Kidney Function Decline Prediction and Risk Factor Analysis After Kidney Transplantation: Machine Learning and Network Analysis on Nationwide Cohort Data (Preprint)

BACKGROUND Early identification of graft loss risk and timely therapeutic intervention are crucial for preventing late renal allograft failure and improving long-term graft function. The one-year estimated glomerular filtration rate (eGFR) is the best predictor of long-term graft function in kidney transplant recipients; there is an increased risk of late graft failure in recipients with low one-year eGFR. OBJECTIVE To create a sparse model capable of predicting the one-year renal allograft dysfunction and to build a factor network suggesting risk control targets. METHODS Development data were constructed using the Korean Organ Transplant Registry (KOTRY), a national cohort data of 4317 recipients who underwent kidney transplantation between 2014 and 2019. The XGBoost algorithm was trained to predict the model outcome with 112 features, and the relevant factors were selected. The statistical significance of factors was calculated using multiple logistic regression for the development data. A factor correlation network was drawn and simplified by excluding spurious connections with LASSO (least absolute shrinkage and selection operator) regularization with EBIC (extended bayesian information criterium) model selection. The model outcome was one-year eGFR < 45 mL/min/1.73 m2, and model performance was measured using AUC, sensitivity, and specificity. A SHAP value plot was used to determine the feature importance of the model. The clinical importance of the model outcome was assessed using long-term graft survival and rejection-free survival. The factor network was built using inter-factor partial correlations and the statistical significance of each factor. RESULTS The model achieved an AUC of 0.82, a sensitivity of 0.8, and a specificity of 0.8 using seven pre- or peri-transplantation factors. Three pre-transplantation factors (donor age, recipient age, recipient-donor height difference) and four peri-transplantation factors (low eGFR at discharge, high eGFR at discharge, serum creatinine at discharge, post-transplantation stay) were chosen by the model. Model prediction was significantly associated with a five-year survival of graft and rejection-free survival (P = .02 and P = .007). Post-transplantation stay and discharge eGFR ≥ 88.0 were the most prominent risk and preventive nodes on the network, respectively. Donor age and discharge eGFR < 59.8 had a high impact on model prediction and could be effective risk control targets for their multiple connections to other risk nodes. CONCLUSIONS One-year renal allograft dysfunction could be predicted early after transplantation. The long-term outcomes of kidney transplantation might be improved by preemptive measures on donor age, kidney function at discharge, and post-transplantation stay. INTERNATIONAL REGISTERED REPORT RR2-doi: 10.1097/TXD.0000000000000678