Explainable machine learning for the assessment of donor grafts in liver transplantation - Zhixing, Liang; Linsen, Ye; Peng, Jiang; Siyi, Dong; Haoyuan, Yu; Kun, Li; Siqi, Li; Yongwei, Hu; Mingshen, Zhang; Wei, Liu; Hua, Li; Shuhong, Yi; et al. (Hepatology research, 2025)
The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision-making process for clinical application. Data from 5242 donor-recipient pairs who underwent liver transplantation (LT) at the top 10 liver transplant centers in China (January 2017 to December 2022) were collected. The dataset was divided into training and validation sets. Sixty-three variables, including demographics, donor characteristics, diagnosis, preoperative lab results, and surgical information were analyzed. The primary outcome was posttransplantation DGF and the second outcome was posttransplantation 1-month and 3-month survival. Recursive feature elimination selected critical variables, and models were built using ML algorithms and logistic regression. Model performance was evaluated by AUC, accuracy, sensitivity, and specificity. The best model was validated with an independent dataset of 394 LT cases (January to June 2023). The SHapley Additive exPlanations package interpreted the top model's decisions. Among 5242 cases, 328 (6.26%) developed DGF, with 15 cases (3.81%) in the external validation set. Thirty critical features were selected. The eXtreme Gradient Boosting algorithm achieved the highest AUC (0.877) and accuracy (0.936) in the internal set, and a comparable AUC (0.776) and accuracy (0.957) in the external set. SHAP analysis identified short perfusion time, high donor serum sodium, excessive bleeding during transplantation, high donor γ-glutamyl transpeptidase, and blood glucose levels as top predictors of post-LT DGF. The proposed model AUC's 1-month survival prediction was 0.841 and the 3-month survival prediction was 0.834. The developed model for predicting postoperative DGF demonstrated excellent predictive performance, aiding clinicians in evaluating donor grafts and making informed decisions.