title: Precise prediction model and simplified scoring system for sustained combined response to interferon-alpha.
abstract: OBJECTIVE
  To establish a predictive algorithm which may serve for selecting optimal candidates for interferon-alpha (IFN-alpha) treatment.
  
  
  METHODS
  A total of 474 IFN-alpha treated hepatitis B virus e antigen (HBeAg)-positive patients were enrolled in the present study. The patients' baseline characteristics, such as age, gender, blood tests, activity grading (G) of intrahepatic inflammation, score (S) of liver fibrosis, hepatitis B virus (HBV) DNA and genotype were evaluated; therapy duration and response of each patient at the 24th wk after cessation of IFN-alpha treatment were also recorded. A predictive algorithm and scoring system for a sustained combined response (CR) to IFN-alpha therapy were established. About 10% of the patients were randomly drawn as the test set. Responses to IFN-alpha therapy were divided into CR, partial response (PR) and non-response (NR). The mixed set of PR and NR was recorded as PR+NR.
  
  
  RESULTS
  Stratified by therapy duration, the most significant baseline predictive factors were alanine aminotransferase (ALT), HBV DNA level, aspartate aminotransferase (AST), HBV genotype, S, G, age and gender. According to the established model, the accuracies for sustained CR and PR+NR, respectively, were 86.4% and 93.0% for the training set, 81.5% and 91.0% for the test set. For the scoring system, the sensitivity and specificity were 78.8% and 80.6%, respectively. There were positive correlations between ALT and AST, and G and S, respectively.
  
  
  CONCLUSIONS
  With these models, practitioners may be able to propose individualized decisions that have an integrated foundation on both evidence-based medicine and personal characteristics.
authors.affiliation: Hepatology Unit and Department of Infectious Diseases, Xiamen Hospital of Traditional Chinese Medicine, Xiamen 361000, Fujian Province, China.
authors.forename: Qian-Guo
authors.initials: QG
authors.lastname: Mao
authors.forename: Jin-Shui
authors.initials: JS
authors.lastname: Pan
authors.forename: Kuang-Nan
authors.initials: KN
authors.lastname: Fang
authors.forename: Ru-Mian
authors.initials: RM
authors.lastname: Zhang
authors.forename: Qing-Yang
authors.initials: QY
authors.lastname: Hong
authors.forename: Min-Ning
authors.initials: MN
authors.lastname: Song
authors.forename: Jian-Ping
authors.initials: JP
authors.lastname: Zhu
authors.forename: Wen-Qi
authors.initials: WQ
authors.lastname: Huang
authors.forename: Li-Min
authors.initials: LM
authors.lastname: Chen
authors.forename: Mei-Zhu
authors.initials: MZ
authors.lastname: Hong
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D005260:Female; D006513:Hepatitis B e Antigens / Q000097:blood / Q000276:immunology; D006515:Hepatitis B virus / Q000235:genetics / Q000276:immunology; D019694:Hepatitis B, Chronic / Q000097:blood / Q000188:drug therapy* / Q000276:immunology / Q000473:pathology; D006801:Humans; D016898:Interferon-alpha / Q000627:therapeutic use*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012372:ROC Curve; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D006513:Hepatitis B e Antigens; D016898:Interferon-alpha