Pretreatment prediction of response to peginterferon plus ribavirin therapy in genotype 1 chronic hepatitis C using data mining analysis

2011年4月20日日記

 

 

 

 

 

 

 

For patients with chronic hepatits C (particularly genotype 1, considering the interferon treatment), this article is very informative when they have to decide to start the treatment.

日本語では追って投稿致します。

 

Pretreatment prediction of response to peginterferon plus ribavirin therapy in genotype 1 chronic hepatitis C using data mining analysis

Masayuki KurosakiNaoya SakamotoManabu IwasakiMinoru SakamotoYoshiyuki SuzukiNaoki HiramatsuFuminaka SugauchiHiroshi Yatsuhashi and Namiki Izumi

JOURNAL OF GASTROENTEROLOGY

Volume 46, Number 3, 401-409, DOI: 10.1007/s00535-010-0322-5

 

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 × 109/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 (r2 = 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.