A predictive model of response to peginterferon ribavirin in chronic hepatitis C using classification and regression tree analysis

Aim: Early disappearance of serum hepatitis C virus (HCV) RNA is the prerequisite for achieving sustained virological response (SVR) in peg-interferon (PEG-IFN) plus ribavirin (RBV) therapy for chronic hepatitis C. This study aimed to develop a decision tree model for the pre-treatment prediction of response. Methods: Genotype 1b chronic hepatitis C treated with PEG-IFN alpha-2b and RBV were studied. Predictive factors of rapid or complete early virological response (RVR/cEVR) were explored in 400 consecutive patients using a recursive partitioning analysis, referred to as classification and regression tree (CART) and validated. Results: CART analysis identified hepatic steatosis (< 30%) as the first predictor of response followed by low-density-lipoprotein cholesterol (LDL-C) (>= 100 mg/dL), age (< 50 and < 60 years), blood sugar (< 120 mg/dL), and gamma-glutamyltransferase (GGT) (< 40 IU/L) and built decision tree model. The model consisted of seven groups with variable response rates from low (15%) to high (77%). The reproducibility of the model was confirmed by the independent validation group (r2 = 0.987). When reconstructed into three groups, the rate of RVR/cEVR was 16% for low probability group, 46% for intermediate probability group and 75% for high probability group. Conclusions: A decision tree model that includes hepatic steatosis, LDL-C, age, blood sugar, and GGT may be useful for the prediction of response before PEG-IFN plus RBV therapy, and has the potential to support clinical decisions in selecting patients for therapy and may provide a rationale for treating metabolic factors to improve the efficacy of antiviral therapy.