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| Home > Articles > Published articles > Baseline Prediction of Combination Therapy Outcome in Hepatitis C Virus 1b Infected Patients by Discriminant Analysis Using Viral and Host Factors |
(Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol) (Universitat Autònoma de Barcelona. Departament de Matemàtiques) (Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública) (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol) (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)| Date: | 2010 |
| Abstract: | Current treatment of chronic hepatitis C virus (HCV) infection has limited efficacy −especially among genotype 1 infected patients−, is costly, and involves severe side effects. Thus, predicting non-response is of major interest for both patient wellbeing and health care expense. At present, treatment cannot be individualized on the basis of any baseline predictor of response. We aimed to identify pre-treatment clinical and virological parameters associated with treatment failure, as well as to assess whether therapy outcome could be predicted at baseline. Forty-three HCV subtype 1b (HCV-1b) chronically infected patients treated with pegylated-interferon alpha plus ribavirin were retrospectively studied (21 responders and 22 non-responders). Host (gender, age, weight, transaminase levels, fibrosis stage, and source of infection) and viral-related factors (viral load, and genetic variability in the E1-E2 and Core regions) were assessed. Logistic regression and discriminant analyses were used to develop predictive models. A "leave-one-out" cross-validation method was used to assess the reliability of the discriminant models. Lower alanine transaminase levels (ALT, p = 0. 009), a higher number of quasispecies variants in the E1-E2 region (number of haplotypes, nHap_E1-E2) (p = 0. 003), and the absence of both amino acid arginine at position 70 and leucine at position 91 in the Core region (p = 0. 039) were significantly associated with treatment failure. Therapy outcome was most accurately predicted by discriminant analysis (90. 5% sensitivity and 95. 5% specificity, 85. 7% sensitivity and 81. 8% specificity after cross-validation); the most significant variables included in the predictive model were the Core amino acid pattern, the nHap_E1-E2, and gamma-glutamyl transferase and ALT levels. Discriminant analysis has been shown as a useful tool to predict treatment outcome using baseline HCV genetic variability and host characteristics. The discriminant models obtained in this study led to accurate predictions in our population of Spanish HCV-1b treatment naïve patients. |
| Grants: | Ministerio de Ciencia e Innovación CP09/00044 Ministerio de Sanidad y Consumo CD05/00258 Ministerio de Ciencia e Innovación CP09/00044 Ministerio de Sanidad y Consumo CP07/00078 Ministerio de Ciencia e Innovación BFU2008-03000 |
| Rights: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.  |
| Language: | Anglès |
| Document: | Article ; recerca ; Versió publicada |
| Published in: | PloS one, Vol. 5 (november 2010) , ISSN 1932-6203 |
