title: Noninvasive scoring algorithm to identify significant liver fibrosis among treatment-naive chronic hepatitis C patients.
abstract: OBJECTIVE
  Staging for liver fibrosis is recommended in the management of hepatitis C as an argument for treatment priority. Our aim was to construct a noninvasive algorithm to predict the significant liver fibrosis (SLF) using common biochemical markers and compare it with some existing models.
  
  
  METHODS
  The study group included 104 consecutive cases; SLF was defined as Ishak fibrosis stage greater than 2. The patient population was assigned randomly to the training and the validation groups of 52 cases each. The training group was used to construct the algorithm from parameters with the best predictive value. Each parameter was assigned a score that was added to the noninvasive fibrosis score (NFS). The accuracy of NFS in predicting SLF was tested in the validation group and compared with APRI, FIB4, and Forns models.
  
  
  RESULTS
  Our algorithm used age, alkaline phosphatase, ferritin, APRI, α2 macroglobulin, and insulin and the NFS ranged from -4 to 5. The probability of SLF was 2.6 versus 77.1% in NFS<0 and NFS>0, leaving NFS=0 in a gray zone (29.8% of cases). The area under the receiver operating curve was 0.895 and 0.886, with a specificity, sensitivity, and diagnostic accuracy of 85.1, 92.3, and 87.5% versus 77.8, 100, and 87.9% for the training and the validation group. In comparison, the area under the receiver operating curve for APRI=0.810, FIB4=0.781, and Forns=0.703 with a diagnostic accuracy of 83.9, 72.3, and 62% and gray zone cases in 46.15, 37.5, and 44.2%.
  
  
  CONCLUSIONS
  We devised an algorithm to calculate the NFS to predict SLF with good accuracy, fewer cases in the gray zone, and a straightforward clinical interpretation. NFS could be used for the initial evaluation of the treatment priority.
authors.affiliation: a5th Department of Internal Medicine, Comenius University Medical Faculty and University Hospital Bratislava bDepartment of Pathology, Alpha Medical s.r.o., Bratislava, Slovakia.
authors.forename: Tomas
authors.initials: T
authors.lastname: Koller
authors.forename: Jana
authors.initials: J
authors.lastname: Kollerova
authors.forename: Martin
authors.initials: M
authors.lastname: Huorka
authors.forename: Iveta
authors.initials: I
authors.lastname: Meciarova
authors.forename: Juraj
authors.initials: J
authors.lastname: Payer
mesh_terms: D000328:Adult; D000367:Age Factors; D000465:Algorithms; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood*; D001706:Biopsy; D005260:Female; D019698:Hepatitis C, Chronic / Q000150:complications* / Q000175:diagnosis; D006801:Humans; D008099:Liver / Q000378:metabolism* / Q000473:pathology / Q000821:virology; D008103:Liver Cirrhosis / Q000097:blood / Q000175:diagnosis* / Q000821:virology; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D012372:ROC Curve; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D018154:Slovakia; D055815:Young Adult
chemical_list: D015415:Biomarkers