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 [DOI: 10.4254/wjh.v18.i2.111099]
Corresponding Author of This Article
Jonathan Soldera, PhD, Gastroenterology and Acute Medicine, University of South Wales in Association with Learna Ltd., Llantwit Road Pontypridd, Cardiff CF37 1DL, United Kingdom. jonathansoldera@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Cohort Study
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Feb 27, 2026 (publication date) through Feb 12, 2026
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Publication Name
World Journal of Hepatology
ISSN
1948-5182
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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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 [DOI: 10.4254/wjh.v18.i2.111099]
World J Hepatol. Feb 27, 2026; 18(2): 111099 Published online Feb 27, 2026. doi: 10.4254/wjh.v18.i2.111099
Development and prospective validation of a machine learning model to predict mortality in cirrhosis with esophageal variceal bleeding
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, Jonathan Soldera
Matheus Machado Rech, Elisa Fioreze Dal Bó, Andressa Daiane Ferraza, Rafael Sartori Balbinot, School of Medicine, Universidade de Caxias do Sul, Caxias do Sul 95070-560, RS, Brazil
Leandro Luis Corso, Fernanda Tomé, Department of Engineering, Universidade de Caxias do Sul, Caxias do Sul 95070-560, RS, Brazil
Alana Zulian Terres, Raul Angelo Balbinot, Silvana Sartori Balbinot, Jonathan Soldera, Department of Clinical Gastroenterology, Universidade de Caxias do Sul, Caxias do Sul 95070-560, RS, Brazil
Jonathan Soldera, Gastroenterology and Acute Medicine, University of South Wales in Association with Learna Ltd., Cardiff CF37 1DL, United Kingdom
Co-first authors: Matheus Machado Rech and Leandro Luis Corso.
Co-corresponding authors: Silvana Sartori Balbinot and Jonathan Soldera.
Author contributions: Rech MM, Corso LL, Terres AZ, Balbinot RA, Balbinot SS, and Soldera J conceptualized and designed the study; Rech MM, Dal Bó EF, and Ferraza AD performed the data collection; Rech MM, Corso LL, Tomé F, and Soldera J analyzed the data, interpreted the results, and prepared the manuscript; All authors reviewed the results, participated in the first draft writing, and approved the final version of the manuscript. Rech MM and Corso LL contributed equally to this manuscript as co-first authors. Balbinot SS and Soldera J contributed equally to this manuscript as co-corresponding authors.
Institutional review board statement: The study was reviewed and approved by the University of Caxias do Sul Ethics Committee (Approval No. 66646617.3.0000.5341).
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The original anonymized dataset is available upon reasonable request from the corresponding authors at mmrech.md@gmail.com or jonathansoldera@gmail.com.
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/
Corresponding author: Jonathan Soldera, PhD, Gastroenterology and Acute Medicine, University of South Wales in Association with Learna Ltd., Llantwit Road Pontypridd, Cardiff CF37 1DL, United Kingdom. jonathansoldera@gmail.com
Received: June 24, 2025 Revised: August 11, 2025 Accepted: December 3, 2025 Published online: February 27, 2026 Processing time: 234 Days and 19.8 Hours
Abstract
BACKGROUND
Acute esophageal variceal bleeding (AEVB) is a critical complication in patients with cirrhosis, associated with high mortality despite advancements in management. Traditional prognostic scores often lack predictive accuracy in this context.
AIM
To develop, internally validate, and prospectively validate a machine learning (ML) model to predict 1-year mortality in patients with cirrhosis presenting with AEVB.
METHODS
A retrospective cohort of 94 patients treated between 2010 and 2016 was used to train ML models, incorporating 36 clinical, laboratory, and imaging variables. Four algorithms (generalized linear models, boosted generalized linear models, naive Bayes, random forests) were evaluated, and the best-performing model was prospectively validated in a cohort of 24 patients treated between 2017 and 2018. Performance metrics included the area under the curve (AUC), sensitivity, specificity, and calibration via Brier scores. Data preprocessing involved k-nearest neighbor imputation, one-hot encoding, and scaling.
RESULTS
The random forest model achieved the highest AUC (0.91, 95% confidence interval [CI]: 0.85-0.96) during internal validation and demonstrated robust performance in the prospective cohort (AUC 0.88, 95%CI: 0.80-0.94). Calibration was excellent, with a low Brier score (0.12). The model was deployed as an online prediction tool.
CONCLUSION
This ML model shows promise in improving mortality prediction for AEVB, potentially aiding timely clinical interventions and decision-making. Prospective validation underscores its generalizability and clinical utility. Future research should explore external validation in diverse settings.
Core Tip: Acute esophageal variceal bleeding in patients with cirrhosis 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.
