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
Abstract
BACKGROUND

Portal hypertension (PHT), primarily induced by cirrhosis, manifests severe symptoms impacting patient survival. Although transjugular intrahepatic portosystemic shunt (TIPS) is a critical intervention for managing PHT, it carries risks like hepatic encephalopathy, thus affecting patient survival prognosis. To our knowledge, existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes. Consequently, the development of an innovative modeling approach is essential to address this limitation.

AIM

To develop and validate a Bayesian network (BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.

METHODS

The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed. Variables were selected using Cox and least absolute shrinkage and selection operator regression methods, and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.

RESULTS

Variable selection revealed the following as key factors impacting survival: age, ascites, hypertension, indications for TIPS, postoperative portal vein pressure (post-PVP), aspartate aminotransferase, alkaline phosphatase, total bilirubin, prealbumin, the Child-Pugh grade, and the model for end-stage liver disease (MELD) score. Based on the above-mentioned variables, a BN-based 2-year survival prognostic prediction model was constructed, which identified the following factors to be directly linked to the survival time: age, ascites, indications for TIPS, concurrent hypertension, post-PVP, the Child-Pugh grade, and the MELD score. The Bayesian information criterion was 3589.04, and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16. The model’s accuracy, precision, recall, and F1 score were 0.90, 0.92, 0.97, and 0.95 respectively, with the area under the receiver operating characteristic curve being 0.72.

CONCLUSION

This study successfully developed a BN-based survival prediction model with good predictive capabilities. It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

Keywords: Bayesian network, Cirrhosis, Portal hypertension, Transjugular intrahepatic portosystemic shunt, Survival prediction model

Core Tip: This study introduces an advanced Bayesian network model to better understand the interrelationships among prognostic factors and their combined impact on prognosis, thus enhancing the accuracy of survival predictions for patients with cirrhosis-induced portal hypertension after the transjugular intrahepatic portosystemic shunt procedure. This approach potentially assists in refining and advancing current prognostic research.