Development and Internal Validation of a Nomogram for Predicting Renal Function after Partial Nephrectomy.

Area of Science:

• Nephrology
• Urologic Oncology
• Medical Informatics

Background:

• Partial nephrectomy (PN) is a standard treatment for renal tumors.
• Loss of renal function post-PN is a significant clinical concern.
• Predictive tools for renal function decline after PN are needed.

Purpose of the Study:

• To develop and validate a predictive model for loss of renal function after PN.
• To identify key factors contributing to renal function decline post-PN.
• To create a nomogram for risk stratification.

Main Methods:

• Retrospective analysis of 1897 patients undergoing PN with warm ischemia.
• Definition of loss of renal function as eGFR upstaging at 3 months post-PN.
• Multivariable modeling including age, sex, BMI, baseline eGFR, RENAL score, and ischemia time.
• Internal validation using interval validation, calibration plots, ROC curves, and leave-one-out cross-validation.

Main Results:

• The developed nomogram achieved an area under the ROC curve of 76% after internal validation.
• The model demonstrated excellent calibration.
• At a 27% probability cutoff, the model predicted renal function upstaging with an 86% positive predictive value.

Conclusions:

• A validated model incorporating patient characteristics and ischemia time can predict renal function loss after PN.
• This tool aids in the early identification of high-risk patients.
• The model offers good predictive power for renal function decline post-PN.