Article Title

Complex non-invasive fibrosis models are more accurate than simple models in non-alcoholic fatty liver disease


Background and Aim: Significant hepatic fibrosis is prognostic of liver morbidity and mortality in nonalcoholic fatty liver disease (NAFLD), however it remains unclear whether non-invasive fibrosis models can determine this end-point. We therefore compared the accuracy of simple bed-side versus complex fibrosis models across a range of fibrosis in a multi-centre NAFLD cohort.

Methods: Simple (APRI, BARD) and complex (Hepascore, Fibrotest, FIB4) fibrosis models were calculated in 242 NAFLD subjects undergoing liver biopsy. Significant (F2-4) and advanced fibrosis (F3,4) were defined using Kleiner criteria. Models were compared using area under the receiver operator characteristic curves (AUC). Cut-offs were determined by Youden Index or 90% predictive values.

Results: For significant fibrosis, non-invasive fibrosis models had modest accuracy (AUC 0.707-0.743) with BARD being least accurate (AUC 0.609, p < 0.05 versus others). Using single cut-offs, sensitivities and predictive values were <80%; using two cut-offs, >75% of subjects fell within indeterminate ranges. Simple models had significantly more subjects within indeterminate ranges than complex models (99.1-100% vs. 82.1-84.4% respectively, p < 0.05 for all). For advanced fibrosis, complex models were more accurate than BARD (AUC 0.802-0.858 vs. 0.701, p < 0.05). Using two cut-offs, complex models had fewer individuals within indeterminate ranges than BARD (11.1%-32.3% vs. 70.7%, p < 0.01 for all). For cirrhosis, complex models had higher AUC values than simple models.

Conclusions: In NAFLD subjects, non-invasive models have modest accuracy for determining significant fibrosis and have predictive values less than 90% in the majority of subjects. Complex models are more accurate than simple bed-side models across a range of fibrosis.


peer-reviewed, non-alcoholic fatty liver disease, fibrosis, liver biopsy, accuracy


Link to Publisher Version (DOI)