Background Chronic hepatitis C (CHC) is a major public health problem that currently afflicts over 250,000 Veterans. The Food and Drug Administration (FDA) recently approved several direct acting antiviral agents (DAAs) for the treatment of CHC. Unlike prior interferon-based treatment regimens for CHC, DAAs are highly effective, have a favorable safety profile, and are well tolerated, eliciting significant consumer demand. However, DAAs are also extremely costly, making it difficult for healthcare systems to meet this growing demand from patients. Healthcare systems also lack a sufficient number of trained providers to treat all patients with CHC within a short time, further limiting access. But immediate treatment of all infected patients may be not only prohibitively expensive ? it is also unnecessary. Patients with CHC fall into two broad categories: (1) those with advanced liver disease (cirrhosis, or ?scarring? of the liver) or other extrahepatic manifestations of CHC (e.g., kidney disease) (accounting for ~ 25% of patients); (2) those without advanced liver disease or extrahepatic manifestations (accounting for ~ 75% -- the population of interest for this proposal). While data and guidelines are clear about the short-term benefit of treatment in the former group (i.e., those with cirrhosis), they are less clear about the benefit in the latter group (those without cirrhosis). In fact, most of these non-cirrhotic, asymptomatic patients progress slowly over years to decades (low-risk patients) and thus may not require immediate treatment. Others progress more rapidly and could benefit from immediate treatment. However, clinicians are often uncertain about how to approach such patients. Current treatment approaches for non-cirrhotic CHC vary substantially across healthcare systems, owing largely to discrepancies in guidance on when to treat such patients. As a result, treatment is often driven by a combination of patient preferences, clinician judgment, and drug availability. Veterans who are at high-risk for disease progression but do not actively seek care may therefore fail to receive potentially life-saving therapy. In addition, the guidelines continue to change rapidly based on the availability of new drugs. A systematic, rigorous approach to treatment, one informed by state-of-art prediction modeling to risk-stratify non-cirrhotic Veterans, could help guide risk-based treatment and mitigate this healthcare delivery shortcoming. Objectives The purpose of this study is to lay the groundwork for risk-based treatment of CHC among non- cirrhotic Veterans in the Veterans Health Administration (VHA) by: (1) developing accurate, clinically relevant, and implementable risk prediction models; (2) engaging Veterans to develop consensus on how to implement risk-based treatment; and (3) evaluating the clinical and economic effects of risk-based treatment. Methods In our preliminary work, we demonstrated the feasibility of using a machine-learning (ML) risk prediction model to identify patients at high risk and low risk for disease progression in a clinical trial cohort. We propose a 4- year study where we will use VA electronic data from 2004-2014 to adapt, validate and refine this model among Veterans. We will then engage Veterans, eliciting their preferences and values regarding risk-based treatment of CHC by applying consensus techniques (e.g., deliberative democracy). Finally, to estimate the incremental benefit of risk-based treatment over current treatment, we will use simulation modeling.

Public Health Relevance

Despite highly effective treatment for chronic hepatitis C, we cannot treat all infected patients immediately. In this study, we will develop a risk-based, systematic approach that will maximize treatment benefit while limiting clinical and economic harms. This research will produce a strategy to systemically identify high-risk patients and lay the groundwork for future implementation by engaging the Veteran voice. In addition, we will examine the impact of targeted CHC treatment on clinical outcomes and healthcare utilization, data that will be used to inform stakeholders about future staffing needs, ultimately improving access to care. The approach used in this study can also serve as a template for future efforts to implement risk- based management in other clinical contexts.

National Institute of Health (NIH)
Veterans Affairs (VA)
Non-HHS Research Projects (I01)
Project #
Application #
Study Section
HSR-3 Methods and Modeling for Research, Informatics, and Surveillance (HSR3)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Veterans Health Administration
Ann Arbor
United States
Zip Code