title: Liver fibrosis staging through a stepwise analysis of non-invasive markers (FibroSteps) in patients with chronic hepatitis C infection.
abstract: BACKGROUND
  Non-invasive fibrosis markers can distinguish between liver fibrosis stages in lieu of liver biopsy or imaging elastography.
  
  
  OBJECTIVE
  To develop a sensitive, non-invasive, freely-available algorithm that differentiates between individual liver fibrosis stages in chronic hepatitis C virus (HCV) patients.
  
  
  METHODS
  Chronic HCV patients (n = 355) at Cairo University Hospital, Egypt, with liver biopsy to determine fibrosis stage (METAVIR), were tested for preselected fibrosis markers. A novel multistage stepwise fibrosis classification algorithm (FibroSteps) was developed using random forest analysis for biomarker selection, and logistic regression for modelling. FibroSteps predicted fibrosis stage using four steps: Step 1 distinguished no(F0)/mild fibrosis(F1) vs. moderate(F2)/severe fibrosis(F3)/cirrhosis(F4); Step 2a distinguished F0 vs. F1; Step 2b distinguished F2 vs. F3/F4; and Step 3 distinguished F3 vs. F4. FibroSteps was developed using a randomly-selected training set (n = 234) and evaluated using the remaining patients (n = 118) as a validation set.
  
  
  RESULTS
  Hyaluronic Acid, TGF-β1, α2-macroglobulin, MMP-2, Apolipoprotein-A1, Urea, MMP-1, alpha-fetoprotein, haptoglobin, RBCs, haemoglobin and TIMP-1 were selected into the models, which had areas under the receiver operating curve (AUC) of 0.973, 0.923 (Step 1); 0.943, 0.872 (Step 2a); 0.916, 0.883 (Step 2b) and 0.944, 0.946 (Step 3), in the training and validation sets respectively. The final classification had accuracies of 94.9% (95% CI: 91.3-97.4%) and 89.8% (95% CI: 82.9-94.6%) for the training and validation sets respectively.
  
  
  CONCLUSIONS
  FibroSteps, a freely available, non-invasive liver fibrosis classification, is accurate and can assist clinicians in making prognostic and therapeutic decisions. The statistical code for FibroSteps using R software is provided in the supplementary materials.
authors.affiliation: Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA. selkamar@epi.umaryland.edu
authors.forename: Samer S
authors.initials: SS
authors.lastname: El-Kamary
authors.forename: Mona M
authors.initials: MM
authors.lastname: Mohamed
authors.forename: Maissa
authors.initials: M
authors.lastname: El-Raziky
authors.forename: Michelle D
authors.initials: MD
authors.lastname: Shardell
authors.forename: Olfat G
authors.initials: OG
authors.lastname: Shaker
authors.forename: Wafaa A
authors.initials: WA
authors.lastname: ElAkel
authors.forename: Gamal
authors.initials: G
authors.lastname: Esmat
mesh_terms: D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood*; D004534:Egypt; D005260:Female; D006526:Hepatitis C / Q000150:complications* / Q000473:pathology; D006801:Humans; D008103:Liver Cirrhosis / Q000097:blood / Q000145:classification* / Q000175:diagnosis* / Q000209:etiology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D012372:ROC Curve; D018709:Statistics, Nonparametric
chemical_list: D015415:Biomarkers
keywords: chronic hepatitis; fibrosis markers; hepatitis C virus; liver fibrosis; logistic regression