Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery

Frontiers in Medicine

article

Le-Ping Liu¹  Qin-Yu Zhao²  Jiang Wu³  Yan-Wei Luo¹  Hang Dong¹  Zi-Wei Chen⁴  Rong Gui¹  Yong-Jun Wang⁵ 

[1] Department of Blood Transfusion, The Third Xiangya Hospital of Central South University;College of Engineering and Computer Science, Australian National University;Department of Blood Transfusion, Renji Hospital Affiliated to Shanghai Jiao Tong University;Department of Laboratory Medicine, The Third Xiangya Hospital of Central South University;Department of Blood Transfusion, The Second Xiangya Hospital of Central South University

关键词: liver transplantation;    machine learning;    prediction model;    red blood cell transfusion;    SHapley Additive exPlanations;   

DOI  :  10.3389/fmed.2021.632210

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY