To evaluate the efficiency of prediction models constructed based on different machine learning technologies for early severe complications after liver transplantation.

Clinical data of 129 patients undergoing orthotopic liver transplantation in Nanfang Hospital of Southern Medical University from July 2018 to August 2024 were retrospectively analyzed. The informed consents of all patients or family members were obtained. Among them, 121 patients were male and 8 female, aged from 18 to 71 years, with a median age of 51 years. According to Clavien-Dindo classification, all patients were divided into the severe and non-severe complication groups according to the incidence of Clavien-Dindo grade Ⅲb or above complications within 90 d after liver transplantation. Normalization method was used to preprocess continuous variables. Information gain, Boruta and elastic network were utilized for feature selection. Common factors in the methods and results were included. Five machine learning technologies, including support vector machine (SVM), random forest (RF), logistic regression (LR), eXtreme Gradient Boosting (XGBoost) and naive Bayes, were adopted to construct prediction models for early severe complications after liver transplantation. The accuracy rate, precision rate, recall rate, F1 value and the area under the ROC curve (AUC) of different machine learning algorithms were calculated by ten-fold cross-validation. Shapley additive explanations (SHAP) was adopted in the optimal model to evaluate the relative importance of each feature in the prediction models.

The incidence of early severe complications after liver transplantation was 24.8%(32/129). Five features including preoperative controlling nutritional status (COUNT) score, preoperative psoas muscle thickness per height (PMTH), preoperative MELD score, anhepatic phase and intraoperative blood loss, were input into machine learning models. Considering all indexes, the average efficiency of the RF prediction model was high and yielded the highest recall rate (0.844) and F1 score (0.866), demonstrating excellent capability in practical application. The average AUC, accuracy and precision rates in the validation set were 0.808, 0.800 and 0.898, respectively. The visual ranking of the importance of prediction variables in RF prediction models was: COUNT score, anhepatic phase, preoperative MELD score, intraoperative blood loss and PMTH, respectively. According to the SHAP, preoperative COUNT score, intraoperative blood loss, anhepatic phase and preoperative MELD score were positively correlated with, while preoperative PTMH value was negatively correlated with the incidence of early severe complications after liver transplantation.

Prediction models for early severe complications after liver transplantation are constructed based on different machine learning technologies. RF prediction model has high prediction performance, which is of certain significance to investigate perioperative management of liver transplantation recipients.