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Saludes V, Bascuñana E, Jordana-Lluch E, Casanovas S, Ardèvol M, Soler E, Planas R, Ausina V, Martró E. Relevance of baseline viral genetic heterogeneity and host factors for treatment outcome prediction in hepatitis C virus 1b-infected patients. PLoS One 2013; 8:e72600. [PMID: 24015264 PMCID: PMC3755994 DOI: 10.1371/journal.pone.0072600] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Accepted: 07/10/2013] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Only about 50% of patients chronically infected with HCV genotype 1 (HCV-1) respond to treatment with pegylated interferon-alfa and ribavirin (dual therapy), and protease inhibitors have to be administered together with these drugs increasing costs and side-effects. We aimed to develop a predictive model of treatment response based on a combination of baseline clinical and viral parameters. METHODOLOGY Seventy-four patients chronically infected with HCV-1b and treated with dual therapy were studied (53 retrospectively -training group-, and 21 prospectively -validation group-). Host and viral-related factors (viral load, and genetic variability in the E1-E2, core and Interferon Sensitivity Determining Region) were assessed. Multivariate discriminant analysis and decision tree analysis were used to develop predictive models on the training group, which were then validated in the validation group. PRINCIPAL FINDINGS A multivariate discriminant predictive model was generated including the following variables in decreasing order of significance: the number of viral variants in the E1-E2 region, an amino acid substitution pattern in the viral core region, the IL28B polymorphism, serum GGT and ALT levels, and viral load. Using this model treatment outcome was accurately predicted in the training group (AUROC = 0.9444; 96.3% specificity, 94.7% PPV, 75% sensitivity, 81% NPV), and the accuracy remained high in the validation group (AUROC = 0.8148, 88.9% specificity, 90.0% PPV, 75.0% sensitivity, 72.7% NPV). A second model was obtained by a decision tree analysis and showed a similarly high accuracy in the training group but a worse reproducibility in the validation group (AUROC = 0.9072 vs. 0.7361, respectively). CONCLUSIONS AND SIGNIFICANCE The baseline predictive models obtained including both host and viral variables had a high positive predictive value in our population of Spanish HCV-1b treatment naïve patients. Accurately identifying those patients that would respond to the dual therapy could help reducing implementation costs and additional side effects of new treatment regimens.
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Affiliation(s)
- Verónica Saludes
- Microbiology Service, Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Elisabet Bascuñana
- Microbiology Service, Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
| | - Elena Jordana-Lluch
- Microbiology Service, Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
| | - Sònia Casanovas
- Microbiology Service, Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
| | - Mercè Ardèvol
- Hospital Pharmacy, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
| | - Esther Soler
- Liver Unit, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
- CIBER Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Ramón Planas
- Liver Unit, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
- CIBER Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Vicente Ausina
- Microbiology Service, Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- CIBER Enfermedades Respiratorias (CIBERES), Bunyola, Spain
| | - Elisa Martró
- Microbiology Service, Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
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