Publication Name	World Journal of Hepatology
Manuscript ID	89491
Country	United Kingdom

Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Cohort Study
Article Title	Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study
Manuscript Source	Invited Manuscript
All Author List	Jonathan Soldera, Leandro Luis Corso, Matheus Machado Rech, Vinícius Remus Ballotin, Lucas Goldmann Bigarella, Fernanda Tomé, Nathalia Moraes, Rafael Sartori Balbinot, Santiago Rodriguez, Ajacio Bandeira de Mello Brandão and Bruno Hochhegger
Funding Agency and Grant Number
Corresponding Author	Jonathan Soldera, MD, PhD, Instructor, Post Graduate Program at Acute Medicine and Gastroenterology, University of South Wales, Llantwit Rd, Pontypridd, Cardiff CF37 1DL, United Kingdom. jonathansoldera@gmail.com
Key Words	Liver transplantation; Major adverse cardiac events; Machine learning; Myocardial perfusion imaging; Stress test
Core Tip	This study presents a robust machine learning model, utilizing the XGBoost algorithm, to predict major adverse cardiovascular events (MACE) following liver transplantation. The model demonstrated high accuracy and calibration, with key factors such as noninvasive cardiac stress test outcomes, use of nonselective beta-blockers, direct bilirubin levels, blood type O, and dynamic alterations on myocardial perfusion scintigraphy identified as significant predictors. This tool offers valuable insights into the risk assessment of post-liver transplant MACE, particularly in an aging and comorbid patient population.
Citation	Soldera J, Corso LL, Rech MM, Ballotin VR, Bigarella LG, Tomé F, Moraes N, Balbinot RS, Rodriguez S, Brandão ABM, Hochhegger B. Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study. World J Hepatol 2024; 16(2): 193-210

Received	2023-11-02 21:01
Peer-Review Started	2023-11-02 21:03
To Make the First Decision
Return for Revision	2023-12-01 18:52
Revised	2023-12-27 02:56
Second Decision	2024-02-04 03:02
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-02-04 06:44
Articles in Press	2024-02-04 06:44
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-02-19 06:25

ISSN	1948-5182 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Copyright	© The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
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