title: Complex non-invasive fibrosis models are more accurate than simple models in non-alcoholic fatty liver disease.
abstract: OBJECTIVE
  Significant hepatic fibrosis is prognostic of liver morbidity and mortality in non-alcoholic 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 bedside 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 vs 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 bedside models across a range of fibrosis.
authors.affiliation: School of Medicine and Pharmacology, The University of Western Australia, Western Australia, Australia. leon.adams@uwa.edu.au
authors.forename: Leon A
authors.initials: LA
authors.lastname: Adams
authors.forename: Jacob
authors.initials: J
authors.lastname: George
authors.forename: Elisabetta
authors.initials: E
authors.lastname: Bugianesi
authors.forename: Enrico
authors.initials: E
authors.lastname: Rossi
authors.forename: W Bastiaan
authors.initials: WB
authors.lastname: De Boer
authors.forename: David
authors.initials: D
authors.lastname: van der Poorten
authors.forename: Helena L I
authors.initials: HL
authors.lastname: Ching
authors.forename: Max
authors.initials: M
authors.lastname: Bulsara
authors.forename: Gary P
authors.initials: GP
authors.lastname: Jeffrey
mesh_terms: D000328:Adult; D000367:Age Factors; D000465:Algorithms; D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D001706:Biopsy; D015992:Body Mass Index; D005234:Fatty Liver / Q000150:complications / Q000175:diagnosis* / Q000473:pathology; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D007558:Italy; D016013:Likelihood Functions; D016014:Linear Models; D008099:Liver / Q000473:pathology*; D008103:Liver Cirrhosis / Q000175:diagnosis* / Q000209:etiology / Q000473:pathology; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009517:New South Wales; D065626:Non-alcoholic Fatty Liver Disease; D010976:Platelet Count; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012720:Severity of Illness Index; D012737:Sex Factors; D014904:Western Australia
chemical_list: D015415:Biomarkers