Advancing Prediction of Risk of Intraoperative Massive Blood Transfusion in Liver Transplantation With Machine Learning Models. A Multicenter Retrospective Study - Chen, Sai; Liu, Le-ping; Wang, Yong-jun; Zhou, Xiong-hui; Dong, Hang; Chen, Zi-wei; Wu, Jiang; Gui, Rong; Zhao, Qin-yu (Frontiers in Neuroinformatics, 2022)
Background: Liver Transplantation Surgery is often accompanied by massive blood loss and massive transfusion (MT), while MT can cause many serious complications related to high mortality. Therefore, there is an urgent need for a model that can predict the demand for MT to reduce the waste of blood resources and improve the prognosis of patients. Objective: To develop a model for predicting intraoperative massive blood transfusion in liver transplantation based on machine learning algorithms. Methods: 1239 patients who underwent liver transplantation in three large hospitals in China from March 2014 to November 2021 were included and analyzed. 1193 cases were randomly divided into the training set (70%) and test set (30%), and 46 cases were prospectively collected as a validation set. The outcome of this study was intraoperative massive blood transfusion. A total of 27 candidate risk factors were collected, and recursive feature elimination (RFE) was used to select key features based on the Categorical Boosting (CatBoost) model. A total of ten machine learning models were built, among which the three best performing models and traditional logistic regression (LR) method were prospectively verified in the validation set. The Area Under the Receiver Operating Characteristic Curve (AUROC) was used for model performance evaluation. The Shapley additive explanation value was applied to explain the complex ensemble learning models. Results: Fifteen key variables were screened out, including age, weight, hemoglobin, platelets, white blood cells count, activated partial thromboplastin time, prothrombin time, aspartate aminotransferase, total protein, albumin, globulin, creatinine, urea. Among all algorithms, the predictive performance of CatBoost model (AUROC: 0.810) was the best. In the prospective validation cohort, LR performed far less well than other algorithms. Conclusion: A prediction model for massive blood transfusion in liver transplantation was successfully established based on the CatBoost algorithm, which is superior to the traditional LR model and other algorithms, and it can more accurately predict the risk of massive blood transfusions and guide clinical decision-making.