Predicting short- and long-term renal function following partial and radical nephrectomy.

Area of Science:

• Nephrology
• Urology
• Surgical Oncology

Background:

• Renal function prediction is crucial for kidney cancer surgery outcomes.
• Existing models like the Mayo Clinic model focus on short-term outcomes.
• Long-term renal function prediction after nephrectomy requires further investigation.

Purpose of the Study:

• To externally validate the Mayo Clinic model for predicting early postoperative renal failure.
• To develop novel models for predicting long-term renal function after partial nephrectomy (PN) and radical nephrectomy (RN).
• To identify key patient and tumor factors influencing long-term kidney function post-nephrectomy.

Main Methods:

• Retrospective cohort study of patients undergoing PN or RN for unilateral renal tumors (2003-2019).
• External validation of the Mayo Clinic model using Area Under the Receiver Operating Characteristics Curve (AUC).
• Development of long-term eGFR prediction models using hierarchical linear mixed models.

Main Results:

• The Mayo Clinic model demonstrated good accuracy for predicting early renal failure (AUCs 0.816 for PN, 0.825 for RN).
• Long-term eGFR after PN was associated with age, diabetes, solitary kidney, tumor diameter, and preoperative eGFR.
• Long-term eGFR after RN was associated with age, BMI, RENAL nephrometry score, and preoperative eGFR.

Conclusions:

• The Mayo Clinic model shows external validity for short-term renal failure prediction.
• Developed long-term eGFR prediction models rely primarily on patient factors rather than tumor complexity.
• These models can assist in clinical decision-making regarding PN versus RN.