| 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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. 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 |
Development and prospective validation of a machine learning model to predict mortality in cirrhosis with esophageal variceal bleeding
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Matheus Machado Rech, Leandro Luis Corso, Elisa Fioreze Dal Bó, Andressa Daiane Ferraza, Fernanda Tomé, Alana Zulian Terres, Rafael Sartori Balbinot, Raul Angelo Balbinot, Silvana Sartori Balbinot and Jonathan Soldera |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Jonathan Soldera, PhD, Professor, Tutor, Gastroenterology and Acute Medicine, University of South Wales in association with Learna Ltd., Llantwit Road Pontypridd, Cardiff CF37 1DL, United Kingdom. jonathansoldera@gmail.com |
| Key Words |
Cirrhosis; Esophageal and gastric varices; Machine learning; Hemorrhage; Mortality; Artificial intelligence |
| Core Tip |
Acute esophageal variceal bleeding in cirrhotic patients carries high mortality, and traditional scores often underperform in risk stratification. This study presents the development, internal validation, and prospective validation of a machine learning model using random forests to predict 1-year mortality in acute esophageal variceal bleeding. The model demonstrated excellent discrimination and calibration, and was deployed as an online clinical tool. This is among the first machine learning models prospectively validated for this indication, offering a promising aid for timely and individualized decision-making in cirrhosis-related gastrointestinal bleeding. |
| Publish Date |
2026-02-12 11:20 |
| Citation |
Rech MM, Corso LL, Dal Bó EF, Ferraza AD, Tomé F, Terres AZ, Balbinot RS, Balbinot RA, Balbinot SS, Soldera J. Development and prospective validation of a machine learning model to predict mortality in cirrhosis with esophageal variceal bleeding. World J Hepatol 2026; 18(2): 111099 |
| URL |
https://www.wjgnet.com/1948-5182/full/v18/i2/111099.htm |
| DOI |
https://dx.doi.org/10.4254/wjh.v18.i2.111099 |
