We developed a machine-learning-based model that could predict a decrease in one-year graft function after kidney transplantation, and investigated the risk factors of the decreased function. A total of 4317 cases were included from the Korean Organ Transplant Registry (2014−2019). An XGBoost model was trained to predict the recipient’s one-year estimated glomerular filtration rate (eGFR) below 45 mL/min/1.73 m2 using 112 pre- and peri-transplantation variables. The network of model factors was drawn using inter-factor partial correlations and the statistical significance of each factor. The model with seven features achieved an area under the curve of 0.82, sensitivity of 0.73, and specificity of 0.79. The model prediction was associated with five-year graft and rejection-free survival. Post-transplantation hospitalization >25 days and eGFR ≥ 88.0 were the prominent risk and preventive factors, respectively. Donor age and post-transplantation eGFR < 59.8 were connected to multiple risk factors on the network. Therefore, careful donor−recipient matching in older donors, and avoiding pre-transplantation risk factors, would reduce the risk of graft dysfunction. The model might improve long-term graft outcomes by supporting early detection of graft dysfunction, and proactive risk factor control.
Keywords: graft survival; kidney transplantation; machine learning; risk factors.