素对术后肾功能减退有明显影响。

结论

本研究为临床提供了一种潜在的预测工具，可帮助识别高风险患者并优化术后管理策略。

Abstract

Objective

To compare the efficacy of various machine learning models in predicting renal function decline after robot-assisted partial nephrectomy（RAPN），and to provide evidence for clinical risk stratification.

Methods

This study retrospectively included the clinical data of 733 patients with renal cell carcinoma undergoing RAPN at the Urology Department of The First Affiliated Hospital of Chongqing Medical University from January 2019 to December 2023. Demographic characteristics，laboratory indicators，and perioperative parameters were integrated to construct seven machine learning models. Key predictors were interpreted using Shapley additive explanations（SHAP）. Model performance was evaluated using the area under the receiver operating characteristic curve （AUC）.

Results

The random forest model demonstrated the best predictive performance（AUC=0.84）. SHAP analysis identified neutrophil-to-lymphocyte ratio，tumor diameter，the international normalized ratio of prothrombin time，white blood cell count，and intraoperative blood loss as significant factors influencing postoperative renal function decline.

Conclusion

This study provides a potential predictive tool for clinical practice，aiding in identifying high-risk patients and optimizing postoperative management strategies.

关键词

Keywords

references

Bhatt JR ， Finelli A . Landmarks in the diagnosis and treatment of renal cell carcinoma [J ] . Nat Rev Urol ， 2014 ， 11 （ 9 ）： 517 - 525 ．

Charytoniuk T ， Małyszko M ， Bączek J ， et al . Progression to chronic kidney disease in patients undergoing nephrectomy for small renal masses：a price to pay for a therapeutic success? [J ] . Postgrad Med ， 2018 ， 130 （ 7 ）： 613 - 620 ．

Tantisattamo E ， Dafoe DC ， Reddy UG ， et al . Current management of patients with acquired solitary kidney [J ] . Kidney Int Rep ， 2019 ， 4 （ 9 ）： 1205 - 1218 ．

邹湘鹏，宁　康，张志凌，等 . 单中心1 367例根治性肾切除肾癌患者的远期生存分析 [J ] . 中华肿瘤杂志， 2023 ， 45 （ 11 ）： 981 - 987 ．

Zou XP ， Ning K ， Zhang ZL ， et al . Long-term survival analysis of 1 367 patients treated with radical nephrectomy from a single center [J ] . Chin J Oncol ， 2023 ， 45 （ 11 ）： 981 - 987 ．

Babbar P ， Hemal AK . Robot-assisted partial nephrectomy：current status，techniques，and future directions [J ] . Int Urol Nephrol ， 2012 ， 44 （ 1 ）： 99 - 109 ．

Huang L ， Wang JQ . A meta-analysis of perioperative，oncological，and functional outcomes of robot-assisted partial nephrectomy for completely endophytic and hilar lesions [J ] . Int Urol Nephrol ， 2025 ， 11 （ 2 ）： 112 - 121 ．

Liu S ， Zhang B ， Weng B ， et al . Comparison of robotic-assisted and laparoscopic partial nephrectomy based on the PADUA score and the predictive value of the PADUA score and the Mayo Adhesive Probability score for postoperative complications：a single-center retrospective study [J ] . J Cancer Res Clin Oncol ， 2024 ， 151 （ 1 ）： 1 ．

Mansoor S ， Hamid S ， Tuan TT ， et al . Advance computational tools for multiomics data learning [J ] . Biotechnol Adv ， 2024 ， 77 ： 108447 ．

Magro MC ， Vattimo MD . Impact of cystatin C and RIFLE on renal function assessment after cardiac surgery [J ] . Biol Res Nurs ， 2013 ， 15 （ 4 ）： 451 - 458 ．

刘彩虹， Koyner Jay ，赵宇亮，等 . 《第28届急性疾病质量倡议工作组共识报告：脓毒症相关急性肾损伤》解读 [J ] . 中华肾脏病杂志， 2024 ， 40 （ 3 ）： 123 - 134 ．

Liu CH ， Koyner J ， Zhao YL ， et al . Interpretation for “sepsis associated acute kidney injury：consensus report of the 28th Acute Disease Quality Initiative Workgroup ”[J ] . Chin J Nephrol ， 2024 ， 40 （ 3 ）： 123 - 134 ．

Bellin MF ， Valente C ， Bekdache O ， et al . Update on renal cell carcinoma diagnosis with novel imaging approaches [J ] . Cancers ， 2024 ， 16 （ 10 ）： 1926 ．

Ljungberg B ， Albiges L ， Abu-Ghanem Y ， et al . European association of urology guidelines on renal cell carcinoma：the 2022 update [J ] . Eur Urol ， 2022 ， 82 （ 4 ）： 399 - 410 ．

中国医促会泌尿健康促进分会，中国研究型医院学会泌尿外科学专业委员会 . 肾部分切除术安全共识 [J ] . 现代泌尿外科杂志， 2020 ， 25 （ 6 ）： 474 - 481

Chinese Association for the Promotion of Healthcare Urology Branch ， Chinese Research Hospital Association Urology Professional Committee . Safety consensus on partial nephrectomy [J ] . Journal of Modern Urology ， 2020 ， 25 （ 6 ）： 474 - 481，500

Lefrère A ， Burtey S ， Bobot S ， et al . Depression in chronic kidney disease：Particularities，specific mechanisms and therapeutic considerations，a narrative review [J ] . Behav Brain Res ， 2025 ， 483 ： 115467 ．

Albisinni S ， Pretot D ， Al Hajj Obeid W ， et al . The impact of neutrophil-to-lymphocyte，platelet-to-lymphocyte and haemoglobin-to-platelet ratio on localised renal cell carcinoma oncologic outcomes [J ] . Prog Urol ， 2019 ， 29 （ 8/9 ）： 423 - 431 ．

李泽宇，柳　豪，黄垣堤，等 . 脂代谢相关基因ABHD5抑制肾癌细胞体外迁移及侵袭 [J ] . 重庆医科大学学报， 2024 ， 49 （ 6 ）： 701 - 706 .

Li ZY ， Liu H ， Huang YD ， et al . Lipid metabolism-related gene ABHD5 inhibits renal cancer cell migration and invasion in vitro [J ] . J Chongqing Med Univ ， 2024 ， 49 （ 6 ）： 701 - 706 .

Christiansen CF ， Schmidt M ， Lamberg AL ， et al . Kidney disease and risk of venous thromboembolism：a nationwide population-based case-control study [J ] . J Thromb Haemost ， 2014 ， 12 （ 9 ）： 1449 - 1454 ．

Zhang ZL ， Zhao JP ， Dong W ， et al . Acute kidney injury after partial nephrectomy：role of parenchymal mass reduction and ischemia and impact on subsequent functional recovery [J ] . Eur Urol ， 2016 ， 69 （ 4 ）： 745 - 752 ．

Takagi T ， Mir MC ， Sharma N ， et al . Compensatory hypertrophy after partial and radical nephrectomy in adults [J ] . J Urol ， 2014 ， 192 （ 6 ）： 1612 - 1618 ．

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