Development of Machine Learning Models Predicting Estimated Blood Loss during Liver Transplant Surgery.

ABSTRACT: The incidence of major hemorrhage and transfusion during liver transplantation has decreased significantly over the past decade, but major bleeding remains a common expectation. Massive intraoperative hemorrhage during liver transplantation can lead to mortality or reoperation. This study aimed to develop machine learning models for the prediction of massive hemorrhage and a scoring system which is applicable to new patients. Data were retrospectively collected from patients aged >18 years who had undergone liver transplantation. These data included emergency information, donor information, demographic data, preoperative laboratory data, the etiology of hepatic failure, the Model for End-stage Liver Disease (MELD) score, surgical history, antiplatelet therapy, continuous renal replaceme

SUBMITTER: Park S 

PROVIDER: S-EPMC9320884 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature