title: A practical scoring system for predicting cirrhosis in patients with chronic viral hepatitis.
abstract: OBJECTIVE
  The purpose of the current study was to develop a simple model for predicting cirrhosis in chronic viral hepatitis patients and to evaluate the usefulness of decision tree algorithms.
  
  
  METHODS
  Serum markers of fibrosis were compared with the stage of fibrosis in liver biopsy specimens prospectively obtained from 526 subjects with chronic HBV and HCV infections (estimation set, 367; validation set, 159).
  
  
  RESULTS
  Univariate analysis revealed that age, bilirubin, platelet count, APRI, ALP, hyaluronic acid (HA), α2-macroglobulin, MMP-2, TIMP-1, and procollagen III N-terminal peptide (PIIINP) were significantly different between patients with (F4) and without cirrhosis (F0123). Multivariate logistic regression analysis identified platelet count, HA and PIIINP as independent predictors of cirrhosis. We categorized the individual variable into the most appropriate cut-off value by calculating the likelihood ratio for predicting cirrhosis and constructed a score system expressed by the following simple formula: PHP index = platelet score + HA score + PIIINP score. For predicting cirrhosis, the area under the receiver operating characteristic curve (AUROC) was 0.824 and 0.759 in the estimation and validation set, respectively. Using a cut-off score of 4, the presence of cirrhosis was predicted with high accuracy. The diagnostic performance of the PHP index was similar to decision tree algorithms (AUROC=0.819) for predicting liver cirrhosis, but more useful in clinical situations.
  
  
  CONCLUSIONS
  Compared to a decision tree model, a simple score system using a categorized value based on a combination of platelet count, HA and PIIINP identified patients with liver cirrhosis with a higher clinical usability.
authors.affiliation: Department of Gastroenterology, Ajou University School of Medicine, Suwon, South Korea.
authors.forename: Jae Youn
authors.initials: JY
authors.lastname: Cheong
authors.forename: Soon Ho
authors.initials: SH
authors.lastname: Um
authors.forename: Yeon Seok
authors.initials: YS
authors.lastname: Seo
authors.forename: Seung Soo
authors.initials: SS
authors.lastname: Shin
authors.forename: Rae Woong
authors.initials: RW
authors.lastname: Park
authors.forename: Dong Joon
authors.initials: DJ
authors.lastname: Kim
authors.forename: Seong Gyu
authors.initials: SG
authors.lastname: Hwang
authors.forename: Youn Jae
authors.initials: YJ
authors.lastname: Lee
authors.forename: Mong
authors.initials: M
authors.lastname: Cho
authors.forename: Jin Mo
authors.initials: JM
authors.lastname: Yang
authors.forename: Young Bae
authors.initials: YB
authors.lastname: Kim
authors.forename: Young Nyun
authors.initials: YN
authors.lastname: Park
authors.forename: Sung Won
authors.initials: SW
authors.lastname: Cho
mesh_terms: D000328:Adult; D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D001706:Biopsy; D016009:Chi-Square Distribution; D003661:Decision Support Techniques; D003663:Decision Trees; D005260:Female; D019694:Hepatitis B, Chronic / Q000097:blood / Q000150:complications* / Q000175:diagnosis; D019698:Hepatitis C, Chronic / Q000097:blood / Q000150:complications* / Q000175:diagnosis; D006801:Humans; D006820:Hyaluronic Acid / Q000097:blood; D016013:Likelihood Functions; D008103:Liver Cirrhosis / Q000097:blood / Q000175:diagnosis / Q000821:virology*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D010446:Peptide Fragments / Q000097:blood; D010976:Platelet Count; D011237:Predictive Value of Tests; D011347:Procollagen / Q000097:blood; D011446:Prospective Studies; D012372:ROC Curve; D015203:Reproducibility of Results; D056910:Republic of Korea; D018570:Risk Assessment; D012307:Risk Factors
chemical_list: D015415:Biomarkers; D010446:Peptide Fragments; D011347:Procollagen; C041876:procollagen Type III-N-terminal peptide; D006820:Hyaluronic Acid