A Nomogram to Predict Significant Estimated Glomerular Filtration Rate Reduction After Robotic Partial Nephrectomy

Area of Science:

• Nephrology
• Urology
• Oncology

Background:

• Decreased kidney function after partial nephrectomy is linked to mortality.
• Robot-assisted partial nephrectomy (RAPN) is increasingly used for kidney cancer.
• Predicting renal function decline post-RAPN is crucial for patient outcomes.

Purpose of the Study:

• To develop a predictive model for ≥25% estimated glomerular filtration rate (eGFR) reduction after RAPN.
• To investigate the impact of acute kidney injury (AKI) on renal function post-RAPN.

Main Methods:

• A multi-institutional database of 999 patients undergoing RAPN was analyzed.
• A Cox survival model was used to build a nomogram predicting eGFR reduction.
• Key predictors included age, sex, comorbidities, baseline eGFR, RENAL score, and AKI status.

Main Results:

• The nomogram accurately predicted significant eGFR reduction (≥25%) within 3-15 months post-RAPN.
• Acute kidney injury (AKI) in patients with normal renal function (HR: 4.51) and AKI on chronic kidney disease (CKD) (HR: 4.90) were significant predictors.
• The model achieved a c-index of 73% with excellent calibration and clinical utility.

Conclusions:

• A validated nomogram can predict significant eGFR reduction after RAPN.
• This tool aids in identifying patients at high risk for renal function decline.
• Early risk stratification can inform personalized patient management strategies.