Machine learning models for predicting acute kidney injury after pediatric living donor liver transplantation in biliary atresia

Rong-Rong Wang, Min Zhu, Heng-Chang Ren, Wen-Li Yu

BACKGROUND Living donor liver transplantation (LDLT) is an important treatment method for end-stage pediatric liver diseases, e.g. , biliary atresia (BA). Acute kidney injury (AKI) after transplantation is a common and serious complication in clinical practice that significantly influences patient mortality and survival rate. AIM To construct a clinical prediction model for AKI after pediatric LDLT based on machine learning (ML). METHODS This study included 340 children with BA who underwent LDLT at our center between December 2022 and December 2024. Complete clinical data were collected, including baseline characteristics, preoperative assessments, intraoperative variables, and postoperative recovery indicators. Least absolute shrinkage and selection operator regression was used for feature selection, and nine ML models were developed for model training and evaluation. After training on the training set, the predictive performance of each model was tested and compared. Finally, the best-performing model was interpreted and visualized using the SHapley Additive exPanations (SHAP) algorithm. RESULTS Excluding postoperative creatinine (Cr) levels, this study identified a total of six potential predictors associated with AKI after LDLT. The random forest model showed comprehensive and optimal predictive performance after 10-fold cross-validation, with an area under the curve of 0.875 (95% confidence interval: 0.805-0.944). In addition, the importance of predictors for AKI occurrence was ranked by SHAP analysis, and preoperative Cr level was identified as the most important predictor. CONCLUSION This study employed ML algorithms to construct a predictive model for early AKI following pediatric liver transplantation. The developed model is expected to assist doctors in performing timely treatment interventions, thereby reducing the occurrence of post-transplant complications and improve the survival time and quality of life in children undergoing liver transplantation.