Effectiveness of direct-acting antiviral drugs against hepatitis C virus: Predictive factors of response to the treatment

Abstract

Background/Aims: Despite the high efficacy and safety of direct-acting antivirals against hepatitis C virus shown in clinical trials, treatment failures continue to occur. Our aim was to establish the effectiveness of these drugs in routine clinical practice, as well as to determine factors that could influence the response to the treatment.

Materials/methods: Single-center, observational, retrospective study. Clinical, virological and pharmacotherapeutic variables were registered at baseline. Adverse drug reactions that occurred were recorded until week 24 of follow-up. Achievement of sustained virologic response was also recorded. Univariate and multivariate analysis were done to determine factors of response.

Results: A total of 333 treatment regimens corresponding to 330 different patients were evaluated. Sustained virologic response rate was 94.6% [95%CI: 91.6–96.6%]. 67.9% of the patients experienced adverse drugs reactions (92.2% were grade 1). The univariate analysis identified a higher baseline of platelets, albumin and total cholesterol as predictive factors of sustained virologic response (p < 0.05). Presence of diabetes and complications related to liver disease (splenomegaly, portal hypertension, portal hypertensive gastropathy), body mass index ≥30, greater liver fibrosis, receiving simeprevir and higher baseline levels of glucose, aspartate-aminotransferase, alanine-aminotransferase and alkaline-phosphatase, have been identified as predictive factors of non-response (p < 0.05). The multivariate analysis detected the following independent factors of non-response: body mass index ≥30 and presence of complications related to liver disease.

Conclusion: The effectiveness and safety of direct-acting antivirals against hepatitis C virus have been maintained in routine clinical practice. Further research on predictive factors of response is required in order to develop more reliable and reproducible predictive models.