Retrospective Cohort Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 7, 2024; 30(13): 1859-1870
Published online Apr 7, 2024. doi: 10.3748/wjg.v30.i13.1859
Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension
Rong Chen, Ling Luo, Yun-Zhi Zhang, Zhen Liu, An-Lin Liu, Yi-Wen Zhang
Rong Chen, Ling Luo, Yun-Zhi Zhang, Zhen Liu, An-Lin Liu, Yi-Wen Zhang, Department of Infectious Diseases, Second Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
Author contributions: Chen R designed the research, collected and organized the data, and wrote the initial draft of the manuscript; Zhang YZ guided the research design; Liu Z, Liu AL, and Zhang YW were involved in data collection and analysis; and Luo L managed the project and participated in the manuscript’s review and editing; and all authors have read and approved the final version of the manuscript for publication.
Supported by the Chinese Nursing Association, No. ZHKY202111; Scientific Research Program of School of Nursing, Chongqing Medical University, No. 20230307; and Chongqing Science and Health Joint Medical Research Program, No. 2024MSXM063.
Institutional review board statement: This study was reviewed and approved by the Ethical Review Committee of the Second Affiliated Hospital of Chongqing Medical University (Approval No. 005, 2023).
Informed consent statement: Informed consent was waived due to the retrospective cohort design of this study. For privacy reasons, patients’ identifying information was replaced with codes before data extraction.
Conflict-of-interest statement: Authors declare no conflict of interest for this article.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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: Ling Luo, MNurs, Researcher, Department of Infectious Diseases, Second Affiliated Hospital of Chongqing Medical University, No. 288 Tianwen Avenue, Nanan District, Chongqing 400016, China. ll7765@cqmu.edu.cn
Received: December 29, 2023
Peer-review started: December 29, 2023
First decision: January 9, 2024
Revised: February 1, 2024
Accepted: March 19, 2024
Article in press: March 19, 2024
Published online: April 7, 2024
Processing time: 95 Days and 13 Hours
ARTICLE HIGHLIGHTS
Research background

Portal hypertension (PHT) secondary to cirrhosis leads to severe symptoms, exacerbating disease progression and adversely affecting survival rates. Transjugular intrahepatic portosystemic shunt (TIPS) is pivotal in managing PHT; however, its resultant complications can significantly impact patient prognosis. A thorough understanding of the interplay and mechanisms of various prognostic factors is crucial for enhancing treatment strategies and improving patient survival.

Research motivation

There is a gap in existing research concerning the comprehensive exploration of the interrelationships and mechanisms of prognostic factors in PHT. We believe there is an urgent requirement for advanced modeling approaches to intricately analyze these interactions.

Research objectives

To use Bayesian network (BN) methodology for extensive analysis of factors influencing the prognosis of PHT patients after TIPS. The objective involves elucidating the interdependencies among these factors and developing a BN model to predict patient survival after TIPS, thus facilitating informed clinical decisions.

Research methods

In this study, we included 393 patients and used Cox and least absolute shrinkage and selection operator regression to select variables most relevant to prognosis, and we established a new BN survival prediction model for patients having undergone TIPS surgery for PHT.

Research results

We successfully developed a BN-based survival prediction model with good predictive capabilities. Key factors impacting survival were identified, and the model showed high accuracy, precision, recall, and F1 score, with an AUC of 0.72, indicating its efficacy in survival prediction after TIPS in patients with PHT.

Research conclusions

We developed a novel BN-based model for predicting survival in patients having undergone TIPS surgery for PHT. This model enhances the precision of survival prognosis and provides new analytical tools for patient evaluation, treatment planning, and disease management.

Research perspectives

Data from other centers are essential for further validating the clinical usability of this novel model. Concurrently, continued research is imperative to deepen the understanding of post-TIPS complication risk factors and their impact on patient outcomes, thus guiding the development of more effective and scientific treatment strategies for patients with PHT.