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

 Seungho Jung  ;  Kyemyung Park  ;  Kyong Ihn  ;  Seon Ju Kim  ;  Myoung Soo Kim  ;  Dongwoo Chae  ;  Bon-Nyeo Koo 
 SCIENTIFIC REPORTS, Vol.12(1) : 22411, 2022-12 
Journal Title
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Biomarkers ; Child ; Humans ; Liver Transplantation* / adverse effects ; Machine Learning ; Retrospective Studies ; Risk Factors
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 selection operator (LASSO) -based method was used for feature selection to overcome the high dimensionality and collinearity of variables. Among 146 features, four variables were selected as the resultant features, namely, preoperative hepatic encephalopathy, sodium level at the end of surgery, hepatic artery thrombosis, and total bilirubin level on postoperative day 7. These features were selected from different times and represent distinct clinical aspects. The model with logistic regression demonstrated the best prediction performance among various machine learning methods tested (area under the receiver operating characteristic curve (AUROC) = 0.898 and area under the precision-recall curve (AUPR) = 0.882). The risk scoring system developed based on the logistic regression model showed an AUROC of 0.910 and an AUPR of 0.830. Together, the prediction of graft failure in pediatric liver transplantation using the proposed machine learning model exhibited superior discrimination power and, therefore, can provide valuable information to clinicians for their decision making during the postoperative management of the patients.
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1. College of Medicine (의과대학) > Dept. of Anesthesiology and Pain Medicine (마취통증의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pharmacology (약리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
Yonsei Authors
Koo, Bon-Nyeo(구본녀) ORCID logo https://orcid.org/0000-0002-3189-1673
Kim, Myoung Soo(김명수) ORCID logo https://orcid.org/0000-0002-8975-8381
Ihn, Kyong(인경) ORCID logo https://orcid.org/0000-0002-6161-0078
Jung, Seungho(정승호) ORCID logo https://orcid.org/0000-0001-6220-6766
Chae, Dong Woo(채동우) ORCID logo https://orcid.org/0000-0002-7675-3821
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