Usefulness of Multiple Biomarkers for the Prediction of Significant Fibrosis in Chronic Hepatitis B

Background/Aims: It has not been explicitly addressed whether new biomarkers, in addition to readily available clinical data, make a contribution to the prediction of liver fibrosis. Methods: A total of 209 patients with chronic hepatitis B who underwent liver biopsy were recruited. The Clinical Score (CS) model using only routine clinical data and the Biomarker Score (BS) model using 7 putative biomarkers were derived from the derivation set (n = 105), and these models were applied to a separate patients group (n = 104) to investigate whether the addition of BS improved the diagnostic accuracy in predicting significant fibrosis beyond an assessment based solely on CS. Results: The most informative biomarkers for predicting significant fibrosis were hyaluronic acid and matrix metalloproteinase-2. The BS was an independent predictive factor for significant fibrosis even after accounting for CS in both sets. Among the derivation set, the incorporation of the BS into the CS model did not significantly increase the receiver operating characteristic area, with only a small improvement of about 2% (P = 0.11). Similarly, in the validation set, a combined model with CS and BS showed no superior diagnostic accuracy over the CS model alone, with an improvement of approximately 2% [0.83 (0.75 to 0.92) vs. 0.81 (0.70 to 0.91); P = 0.16]. Conclusions: The simultaneous addition of several biomarkers adds only modestly to clinical predictive factors for risk assessment of individual patients. These results highlight the need for the models to be validated in another cohort with a broader distribution of fibrosis severity.