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J Viral Hepat. 2008 Apr;15(4):271-8. Epub 2007 Dec 11.

Response rates to combination therapy for chronic HCV infection in a clinical setting and derivation of probability tables for individual patient management.

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  • 1Department of Microbiology and Infectious Diseases, University of Nottingham, Nottingham, UK.

Abstract

Evidence for efficacy of established treatment guidelines for chronic hepatitis C virus (HCV) disease is based on multinational randomized controlled trials (RCTs). Strategies for managing HCV, however, require an assessment of the effectiveness of intervention in routine clinical practice. We report the outcomes of combination therapy in a large cohort of HCV-infected individuals in the UK. A total of 347 (113 genotype 1, 234 genotype non-1) patients were treated with pegylated interferon and ribavirin according to current guidelines. Forty-two (37.2%) of those with genotype 1 infection and 164 (70.1%) with genotype non-1 infection achieved sustained viral response (SVR). Thirty-nine (11%) patients withdrew from treatment. In addition to viral genotype, factors predictive of a response to therapy were age at start of treatment and disease stage on pretreatment liver biopsy. Multivariate regression analysis demonstrated that the effects of age [odds ratio 0.5; 95% confidence interval (0.31-0.82) per 10-year increment (P = 0.006)] were confined to genotype 1 disease. In order to further inform the management of the individual patient, a multivariate logistic model was used to predict the probability of SVR for subgroups defined by disease stage, genotype and age at commencement of therapy. This model revealed striking differences in predicted response rates between subgroups and provided a strong rationale for early treatment, particularly for those with genotype 1 disease. Our study demonstrates that results comparable with those of RCTs can be achieved in clinical practice, and suggests that prediction of response rates based on probability modelling will provide a valuable adjunct to individual patient management.

PMID:
18086181
[PubMed - indexed for MEDLINE]
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