Abstract
BackgroundGenotypic antiretroviral resistance testing (GRT) in HIV infection with drug resistant virus is recommended to optimize antiretroviral therapy, in particular in patients with virological failure. We estimated the clinical effect, cost and cost-effectiveness of using GRT as compared to expert opinion in patients with antiretroviral treatment failure.MethodsWe developed a mathematical model of HIV disease to describe disease progression in HIV-infected patients with treatment failure and compared the incremental impact of GRT versus expert opinion to guide antiretroviral therapy. The analysis was conducted from the health care (discount rate 4%) and societal (discount rate 2%) perspective. Outcome measures included life-expectancy, quality-adjusted life-expectancy, health care costs, productivity costs and cost-effectiveness in US Dollars per quality-adjusted life-year (QALY) gained. Clinical and economic data were extracted from the large Swiss HIV Cohort Study and clinical trials.ResultsPatients whose treatment was optimized with GRT versus expert opinion had an increase in discounted life-expectancy and quality-adjusted life-expectancy of three and two weeks, respectively. Health care costs with and without GRT were $US 421,000 and $US 419,000, leading to an incremental cost-effectiveness ratio of $US 35,000 per QALY gained. In the analysis from the societal perspective, GRT versus expert opinion led to an increase in discounted life-expectancy and quality-adjusted life-expectancy of three and four weeks, respectively. Health care costs with and without GRT were $US 551,000 and $US 549,000, respectively. When productivity changes were included in the analysis, GRT was cost-saving.ConclusionsGRT for treatment optimization in HIV-infected patients with treatment failure is a cost-effective use of scarce health care resources and beneficial to the society at large.
Highlights
The advent of potent antiretroviral therapies (ART) a decade ago has led to a substantial decline of morbidity and mortality in HIV infected patients [1,2,3]
We developed a mathematical model of HIV disease to assess the incremental impact of antiretroviral therapy guided by genotypic antiretroviral resistance testing versus expert opinion alone in patients presenting with treatment failure defined as i) a viral log reduction of,1 log HIV RNA copies/ml during the first six months after initiation of antiretroviral therapy (ART), ii) a viral load increase of .1 log HIV RNA copies/ml within two months of ART, or iii) two consecutive viral load assessments .200 copies/ml after reaching undetectable plasma HIV RNA levels (,50 copies/ml) [21]
Patients whose treatment was optimized with information available from Genotypic antiretroviral resistance testing (GRT) versus expert opinion had an increase in undiscounted life-expectancy and quality-adjusted life-expectancy of 2 and 6 weeks, respectively
Summary
The advent of potent antiretroviral therapies (ART) a decade ago has led to a substantial decline of morbidity and mortality in HIV infected patients [1,2,3]. Genotypic antiretroviral resistance testing (GRT) helps to distinguish between antiretroviral drugs to which HIV has become resistant and compounds that effectively suppress viral replication [7,8,9] It has been documented in randomized controlled trials that GRT based treatment optimization leads to a higher viral load reduction than standard care alone in treatment-experienced patients [8,10,11]. Genotypic antiretroviral resistance testing (GRT) in HIV infection with drug resistant virus is recommended to optimize antiretroviral therapy, in particular in patients with virological failure. We developed a mathematical model of HIV disease to describe disease progression in HIV-infected patients with treatment failure and compared the incremental impact of GRT versus expert opinion to guide antiretroviral therapy. GRT for treatment optimization in HIV-infected patients with treatment failure is a cost-effective use of scarce health care resources and beneficial to the society at large
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