title: Performance of several simple, noninvasive models for assessing significant liver fibrosis in patients with chronic hepatitis B.
abstract: OBJECTIVE
  To compare the performance of several simple, noninvasive models comprising various serum markers in diagnosing significant liver fibrosis in the same sample of patients with chronic hepatitis B (CHB) with the same judgment standard.
  
  
  METHODS
  A total of 308 patients with CHB who had undergone liver biopsy, laboratory tests, and liver stiffness measurement (LSM) at the Southwest Hospital, Chongqing, China between March 2010 and April 2014 were retrospectively studied. Receiver operating characteristic (ROC) curves and area under ROC curves (AUROCs) were used to analyze the results of the models, which incorporated age-platelet (PLT) index (API model), aspartate transaminase (AST) to alanine aminotransferase (ALT) ratio (AAR model), AST to PLT ratio index (APRI model), γ-glutamyl transpeptidase (GGT) to PLT ratio index (GPRI model), GGT-PLT-albumin index (S index model), age-AST-PLT-ALT index (FIB-4 model), and age-AST-PLT-ALT-international normalized ratio index (Fibro-Q model).
  
  
  RESULTS
  The AUROCs of the S index, GPRI, FIB-4, APRI, API, Fibro-Q, AAR, and LSM for predicting significant liver fibrosis were 0.726 (P<0.001), 0.726 (P<0.001), 0.621 (P=0.001), 0.619 (P=0.001), 0.580 (P=0.033), 0.569 (P=0.066), 0.495 (P=0.886), and 0.757 (P<0.001), respectively. The S index and GPRI had the highest correlation with histopathological scores (r=0.373, P<0.001; r=0.372, P<0.001, respectively) and LSM values (r=0.516, P<0.001; r=0.513, P<0.001, respectively). When LSM was combined with S index and GPRI, the AUROCs were 0.753 (P<0.001) and 0.746 (P<0.001), respectively.
  
  
  CONCLUSIONS
  S index and GPRI had the best diagnostic performance for significant liver fibrosis and were robust predictors of significant liver fibrosis in patients with CHB for whom transient elastography was unavailable.
authors.forename: Xianghua
authors.initials: X
authors.lastname: Zeng
authors.forename: Cheng
authors.initials: C
authors.lastname: Xu
authors.forename: Dengming
authors.initials: D
authors.lastname: He
authors.forename: Maoshi
authors.initials: M
authors.lastname: Li
authors.forename: Huiyan
authors.initials: H
authors.lastname: Zhang
authors.forename: Quanxin
authors.initials: Q
authors.lastname: Wu
authors.forename: Dedong
authors.initials: D
authors.lastname: Xiang
authors.affiliation: Yuming Wang, Department of Infectious Diseases, Southwest Hospital, Third Military Medical University, Chongqing 400038, China, wym417@163.com.
authors.forename: Yuming
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000328:Adult; D000367:Age Factors; D000410:Alanine Transaminase / Q000097:blood; D019540:Area Under Curve; D001219:Aspartate Aminotransferases / Q000097:blood; D015415:Biomarkers; D001706:Biopsy; D001792:Blood Platelets / Q000166:cytology; D002681:China; D005260:Female; D019694:Hepatitis B, Chronic / Q000150:complications*; D006801:Humans; D008103:Liver Cirrhosis / Q000175:diagnosis* / Q000209:etiology*; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012189:Retrospective Studies; D005723:gamma-Glutamyltransferase / Q000097:blood
chemical_list: D015415:Biomarkers; D005723:gamma-Glutamyltransferase; D001219:Aspartate Aminotransferases; D000410:Alanine Transaminase