Construction of a non-invasive model to predict liver fibrosis in HBeAg negative hepatitis B with alanine aminotransferase less than 2 upper limit of normal

ObjectiveTo evaluate influencing factors of liver fibrosis in HBeAg negative hepatitis B with alanine aminotransferase (ALT) less than 2 upper limit of normal(ULN)and establish the non-invasive prediction model to assess the severity of liver fibrosis.

MethodsThe clinical data of 295 patients in HBeAg negative CHB patients with ALT≤2ULN were retrospectively analyzed.The degree of liver fibrosis S≥2 was taken as the discriminant criterion for significant liver fibrosis according to the pathological results of liver puncture.There were 94 cases in the mild group of liver fibrosis (S≤1) and 201 cases in the significant group (S≥2).The independent predictors of liver fibrosis were screened by multivariate logistic regression analysis and non-invasive model was constructed.Finally, the model was evaluated by area under the receiver operating characteristic curve to identify the severity of liver fibrosis.

ResultsMultivariate logistic regression analysis showed that aspartate aminotransferase and hepatitis B core antibody were the independent predictors of liver fibrosis (P < 0.01).The AUC of this model was 0.721(95%CI: 0.660-0.782, P < 0.01).The sensitivity and specificity for the diagnosis of significant liver fibrosis were 60.0% and 74.5%.

ConclusionsThe non-invasive prediction model based on the two indicators of aspartate aminotransferase and hepatitis B core antibody has high diagnostic value for evaluating the severity of liver fibrosis in CHB.