Prediction of Recipient Renal Function in Living Donor Kidney Transplantation Using Baseline Characteristics and Donor Renal Volume
Conditions: Renal Transplanted Recipients, Kidney Function
Sponsor: Seoul National University Hospital

Official Title: Prediction of Recipient Renal Function in Living Donor Kidney Transplantation 
Using Baseline Characteristics and Donor Renal Volume 
 
ClinicalTrials.gov NCT Number: 
 
Document Date: December 2, 2024 
 
 

1. Purpose and Background 
Kidney transplantation is the definitive treatment for end-stage renal disease (ESRD). Accurately 
predicting early post-transplant kidney function is critical for patient management and improving 
long-term outcomes. This study aims to develop a machine learning-based predictive model using 
baseline characteristics of recipients and donors, with a focus on donor kidney volume, to predict the 
lowest serum creatinine level within one year post-transplant. 
 
2. Study Design 
 
Study Type: Retrospective Cohort Study 
 
Duration: January 2006- March 2023 
 
Setting: Multicenter study conducted at Seoul National University Hospital, Severance 
Hospital, and Bundang Seoul National University Hospital. 
 
Participants: Living-donor kidney transplant recipients and their corresponding donors. 
 
3. Eligibility Criteria 
Inclusion Criteria: 
 
Living-donor kidney transplant recipients and donors from the above centers. 
Exclusion Criteria: 
 
Recipients or donors with follow-up duration less than 1 year. 
 
Recipients younger than 18 years old. 
 
Re-transplantation cases or recipients with simultaneous multi-organ transplantation. 
 
4. Data Collection and Management Plan 
 
Data was retrieved from hospital records and centralized in a unified dataset. 
 
Variables included demographic data, donor kidney volume (total and cortex), recipient-
donor compatibility, and baseline laboratory data. 

 
5. Primary and Secondary Outcomes 
 
Primary Outcome: Lowest serum creatinine level within one year post-transplant. 
 
Secondary Outcomes: Predictive performance metrics (MAE, RMSE, R²), donor kidney 
volume correlations with recipient eGFR, and subgroup analyses by donor type. 
 
6. Statistical Analysis 
6.1 Statistical Software 
All statistical analyses and data preprocessing will be conducted using the following software tools: 
1. R Software (Version 4.4.1): 
o 
Primary software for statistical analyses, including descriptive statistics, hypothesis 
testing, and data visualization. 
2. Python (Version 3.9): 
o 
Used for developing and validating machine learning models. 
 
6.2 Predictive Model Development 
Algorithms Used: 
 
Multiple Linear Regression (with and without variable selection) 
 
Elastic Net 
 
Generalized Additive Model (GAM) 
 
Random Forest 
 
Gradient Boosting (XGBoost) 
Variable Selection Methods: 
 
Multiple Linear Regression: AIC-based stepwise selection. 
 
Elastic Net: LASSO regularization. 
 
GAM: Cross-validation for smoothing parameter selection. 

 
Tree-based models (Random Forest, Gradient Boosting, XGBoost): Feature importance 
evaluation based on cross-validation. 
Hyperparameter Tuning: 
 
Grid search with 5-fold cross-validation will be used to optimize hyperparameters for 
machine learning models. 
 
6.3 Model Evaluation 
Performance Metrics: 
1. Mean Absolute Error (MAE): 
MAE=
1
n ∑
|yi
n
i=1
−ŷi |  
yi: Actual value          ŷi: Predicted value      n=Number of observations  
2. Root Mean Square Error (RMSE): 
RMSE= √1
n ∑
(yi −ŷi)2
n
i=1
   
 yi: Actual value         ŷi: Predicted value      n=Number of observations  
3. R-squared (R²) : 
R2 = 1 − 
SSres
SS tot 
SSres=∑
(yi −ŷi)2
n
i=1
: Residual Sum of Square   
SStot=∑
|yi
n
i=1
−y̅i |: Total Sum of Squares 
Internal Validation: 
 
Models will be evaluated using the internal validation set for all performance metrics. 
External Validation: 

 
The final model will be tested on the external validation set to assess generalizability. 
 
6.4 Subgroup Analysis 
 
Subgroups based on recipient characteristics (e.g., sex, age, baseline BMI) and donor 
variables (e.g., kidney volume) will be analyzed to evaluate heterogeneity in model 
performance. 
 
6.5 Feature Importance Analysis 
 
Feature importance scores will be derived from tree-based models (Random Forest, 
XGBoost) to identify the most influential predictive variables. 
 
7. Ethical Considerations 
 
Approved by the SNUH Institutional Review Board (IRB No. H-2205-051-1322). 
 
Study participants provided informed consent for data usage.