SummaryBackground Depression and anxiety have been associated with interferon treatment and low treatment adherence.Aim To study the incidence and associated risk factors of depressive and anxiety disorders during pegylated interferon plus ribavirin and treatment adherence in a prospective cohort of 176 patients with chronic hepatitis C patients.Methods Patients were interviewed at baseline using the Structured Clinical Interview for DSM‐IV Mental Disorders and the Patient Health Questionnaire and the Hospital Anxiety and Depression Scale were completed. Both questionnaires were completed also after 4, 12 and 24 weeks of treatment.Results De novo depressive and/or anxiety disorders were diagnosed in 53 (36%) patients, in whom antidepressants and/or anxiolytics were administered. Higher baseline depression‐subscale score (OR = 27.8, 95% CI = 2.82–333), primary education level (OR = 3.1, 95% CI = 1.40–7.03) and being an immigrant (OR = 3.2, 95% CI = 1.12–9.47) were predictors of psychiatric disorders during anti‐viral therapy. The percentage of patients with good adherence was lower in those with depression and/or anxiety (79% vs. 90%, P < 0.04). Only one patient (1%) discontinued treatment because of a major depressive episode. Depression and/or anxiety disorders had no effect on attainment of sustained virological response.Conclusion Early detection and treatment of depressive and anxiety disorders favours good adherence to anti‐viral treatment in hepatitis C.
clinicaltrials.gov Identifier: NCT00166296.
BackgroundCurrent 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.MethodologyForty-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.Principal FindingsLower 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.Conclusions and SignificanceDiscriminant 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.
BackgroundOnly 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.MethodologySeventy-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 FindingsA 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 SignificanceThe 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.
Potential DDIs are frequent with OBV/PTV/r ± DSV ± RBV, although a change in baseline medication is made in only one-quarter of patients. More than half of potential DDIs were only followed, and only 5% of patients developed AEs in which the implication of DDIs could not be excluded.
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