Performance of non-invasive models of fibrosis in predicting mild to moderate fibrosis in patients with non-alcoholic fatty liver disease

Abstract

Background & aims: In non-alcoholic fatty liver disease, presence of fibrosis is predictive of long-term liver-related complications. Currently, there are no reliable and non-invasive means of quantifying fibrosis in those with non-alcoholic fatty liver disease. Therefore, we aimed to evaluate the performance of a panel of non-invasive models in predicting fibrosis in non-alcoholic fatty liver disease.

Methods: The accuracy of FibroMeter non-alcoholic fatty liver disease, fibrosis 4 and four other non-invasive models in predicting fibrosis in those with biopsy proven non-alcoholic fatty liver disease was compared. These models were constructed post hoc in patients who had necessary clinical information collected within 2 months of a liver biopsy. The areas under receiver operating characteristics curves were compared for each model using Delong analysis. Optimum cut-off for each model and fibrosis stage were calculated using the Youden index.

Results: The area under receiver operating characteristics curves for F ≥ 1 fibrosis for fibrosis 4 and FibroMeter non-alcoholic fatty liver disease was 0.821 and 0.801 respectively. For F ≥ 3, the area under receiver operating characteristics curves was 0.866 for fibrosis 4 and 0.862 for FibroMeter non-alcoholic fatty liver disease. Delong analysis showed the area under receiver operating characteristics curves was statistically different for fibrosis 4 and FibroMeter non-alcoholic fatty liver disease compared with BARD, BAAT and aspartate aminotransferase:alanine aminotransferase ratio for F ≥ 1 and F ≥ 3. Area under receiver operating characteristics curves were significantly different for fibrosis 4 and FibroMeter non-alcoholic fatty liver disease for F ≥ 3 compared with non-alcoholic fatty liver disease fibrosis score. At a fixed sensitivity of 90%, FibroMeter non-alcoholic fatty liver disease had the highest specificity for F ≥ 1 (52.4%) and F ≥ 3 (63.8%). In contrast, at a fixed specificity of 90%, fibrosis 4 outperformed other models with a sensitivity of 60.2% for F ≥ 1 and 70.6% for F ≥ 3 fibrosis.

Conclusion: These non-invasive models of fibrosis can predict varying degrees of fibrosis from routinely collected clinical information in non-alcoholic fatty liver disease.

Keywords: FibroMeter non-alcoholic fatty liver disease; fibrosis; fibrosis 4; non-alcoholic fatty liver disease.