Observational Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jan 28, 2025; 31(4): 100401
Published online Jan 28, 2025. doi: 10.3748/wjg.v31.i4.100401
Machine learning prediction of hepatic encephalopathy for long-term survival after transjugular intrahepatic portosystemic shunt in acute variceal bleeding
De-Jia Liu, Li-Xuan Jia, Feng-Xia Zeng, Wei-Xiong Zeng, Geng-Geng Qin, Qi-Feng Peng, Qing Tan, Hui Zeng, Zhong-Yue Ou, Li-Zi Kun, Jian-Bo Zhao, Wei-Guo Chen
De-Jia Liu, Qi-Feng Peng, Qing Tan, Zhong-Yue Ou, Li-Zi Kun, Jian-Bo Zhao, Division of Vascular and Interventional Radiology, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510151, Guangdong Province, China
Li-Xuan Jia, Feng-Xia Zeng, Wei-Xiong Zeng, Geng-Geng Qin, Hui Zeng, Wei-Guo Chen, Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou 510151, Guangdong Province, China
Co-first authors: De-Jia Liu and Li-Xuan Jia.
Co-corresponding authors: Jian-Bo Zhao and Wei-Guo Chen.
Author contributions: Liu DJ, Jia LX, and Zhao JB designed the experiments and analyzed the data; Peng QF, Tan Q, and Li ZK performed the transjugular intrahepatic portosystemic shunt procedure; Chen WG, Qin GG, and Zeng FX analyzed the data; Liu DJ, Jia LX, and Zeng WX wrote the manuscript; Zeng H and Ou ZK prepared the figures; All authors have reviewed and approved the manuscript; Zhao JB and Chen WG are designated co-corresponding authors due to their equal contributions to research conception, methodology, data analysis, and manuscript preparation, they jointly managed journal communication, ensuring their efforts are equally recognized; Similarly, Liu DJ and Jia LX are credited as co-first authors for their equal roles in research design, data collection, analysis, and drafting, both designations reflect the collaborative nature of the project and the shared contributions of all involved.
Supported by the Natural Science Foundation of Guangdong Province, No. 2024A1515013069.
Institutional review board statement: This study was conceived retrospectively in accordance with the Declaration of Helsinki and was approved by the ethics committee of the Nanfang Hospital, Southern Medical University, No. NFEC-2024-258.
Informed consent statement: The Institutional Review Board of the Southern Medical University waived the requirement for patient-informed consent this retrospective analysis.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: Technical appendix, statistical code, and dataset available upon reasonable request to the corresponding author at liudejia1998@163.com.
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.
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: Jian-Bo Zhao, MD, Doctor, Division of Vascular and Interventional Radiology, Department of General Surgery, Nanfang Hospital, Southern Medical University, No. 1838 North Guangzhou Main Road, Guangzhou 510151, Guangdong Province, China. zhaojianbohgl@163.com
Received: August 15, 2024
Revised: October 23, 2024
Accepted: December 2, 2024
Published online: January 28, 2025
Processing time: 136 Days and 22.5 Hours
Core Tip

Core Tip: This study developed a machine learning (ML) model to predict overt hepatic encephalopathy (OHE) after transjugular intrahepatic portosystemic shunt (TIPS) in patients with acute variceal bleeding (AVB). Utilizing a 5-year retrospective dataset of 218 patients, key features such as Child-Pugh score, age, and portal vein thrombosis were identified. The ML model demonstrated a strong performance, with an area under the curve of 0.825. This ML model effectively predicts post-TIPS OHE, providing a valuable tool for tailoring personalized treatment plans. Its superior performance over traditional models supports its integration into clinical practice to enhance outcomes for patients with AVB undergoing TIPS.