External Validation of a Predictive Model to Estimate Renal Function After Living Donor Nephrectomy

Area of Science:

• Nephrology
• Transplant Surgery
• Medical Statistics

Background:

• Living donor nephrectomy (LDN) is optimal for end-stage renal disease but raises concerns about donor renal function decline.
• Accurate prediction of post-donation renal function is crucial for living kidney donor safety.

Purpose of the Study:

• To externally validate a predictive model for 1-year postdonation estimated glomerular filtration rate (eGFR).
• To assess the model's accuracy in predicting the risk of chronic kidney disease (CKD) in living kidney donors.

Main Methods:

• Retrospective analysis of LDN cases from January 2006 to May 2018.
• Comparison of observed 1-year post-LDN eGFR (CKD-EPI formula) with predicted eGFR using a Toulouse-Rangueil developed formula (based on predonation eGFR and age).
• Statistical assessment using Pearson correlation, ROC curves, and calibration curves to evaluate external validity.

Main Results:

• A significant correlation (Pearson r=0.66, P<0.001) and concordance were found between predicted and observed 1-year eGFR.
• The model demonstrated high accuracy in predicting postoperative eGFR, with an Area Under the ROC Curve of 0.86 (95% CI, 0.82-0.89).
• Four hundred donors were evaluated, with a mean postoperative eGFR of 62.1 ± 14 mL/min/1.73m².

Conclusions:

• The study successfully externally validated a formula for predicting 1-year postdonation eGFR in living kidney donors.
• The validated calculator serves as an accurate tool for improving the selection process of living kidney donor candidates.
• This predictive model can enhance donor safety by identifying individuals at higher risk for renal function decline or CKD.