title: Prediction of effect of pegylated interferon alpha-2b plus ribavirin combination therapy in patients with chronic hepatitis C infection.
abstract: Treatment with pegylated interferon alpha-2b (PEGIFN) plus ribavirin (RBV) is standard therapy for patients with chronic hepatitis C. Although the effectiveness, patients with high titres of group Ib hepatitis C virus (HCV) respond poorly compared to other genotypes. At present, we cannot predict the effect in an individual. Previous studies have used traditional statistical analysis by assuming a linear relationship between clinical features, but most phenomena in the clinical situation are not linearly related. The aim of this study is to predict the effect of PEG IFN plus RBV therapy on an individual patient level using an artificial neural network system (ANN). 156 patients with HCV group 1b from multiple centres were treated with PEGIFN (1.5 µg/kg) plus RBV (400-1000 mg) for 48 weeks. Data on the patients' demographics, laboratory tests, PEGIFN, and RBV doses, early viral responses (EVR), and sustained viral responses were collected. Clinical data were randomly divided into training data set and validation data set and analyzed using multiple logistic regression analysis (MLRs) and ANN to predict individual outcomes. The sensitivities of predictive expression were 0.45 for the MLRs models and 0.82 for the ANNs and specificities were 0.55 for the MLR and 0.88 for the ANN. Non-linear relation analysis showed that EVR, serum creatinine, initial dose of Ribavirin, gender and age were important predictive factors, suggesting non-linearly related to outcome. In conclusion, ANN was more accurate than MLRs in predicting the outcome of PEGIFN plus RBV therapy in patients with group 1b HCV.
authors.affiliation: Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku, Tokyo, Japan.
authors.forename: Tetsuro
authors.initials: T
authors.lastname: Takayama
authors.forename: Hirotoshi
authors.initials: H
authors.lastname: Ebinuma
authors.forename: Shinichiro
authors.initials: S
authors.lastname: Tada
authors.forename: Yoshiyuki
authors.initials: Y
authors.lastname: Yamagishi
authors.forename: Kanji
authors.initials: K
authors.lastname: Wakabayashi
authors.forename: Keisuke
authors.initials: K
authors.lastname: Ojiro
authors.forename: Takanori
authors.initials: T
authors.lastname: Kanai
authors.forename: Hidetsugu
authors.initials: H
authors.lastname: Saito
authors.forename: Toshifumi
authors.initials: T
authors.lastname: Hibi
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000998:Antiviral Agents / Q000627:therapeutic use*; D005260:Female; D019698:Hepatitis C, Chronic / Q000188:drug therapy*; D006801:Humans; D000077190:Interferon alpha-2; D016898:Interferon-alpha / Q000008:administration & dosage*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011092:Polyethylene Glycols / Q000008:administration & dosage*; D012372:ROC Curve; D011994:Recombinant Proteins / Q000008:administration & dosage; D012044:Regression Analysis; D015203:Reproducibility of Results; D012254:Ribavirin / Q000008:administration & dosage*; D012680:Sensitivity and Specificity
chemical_list: D000998:Antiviral Agents; D000077190:Interferon alpha-2; D016898:Interferon-alpha; D011994:Recombinant Proteins; D011092:Polyethylene Glycols; D012254:Ribavirin; C417083:peginterferon alfa-2b