Abstract
Aim: This study aimed to use machine learning algorithms to identify critical preoperative variables and predict the red blood cell (RBC) transfusion during or after liver transplantation surgery.Study Design and Methods: A total of 1,193 patients undergoing liver transplantation in three large tertiary hospitals in China were examined. Twenty-four preoperative variables were collected, including essential population characteristics, diagnosis, symptoms, and laboratory parameters. The cohort was randomly split into a train set (70%) and a validation set (30%). The Recursive Feature Elimination and eXtreme Gradient Boosting algorithms (XGBOOST) were used to select variables and build machine learning prediction models, respectively. Besides, seven other machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC) was used to compare the prediction performance of different models. The SHapley Additive exPlanations package was applied to interpret the XGBOOST model. Data from 31 patients at one of the hospitals were prospectively collected for model validation.Results: In this study, 72.1% of patients in the training set and 73.2% in the validation set underwent RBC transfusion during or after the surgery. Nine vital preoperative variables were finally selected, including the presence of portal hypertension, age, hemoglobin, diagnosis, direct bilirubin, activated partial thromboplastin time, globulin, aspartate aminotransferase, and alanine aminotransferase. The XGBOOST model presented significantly better predictive performance (AUROC: 0.813) than other models and also performed well in the prospective dataset (accuracy: 76.9%).Discussion: A model for predicting RBC transfusion during or after liver transplantation was successfully developed using a machine learning algorithm based on nine preoperative variables, which could guide high-risk patients to take appropriate preventive measures.
Highlights
Liver transplantation is an effective method for treating end-stage liver disease
The participants were patients aged more than 18 years who underwent liver transplantation, from March 2014 to September 2019, at one of the following three tertiary hospitals: the Second Xiangya Hospital of Central South University, the Third Xiangya Hospital of Central South University, and the Renji Hospital affiliated to Medical College of Shanghai Jiao Tong University
The proposed model significantly outperformed the conventional LR (AUROC: 0.707) and seven other machine learning models
Summary
Liver transplantation is an effective method for treating end-stage liver disease. Prolonged and complicated surgical procedures may cause bleeding during the perioperative period. Most patients require an infusion of concentrated red blood cells (RBCs) during or after the surgery. Blood transfusion can increase the patient’s oxygen supply and improve tissue perfusion, it is accompanied by many side effects, such as the increased risk of deep vein thrombosis, increased fibrosis, cancer recurrence, and increased mortality, adversely affecting the patient’s prognosis [1,2,3,4,5]. The methods of reducing blood transfusions include the preoperative use of tranexamic acid, intraoperative blood salvage, and intraoperative autotransfusion. These approaches cannot be applied to all patients, considering their risks and the costs [6,7,8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
