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

Authors
 Sujung Park  ;  Kyemyung Park  ;  Jae Geun Lee  ;  Tae Yang Choi  ;  Sungtaik Heo  ;  Bon-Nyeo Koo  ;  Dongwoo Chae 
Citation
 JOURNAL OF PERSONALIZED MEDICINE, Vol.12(7) : 1028, 2022-06 
Journal Title
JOURNAL OF PERSONALIZED MEDICINE
Issue Date
2022-06
Keywords
estimated blood loss ; liver transplantation ; machine learning
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 replacement therapy (CRRT), the preoperative dose of vasopressor, and the estimated blood loss (EBL) during surgery. The logistic regression model was one of the best-performing machine learning models. The most important factors for the prediction of massive hemorrhage were the disease etiology, activated partial thromboplastin time (aPTT), operation duration, body temperature, MELD score, mean arterial pressure, serum creatinine, and pulse pressure. The risk-scoring system was developed using the odds ratios of these factors from the logistic model. The risk-scoring system showed good prediction performance and calibration (AUROC: 0.775, AUPR: 0.753).
Files in This Item:
T202202532.pdf Download
DOI
10.3390/jpm12071028
Appears in Collections:
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
Park, Soo Jung(박수정) ORCID logo https://orcid.org/0000-0003-2963-1394
Lee, Jae Geun(이재근) ORCID logo https://orcid.org/0000-0002-6722-0257
Chae, Dong Woo(채동우) ORCID logo https://orcid.org/0000-0002-7675-3821
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/189441
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