Establish and validate an artificial neural networks model used for predicting portal vein thrombosis risk in hepatitis B-related cirrhosis patients.

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.