Predictive model of 1-year postoperative renal function after living donor nephrectomy

Area of Science:

• Nephrology
• Transplant Surgery
• Urology

Background:

• Living donor nephrectomy (LDN) is crucial for end-stage renal disease treatment.
• Donor renal function decline post-nephrectomy is a concern.
• Accurate prediction of postoperative kidney function is needed.

Purpose of the Study:

• Evaluate factors predicting 1-year postoperative estimated glomerular filtration rate (eGFR) after LDN.
• Develop and validate a predictive model for postoperative eGFR.

Main Methods:

• Retrospective analysis of 202 laparoscopic live donor nephrectomy (LLDN) cases.
• Multivariate regression identified predictors in a training set.
• Internal validation of the predictive model using a validation set and ROC curves.

Main Results:

• Preoperative eGFR and age were independent predictors of 1-year postoperative eGFR.
• The predictive model showed strong correlation (Pearson r=0.70 training, 0.65 validation).
• The model demonstrated high accuracy (AUC=0.89 training, 0.83 validation).

Conclusions:

• Preoperative eGFR and age are key predictors of post-LDN kidney function.
• The developed predictive model is an accurate tool for LDN candidate selection.
• Improved donor selection can optimize outcomes in kidney transplantation.