Meng PP, Xiong FX, Chen JL, Zhou Y, Liu XL, Ji XM, Jiang YY, Hou YX. Establish and validate an artificial neural networks model used for predicting portal vein thrombosis risk in hepatitis B-related cirrhosis patients. World J Hepatol 2025; 17(3): 97767 [PMID: 40177194 DOI: 10.4254/wjh.v17.i3.97767]
Corresponding Author of This Article
Yi-Xin Hou, PhD, Chief Doctor, Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jingshun East Street, Chaoyang District, Beijing 100020, China. xuexin162@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
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/
World J Hepatol. Mar 27, 2025; 17(3): 97767 Published online Mar 27, 2025. doi: 10.4254/wjh.v17.i3.97767
Establish and validate an artificial neural networks model used for predicting portal vein thrombosis risk in hepatitis B-related cirrhosis patients
Pei-Pei Meng, Fei-Xiang Xiong, Jia-Liang Chen, Yang Zhou, Xiao-Li Liu, Xiao-Min Ji, Yu-Yong Jiang, Yi-Xin Hou
Pei-Pei Meng, Fei-Xiang Xiong, Jia-Liang Chen, Yang Zhou, Xiao-Min Ji, Yu-Yong Jiang, Yi-Xin Hou, Center of Integrative Chinese and Western Medicine, Beijing Ditan Hospital affiliated to Capital Medical University, Beijing 100102, China
Xiao-Li Liu, Center of Integrative Medicine, Beijing Ditan Hospital Affiliated to Capital Medical, Beijing 100015, China
Co-first authors: Pei-Pei Meng and Fei-Xiang Xiong.
Co-corresponding authors: Yu-Yong Jiang and Yi-Xin Hou.
Author contributions: Hou YX, Meng PP and Zhou Y designed the study and interpreted the results; Liu XL, Chen JL, Xiong FX, Jiang YY and Ji XM collected the data and carried out analysis. All authors read and approved the final manuscript.
Supported by The Beijing Hospitals Authority Youth Programme, No. QMl220201802.
Institutional review board statement: The study was approved by the Ethics Committee of Beijing Ditan Hospital, Capital Medical University. Written informed consent was obtained from each patient. All procedures followed were by the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008.
Informed consent statement: Written informed consent was obtained from each patient.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest concerning the publication of this research report.
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.
Data sharing statement: The data used in this study are not publicly available due to privacy or ethical restrictions.
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: Yi-Xin Hou, PhD, Chief Doctor, Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jingshun East Street, Chaoyang District, Beijing 100020, China. xuexin162@163.com
Received: June 7, 2024 Revised: November 24, 2024 Accepted: February 24, 2025 Published online: March 27, 2025 Processing time: 291 Days and 12.5 Hours
Abstract
BACKGROUND
The portal vein thrombosis (PVT) can exacerbate portal hypertension and lead to complications, increasing the risk of mortality.
AIM
To evaluate the predictive capacity of artificial neural networks (ANNs) in quantifying the likelihood of developing PVT in individuals afflicted with hepatitis B-induced cirrhosis.
METHODS
A retrospective study was conducted at Beijing Ditan Hospital, affiliated with Capital Medical University, including 986 hospitalized patients. Patients admitted between January 2011 and December 2014 were assigned to the training set (685 cases), while those hospitalized from January 2015 to December 2016 were divided into the validation cohort (301 cases). Independent risk factors for PVT were identified using COX univariate analysis and used to construct an ANN model. Model performance was evaluated through metrics such as the area under the receiver operating characteristic curve (AUC) and concordance index.
RESULTS
In the training set, PVT occurred in 19.0% of patients within three years and 23.7% within five years. In the validation cohort, PVT developed in 16.7% of patients within three years and 24.0% within five years. The ANN model incorporated nine independent risk factors: Age, ascites, hepatic encephalopathy, gastrointestinal varices with bleeding, Child-Pugh classification, alanine aminotransferase levels, albumin levels, neutrophil-to-lymphocyte ratio, and platelet. The model achieved an AUC of 0.967 (95%CI: 0.960–0.974) at three years and 0.975 (95%CI: 0.955–0.992) at five years, significantly outperforming existing models such as model for end-stage liver disease and Child-Pugh-Turcotte (all P < 0.001).
CONCLUSION
The ANN model demonstrated effective stratification of patients into high- and low-risk groups for PVT development over three and five years. Validation in an independent cohort confirmed the model's predictive accuracy.
Core Tip: An artificial neural network was developed to predict portal vein thrombosis (PVT) risk in hepatitis B-induced cirrhosis. The model outperformed existing scoring systems in predicting PVT incidence at three and five years intervals. Decision curve analysis and calibration curves highlighted superior clinical utility and benefits.
