In the proposed study, we will examine long-term clinical outcomes in treated and untreated patients with chronic hepatitis C virus (HCV) infection. Currently, 5.4% of US military veterans are infected with HCV, a major cause of liver disease. Antiviral treatment can eliminate HCV, but highly prevalent behavioral, psychiatric, and cardiopulmonary conditions can adversely impact treatment candidacy and compliance, as well as therapeutic success among VA patients. Thus, only 18% of patients currently in care in the VA have ever received anti-HCV treatment, and fewer than 4% have cleared the virus. Even among relatively healthy HCV patients, improved clinical outcomes due to viral eradication have been difficult to quantitate in short-term follow-up studies. Improved treatment-associated outcomes among VA patients may be even less significant, as marked comorbidities place this population at greater risk for liver disease and for premature death. To date, there have been no long-term clinical outcome studies among large cohorts of HCV-treated patients. These are particularly important to perform now, as a new generation of hepatitis C therapies utilizing protease inhibitors and polymerase inhibitors will likely receive FDA approval in 2011, and many VA patients will seek to be treated with these medications, although precise benefits to them of viral clearance or lack thereof are not currently available. In the current proposal, we will examine the progression to cirrhosis, decompensation of cirrhosis, liver-related death, and all cause mortality among patients from two unique cohorts of HCV patients with the VA. The first is a multisite cohort of 2000 HCV-infected participants recruited between 1999 and 2000 from 10 VA hospitals. The second is a San Francisco VA cohort of 771 HCV patients enrolled over the past 18 years. Using these two cohorts, we will examine differences in long-term clinical outcomes among veterans who cleared HCV with antiviral therapy, failed to clear the virus, or were never treated. We will also explore emerging data that suggest that ethnic background correlates not only with response to treatment, which is well established, but also contributes to risk for cirrhosis development. The results of this study will provide critically needed evidence to better inform clinicians in making treatment decisions for HCV, a disease that is taking an increased toll on the veteran population.

Public Health Relevance

Chronic hepatitis C virus (HCV) infection is epidemic among US veterans, with an estimated 207,000, or 5.4%, of VA patients infected with the virus. This is due largely to the many veterans who were exposed through past drug use or contaminated blood products infected before the virus was identified in 1989. HCV has a long inactive period before it can develop into advanced liver disease, which can be life-threatening. Antiviral therapy is costly, lengthy, causes bad side effects, and often does not clear the virus. A combination of unhealthy conditions influences treatment eligibility. These include depression, obesity, alcoholism and active substance abuse. Only 18% of current VA patients with HCV have received antiviral therapy, due to their often being poor candidates for therapy. Also, only a small proportion of those who are treated clear the virus. By understanding precisely the benefits of treatment to veterans, we aim to use antiviral medications in the best way, and so reduce the long-term complications of liver disease and enhance the lives of veterans with HCV.

National Institute of Health (NIH)
Veterans Affairs (VA)
Non-HHS Research Projects (I01)
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Epidemiology (EPID)
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Veterans Affairs Medical Center San Francisco
San Francisco
United States
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Saxena, V; Manos, M M; Yee, H S et al. (2014) Telaprevir or boceprevir triple therapy in patients with chronic hepatitis C and varying severity of cirrhosis. Aliment Pharmacol Ther 39:1213-24
Bacchetti, Peter; Boylan, Ross; Astemborski, Jacquie et al. (2011) Progression of biopsy-measured liver fibrosis in untreated patients with hepatitis C infection: non-Markov multistate model analysis. PLoS One 6:e20104