Predicting graft failure in pediatric liver transplantation based on early biomarkers using machine learning models.

ABSTRACT: The early detection of graft failure in pediatric liver transplantation is crucial for appropriate intervention. Graft failure is associated with numerous perioperative risk factors. This study aimed to develop an individualized predictive model for 90-days graft failure in pediatric liver transplantation using machine learning methods. We conducted a single-center retrospective cohort study. A total of 87 liver transplantation cases performed in patients aged < 12 years at the Severance Hospital between January 2010 and September 2020 were included as data samples. Preoperative conditions of recipients and donors, intraoperative care, postoperative serial laboratory parameters, and events observed within seven days of surgery were collected as features. A least absolute shrinkage and sele

SUBMITTER: Jung S 

PROVIDER: S-EPMC9794703 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature