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Related Experiment Video

Updated: Jun 30, 2025

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Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell

Seol Whan Oh1,2, Seok-Soo Byun3, Jung Kwon Kim3

  • 1Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 06591, Seoul, Korea.

BMC Medical Informatics and Decision Making
|March 23, 2024
PubMed
Summary
This summary is machine-generated.

A new model predicts chronic kidney disease (CKD) after kidney cancer surgery. This tool helps manage patient risk and improve outcomes using key factors like preoperative eGFR and tumor size.

Keywords:
Chronic kidney diseaseGradient boostKOrean Renal Cell CarcinomaMachine learningRenal cell carcinoma

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Area of Science:

  • Nephrology
  • Oncology
  • Medical Informatics

Background:

  • Patients with renal cell carcinoma (RCC) face increased chronic kidney disease (CKD) risk post-nephrectomy.
  • Postoperative renal function monitoring and intervention are crucial.
  • A predictive tool for CKD onset is vital for patient management.

Purpose of the Study:

  • To develop a machine learning model for predicting CKD after RCC surgery.
  • To identify key predictors of post-nephrectomy CKD.

Main Methods:

  • Utilized data from 4389 RCC patients across eight Korean hospitals (KORCC database).
  • Trained and evaluated nine machine learning models to predict CKD occurrence.
  • Selected the best model based on AUROC and validated variable importance using SHAP and Kaplan-Meier analyses.

Main Results:

  • The gradient boost algorithm achieved the highest performance with an AUROC of 0.826.
  • Preoperative eGFR, albumin levels, and tumor size were identified as significant predictors of post-surgical CKD.

Conclusions:

  • A novel predictive model for post-surgical CKD in RCC patients was developed.
  • This quantitative tool aids in assessing CKD risk, enabling personalized postoperative care and improved patient prognosis.