Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 7, 2025; 31(13): 104697
Published online Apr 7, 2025. doi: 10.3748/wjg.v31.i13.104697
Noninvasive prediction of esophagogastric varices in hepatitis B: An extreme gradient boosting model based on ultrasound and serology
Si-Yi Feng, Zong-Ren Ding, Jin Cheng, Hai-Bin Tu
Si-Yi Feng, Jin Cheng, Hai-Bin Tu, Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China
Zong-Ren Ding, Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China
Co-first authors: Si-Yi Feng and Zong-Ren Ding.
Author contributions: Feng SY conceived and designed the study, performed data analysis and interpretation, and wrote the first draft of the manuscript; Ding ZR participated in study design, assisted with data interpretation, and critically revised the manuscript for important intellectual content; Feng SY and Ding ZR contributed equally to this article, they are the co-first authors of this manuscript; Cheng J conducted data collection and analysis, contributed to the development of predictive models, and reviewed the manuscript; Tu HB supervised the project, provided critical feedback during manuscript preparation, and approved the final version for submission; Feng SY, Ding ZR, Cheng J, and Tu HB accepts responsibility for the integrity of the work and agrees to be accountable for all aspects of the research; and all authors have read and approved the final manuscript.
Supported by the Agency Natural Science Foundation of Fujian Province, China, No. 2022J011285 and No. 2023J011480.
Institutional review board statement: This study was approved by the Medical Ethics Committee of Mengchao Hepatobiliary Hospital, approval No. 2022_028_01.
Informed consent statement: All patients/participants provided their written informed consent to participate in this study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Technical appendix, statistical code, and dataset are available from the corresponding author at thb861126@163.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: Hai-Bin Tu, Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, No. 66 Jintang Road, Jianxin Town, Cangshan District, Fuzhou 350025, Fujian Province, China. thb861126@163.com
Received: December 31, 2024
Revised: February 20, 2025
Accepted: March 11, 2025
Published online: April 7, 2025
Processing time: 94 Days and 4.4 Hours
Abstract
BACKGROUND

Severe esophagogastric varices (EGVs) significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage. Endoscopy is the gold standard for EGV detection but it is invasive, costly and carries risks. Noninvasive predictive models using ultrasound and serological markers are essential for identifying high-risk patients and optimizing endoscopy utilization. Machine learning (ML) offers a powerful approach to analyze complex clinical data and improve predictive accuracy. This study hypothesized that ML models, utilizing noninvasive ultrasound and serological markers, can accurately predict the risk of EGVs in hepatitis B patients, thereby improving clinical decision-making.

AIM

To construct and validate a noninvasive predictive model using ML for EGVs in hepatitis B patients.

METHODS

We retrospectively collected ultrasound and serological data from 310 eligible cases, randomly dividing them into training (80%) and validation (20%) groups. Eleven ML algorithms were used to build predictive models. The performance of the models was evaluated using the area under the curve and decision curve analysis. The best-performing model was further analyzed using SHapley Additive exPlanation to interpret feature importance.

RESULTS

Among the 310 patients, 124 were identified as high-risk for EGVs. The extreme gradient boosting model demonstrated the best performance, achieving an area under the curve of 0.96 in the validation set. The model also exhibited high sensitivity (78%), specificity (94%), positive predictive value (84%), negative predictive value (88%), F1 score (83%), and overall accuracy (86%). The top four predictive variables were albumin, prothrombin time, portal vein flow velocity and spleen stiffness. A web-based version of the model was developed for clinical use, providing real-time predictions for high-risk patients.

CONCLUSION

We identified an efficient noninvasive predictive model using extreme gradient boosting for EGVs among hepatitis B patients. The model, presented as a web application, has potential for screening high-risk EGV patients and can aid clinicians in optimizing the use of endoscopy.

Keywords: Esophagogastric varices; Machine learning; Extreme gradient boosting; Ultrasound; Serological markers

Core Tip: We constructed a noninvasive predictive model using machine learning for esophagogastric varices in hepatitis B patients. An extreme gradient boosting model, based on ultrasound and serological markers, achieved high accuracy (area under the curve = 0.96) in predicting high-risk esophagogastric varices. Key predictive variables included albumin, prothrombin time, portal vein flow velocity and spleen stiffness. A web-based application was developed to facilitate clinical use, offering real-time risk assessment. This model provides a promising tool for targeted screening, potentially reducing the need for costly and risky endoscopic procedures in low-risk individuals.