A noninvasive model to predict liver histology for antiviral therapy decision in chronic hepatitis B with alanine aminotransferase

Background At present, most assessments of liver fibrosis staging mainly focus on non-invasive diagnostic methods. This study aims to construct a noninvasive model to predict liver histology for antiviral therapy in chronic hepatitis B (CHB) with alanine aminotransferase (ALT) Methods We retrospectively analyzed 577 patients with CHB who received liver biopsy and whose ALT was less than 2 ULN. Then they were randomly divided into a training group and a validation group. Through logistic regression analysis, a novel predictive model was constructed in the training group to predict significant changes in liver histology [necro-inflammatory activity grade (G) ≥ 2 or fibrosis stage (S) ≥ 2] and then validated in the validation group. Results If liver biopsy showed moderate or severe inflammation or significant fibrosis, antiviral treatment was recommended. Aspartate aminotransferase (AST), anti-hepatitis B virus core antibody (anti-HBC) and glutamine transpeptidase (GGT) were identified as independent predictors for antiviral therapy, with area under the ROC curve (AUROC) of 0.649, 0.647 and 0.616, respectively. Our novel model index, which combined AST, anti- HBC and GGT with AUROC of 0.700 and 0.742 in training set and validation set. Conclusions This study established a noninvasive model to predict liver histology for antiviral treatment decision in patients with CHB with ALT