Li YQ, Li ZJ, Li YQ, Feng Y, Wang XB. Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension. World J Gastroenterol 2026; 32(4): 113492 [DOI: 10.3748/wjg.v32.i4.113492]
Corresponding Author of This Article
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
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Gastroenterology & Hepatology
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Retrospective 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/
Jan 28, 2026 (publication date) through Jan 23, 2026
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World Journal of Gastroenterology
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1007-9327
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Li YQ, Li ZJ, Li YQ, Feng Y, Wang XB. Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension. World J Gastroenterol 2026; 32(4): 113492 [DOI: 10.3748/wjg.v32.i4.113492]
World J Gastroenterol. Jan 28, 2026; 32(4): 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, Zhuo-Jun Li, Yong-Qi Li, Ying Feng, Xian-Bo Wang
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 Telecommunications, Beijing 100037, China
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
Abstract
BACKGROUND
Hepatitis B-related cirrhosis represents a major contributor to liver-related events (LREs), with the development of clinically significant portal hypertension (CSPH) serving as a critical milestone in disease progression.
AIM
To establish predictive models based on multiple machine learning algorithms to improve the accuracy and clinical utility of LREs prediction.
METHODS
A total of 576 patients were retrospectively enrolled and randomly divided into training (n = 403) and validation (n = 173) cohorts. Features were selected through least absolute shrinkage and selection operator regression, random forest (RF), and support vector machine (SVM). Based on these features, five predictive models were constructed, including SVM, RF, logistic regression, extreme gradient boosting (XGBoost), and k-nearest neighbor. Model performance was evaluated using receiver operating characteristic and decision curve analysis, and feature importance and interactions were further explored using SHapley Additive exPlanations (SHAP).
RESULTS
Of the patients included, 313 (54.3%) developed LREs. Eight core predictive features were ultimately identified, with the liver stiffness measurement (LSM)-to-platelet ratio (LPR) contributing most significantly. The XGBoost and RF models demonstrated superior performance, achieving accuracies of 0.951 and areas under the curve of 0.975 and 0.965, respectively. SHAP analysis revealed that LPR, hemoglobin (HB), and LSM were key factors, with LPR exhibiting significant interactions with HB, international normalized ratio, and spleen thickness.
CONCLUSION
Machine learning-based prediction models, particularly XGBoost and RF, can effectively identify high-risk individuals among patients with compensated hepatitis B virus-related cirrhosis and CSPH. LPR that incorporates LSM is a valuable and robust predictive indicator.
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.
