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©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jan 28, 2026; 32(4): 113492
Published online Jan 28, 2026. doi: 10.3748/wjg.v32.i4.113492
Published online Jan 28, 2026. doi: 10.3748/wjg.v32.i4.113492
Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension
Yan-Qiu Li, Yong-Qi Li, Ying Feng, Xian-Bo Wang, Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
Zhuo-Jun Li, School of Artificial Intelligence, Beijing University of Posts and Telecommu
Co-corresponding authors: Ying Feng and Xian-Bo Wang.
Author contributions: Wang XB and Feng Y contribute equally to this study as co-corresponding authors; Wang XB and Feng Y designed the manuscript; Li YQ drafted the manuscript; Li ZJ drew the figures; Li YQ carefully reviewed the manuscript; all authors approved the final version of the manuscript.
Supported by the High-Level Chinese Medicine Key Discipline Construction Project, No. zyyzdxk-2023005; Capital’s Funds for Health Improvement and Research, No. 2024-1-2173; National Natural Science Foundation of China, No. 82474419 and No. 82474426; Beijing Municipal Natural Science Foundation, No. 7232272; and Beijing Traditional Chinese Medicine Technology Development Fund Project, No. BJZYZD-2023-12.
Institutional review board statement: This study was approved by the Ethics Committee of Beijing Ditan Hospital (Approval No. DTEC-KY2024-069-01).
Informed consent statement: All patients provided written informed consent.
Conflict-of-interest statement: The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.
Data sharing statement: The data that support the findings of this study are available on request from the corresponding author at wangxb@ccmu.edu.cn.
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: Xian-Bo Wang, MD, PhD, Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jingshun East Street, Chaoyang District, Beijing 100015, China. wangxb@ccmu.edu.cn
Received: August 27, 2025
Revised: November 22, 2025
Accepted: December 17, 2025
Published online: January 28, 2026
Processing time: 148 Days and 18.8 Hours
Revised: November 22, 2025
Accepted: December 17, 2025
Published online: January 28, 2026
Processing time: 148 Days and 18.8 Hours
Core Tip
Core Tip: This study developed and validated machine learning models to predict liver-related events in patients with compensated hepatitis B virus-related cirrhosis and clinically significant portal hypertension. Among five models, extreme gradient boosting and random forest achieved the best accuracy and clinical utility. The liver stiffness measurement-to-platelet ratio (LPR) emerged as the most influential predictor, interacting with hemoglobin, international normalized ratio, and spleen thickness. These findings highlight machine learning based on LPR as a robust noninvasive method and provide a novel, interpretable tool for early risk stratification and personalized management in compensated cirrhosis.