title: Validation and comparison of simple noninvasive models for the prediction of liver fibrosis in chronic hepatitis C.
abstract: BACKGROUND
  Although it is standard procedure in the evaluation of liver diseases, biopsy is an invasive method subject to sampling error and intra or inter-observer variability. Thus, surrogate markers of liver fibrosis have been proposed, with variable availability and accuracy.
  
  
  OBJECTIVE
  Validate and compare the performance of APRI and FIB-4 as predictors of liver fibrosis in HCV patients.
  
  
  METHODS
  Cross-sectional study including patients with HCV-RNA (+) who underwent liver biopsy. Significant fibrosis was defined as METAVIR stage ≥ 2. The diagnostic performance of the models in predicting significant fibrosis were evaluated and compared by ROC curves.
  
  
  RESULTS
  The study included 119 patients, mean age 43.7 ± 10.6 years and 62% males. Significant fibrosis was identified in 41 patients. The AUROCs observed were: APRI = 0.793 ± 0.047, FIB-4 = 0.811 ± 0.045 and AST/ALT = 0.661 ± 0.055 (P = 0.054 for APRI vs. AST/ALT, and P = 0.014 for FIB-4 vs. AST/ALT). Considering classic cutoffs, the PPV and NPV for APRI and FIB-4 were, respectively, 77% and 92% and 83% and 81%. Thirteen (19%) patients were misdiagnosed by APRI and 16 (18%) by FIB-4. By restricting the indication of liver biopsy to patients with intermediate values, it could have been correctly avoided in 47% and 63% of the patients with APRI and FIB-4, respectively.
  
  
  CONCLUSIONS
  The models APRI and FIB-4 were superior to AST/ALT ratio in the diagnosis of significant fibrosis in chronic HCV infection. Even though the overall performance of APRI and FIB-4 was similar, a higher proportion of patients may be correctly classified by FIB-4.
authors.affiliation: Núcleo de Estudos em Gastroenterologia e Hepatologia, Gastroenterology Division, Federal University of Santa Catarina. Brazil.
authors.forename: Thabata Glenda Fenili
authors.initials: TG
authors.lastname: Amorim
authors.forename: Guilherme Jönck
authors.initials: GJ
authors.lastname: Staub
authors.forename: César
authors.initials: C
authors.lastname: Lazzarotto
authors.forename: André Pacheco
authors.initials: AP
authors.lastname: Silva
authors.forename: Joice
authors.initials: J
authors.lastname: Manes
authors.forename: Maria da Graça
authors.initials: Mda G
authors.lastname: Ferronato
authors.forename: Maria Beatriz Cacese
authors.initials: MB
authors.lastname: Shiozawa
authors.forename: Janaína Luz
authors.initials: JL
authors.lastname: Narciso-Schiavon
authors.forename: Esther Buzaglo
authors.initials: EB
authors.lastname: Dantas-Correa
authors.forename: Leonardo de Lucca
authors.initials: Lde L
authors.lastname: Schiavon
mesh_terms: D000328:Adult; D000367:Age Factors; D000410:Alanine Transaminase / Q000097:blood; D019540:Area Under Curve; D001219:Aspartate Aminotransferases / Q000097:blood; D015415:Biomarkers / Q000097:blood; D001706:Biopsy; D016009:Chi-Square Distribution; D003430:Cross-Sectional Studies; D003951:Diagnostic Errors; D005260:Female; D016174:Hepacivirus / Q000235:genetics; D019698:Hepatitis C, Chronic / Q000150:complications* / Q000175:diagnosis; D006801:Humans; D008099:Liver / Q000473:pathology* / Q000821:virology; D008103:Liver Cirrhosis / Q000097:blood / Q000175:diagnosis* / Q000473:pathology / Q000821:virology; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D010976:Platelet Count; D011237:Predictive Value of Tests; D012367:RNA, Viral / Q000097:blood; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012720:Severity of Illness Index; D019564:Unnecessary Procedures
chemical_list: D015415:Biomarkers; D012367:RNA, Viral; D001219:Aspartate Aminotransferases; D000410:Alanine Transaminase