| 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. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| 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 |
| ORCID |
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| Funding Agency and Grant Number |
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| 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. |
| Publish Date |
2024-02-27 11:58 |
| 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 |
| URL |
https://www.wjgnet.com/1948-5182/full/v16/i2/193.htm |
| DOI |
https://dx.doi.org/10.4254/wjh.v16.i2.193 |
