A non-invasive model for predicting liver fibrosis in HBeAg-positive patients with normal or slightly elevated alanine aminotransferase

Early and accurate diagnosis of liver fibrosis is necessary for HBeAg-positive chronic hepatitis B (CHB) patients with normal or slightly increased alanine aminotransferase (ALT), Liver biopsy and many non-invasive predicting markers have several application restrictions in grass-roots hospitals. We aimed to construct a non-invasive model based on routinely serum markers to predict liver fibrosis for this population. A total of 363 CHB patients with HBeAg-positive, ALT ≤2-fold the upper limit of normal and liver biopsy data were randomly divided into training (n = 266) and validation groups (n = 97). Two non-invasive models were established based on multivariable logistic regression analysis in the training group. Model 2 with a lower Akaike information criterion (AIC) was selected as a better predictive model. Receiver operating characteristic (ROC) was used to evaluate the model and was then independently validated in the validation group. The formula of Model 2 was logit (Model value) = 5.67+0.08 × Age −2.44 × log10 [the quantification of serum HBsAg (qHBsAg)] −0.60 × log10 [the quantification of serum HBeAg (qHBeAg)]+0.02 × ALT+0.03 × aspartate aminotransferase (AST). The area under the ROC curve (AUC) was 0.89 for the training group and 0.86 for the validation group. Using 2 cut-off points of −2.61 and 0.25, 59% of patients could be identified with liver fibrosis and antiviral treatment decisions were made without liver biopsies, and 149 patients were recommended to undergo liver biopsy for accurate diagnosis. In this study, the non-invasive model could predict liver fibrosis and may reduce the need for liver biopsy in HBeAg-positive CHB patients with normal or slightly increased ALT.