title: Pretreatment prediction of response to peginterferon plus ribavirin therapy in genotype 1 chronic hepatitis C using data mining analysis.
abstract: BACKGROUND
  This study aimed to develop a model for the pre-treatment prediction of sustained virological response (SVR) to peg-interferon plus ribavirin therapy in chronic hepatitis C.
  
  
  METHODS
  Data from 800 genotype 1b chronic hepatitis C patients with high viral load (>100,000 IU/ml) treated by peg-interferon plus ribavirin at 6 hospitals in Japan were randomly assigned to a model building (n = 506) or an internal validation (n = 294). Data from 524 patients treated at 29 hospitals in Japan were used for an external validation. Factors predictive of SVR were explored using data mining analysis.
  
  
  RESULTS
  Age (<50 years), alpha-fetoprotein (AFP) (<8 ng/mL), platelet count (≥ 120 × 10(9)/l), gamma-glutamyltransferase (GGT) (<40 IU/l), and male gender were used to build the decision tree model, which divided patients into 7 subgroups with variable rates of SVR ranging from 22 to 77%. The reproducibility of the model was confirmed by the internal and external validation (r (2) = 0.92 and 0.93, respectively). When reconstructed into 3 groups, the rate of SVR was 75% for the high probability group, 44% for the intermediate probability group and 23% for the low probability group. Poor adherence to drugs lowered the rate of SVR in the low probability group, but not in the high probability group.
  
  
  CONCLUSIONS
  A decision tree model that includes age, gender, AFP, platelet counts, and GGT is useful for predicting the probability of response to therapy with peg-interferon plus ribavirin and has the potential to support clinical decisions regarding the selection of patients for therapy.
authors.affiliation: Division of Gastroenterology and Hepatology, Musashino Red Cross Hospital, 1-26-1 Kyonan-cho, Musashino, Tokyo 180-8610, Japan. kurosaki@musashino.jrc.or.jp
authors.forename: Masayuki
authors.initials: M
authors.lastname: Kurosaki
authors.forename: Naoya
authors.initials: N
authors.lastname: Sakamoto
authors.forename: Manabu
authors.initials: M
authors.lastname: Iwasaki
authors.forename: Minoru
authors.initials: M
authors.lastname: Sakamoto
authors.forename: Yoshiyuki
authors.initials: Y
authors.lastname: Suzuki
authors.forename: Naoki
authors.initials: N
authors.lastname: Hiramatsu
authors.forename: Fuminaka
authors.initials: F
authors.lastname: Sugauchi
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Yatsuhashi
authors.forename: Namiki
authors.initials: N
authors.lastname: Izumi
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000998:Antiviral Agents / Q000627:therapeutic use*; D057225:Data Mining; D003663:Decision Trees; D004337:Drug Carriers / Q000627:therapeutic use; D005260:Female; D005838:Genotype; D019698:Hepatitis C, Chronic / Q000097:blood / Q000188:drug therapy* / Q000235:genetics*; D006801:Humans; D007372:Interferons / Q000627:therapeutic use*; D008297:Male; D008875:Middle Aged; D010976:Platelet Count; D011092:Polyethylene Glycols / Q000627:therapeutic use; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012254:Ribavirin / Q000627:therapeutic use*; D012737:Sex Factors; D016896:Treatment Outcome; D055815:Young Adult; D000509:alpha-Fetoproteins; D005723:gamma-Glutamyltransferase / Q000097:blood
chemical_list: D000998:Antiviral Agents; D004337:Drug Carriers; D000509:alpha-Fetoproteins; D011092:Polyethylene Glycols; D012254:Ribavirin; D007372:Interferons; D005723:gamma-Glutamyltransferase