Prediction of hepatic inflammation in chronic hepatitis B patients with a random forest-backward feature elimination algorithm.

ABSTRACT:

Background

Persistent liver inflammatory damage is the main risk factor for developing liver fibrosis, cirrhosis, and even hepatocellular carcinoma in chronic hepatitis B (CHB) patients. Thus, accurate prediction of the degree of liver inflammation is a high priority and a growing medical need.

Aim

To build an effective and robust non-invasive model for predicting hepatitis B-related hepatic inflammation.

Methods

A total of 650 treatment-naïve CHB (402 HBeAg-positive and 248 HBeAg-negative) patients who underwent liver biopsy were enrolled in this study. Histological inflammation grading was assessed by the Ishak scoring system. Serum quantitative hepatitis B core antibody (qAnti-HBc) levels and 21 immune-related inflammatory factors were measured quantitatively using

SUBMITTER: Zhou JY 

PROVIDER: S-EPMC8173380 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature