The American Journal of Managed Care January 2011
Economic Model for Emergency Use Authorization of Intravenous Peramivir
Objectives: To develop 3 computer simulation models to determine the potential economic effect of using intravenous (IV) antiviral agents to treat hospitalized patients with influenza-like illness, as well as different testing and treatment strategies.
Study Design: Stochastic decision analytic computer simulation model.
Methods: During the 2009 influenza A(H1N1) pandemic, the Food and Drug Administration granted emergency use authorization of IV neuraminidase inhibitors for hospitalized patients with influenza, creating a need for rapid decision analyses to help guide use. We compared the economic value from the societal and third-party payer perspectives of the following 4 strategies for a patient hospitalized with influenza-like illness and unable to take oral antiviral agents: Strategy 1: Administration of IV antiviral agents without polymerase chain reaction influenza testing. Strategy 2: Initiation of IV antiviral treatment, followed by polymerase chain reaction testing to determine whether the treatment should be continued. Strategy 3: Performance of polymerase chain reaction testing, followed by initiation of IV antiviral treatment if the test results are positive. Strategy 4: Administration of no IV antiviral agents. Sensitivity analyses varied the probability of having influenza (baseline, 10%; range, 10%-30%), IV antiviral efficacy (baseline, oral oseltamivir phosphate; range, 25%-75%), IV antiviral daily cost (range, $20-$1000), IV antiviral reduction of illness duration (baseline, 1 day; range, 1-2 days), and ventilated vs nonventilated status of the patient.
Results: When the cost of IV antiviral agents was no more than $500 per day, the incremental cost-effectiveness ratio for most of the IV antiviral treatment strategies was less than $10,000 per quality-adjusted life-year compared with no treatment. When the cost was no more than $100 per day, all 3 IV antiviral strategies were even more costeffective. The order of cost-effectiveness from most to least was strategies 3, 1, and 2. The findings were robust to changing risk of influenza, influenza mortality, IV antiviral efficacy, IV antiviral daily cost, IV antiviral reduction of illness duration, and ventilated vs nonventilated status of the patient for both societal and third-party payer perspectives.
Conclusion: Our study supports the use of IV antiviral treatment for hospitalized patients with influenza-like illness.
(Am J Manag Care. 2011;17(1):e1-e9)
During the 2009 influenza A(H1N1) pandemic, the Food and Drug Administration granted emergency use authorization of peramivir because of a dearth of alternative drugs for patients unable to take oral or inhaled antiviral agents and because of limited safety and efficacy data for peramivir. Our economic model suggests the following:
- Intravenous antiviral agents are a cost-effective intervention for hospitalized patients with severe influenza-like illness.
- The use of PCR influenza testing to guide whether to initiate intravenous antiviral treatment is the most cost-effective strategy.
- The use of intravenous antiviral agents in this setting may save money and offer health benefits.
- Our findings should help guide policy makers and clinicians in the use of such novel treatments during other influenza seasons.
Antiviral medications are the only medications available to reduce the morbidity and mortality of individuals infected with influenza. Neuraminidase inhibitors are an important and widely used class of antiviral agents, the most commonly used being oral oseltamivir phosphate and inhaled zanamivir (both approved by the FDA in 1999).4 In influenza A and influenza B, an enzyme cleaves links between the infected host cell and the influenza virus envelope. This, in turn, allows the viruses that replicated in the host cell to be released to the rest of the body.5 By inhibiting viral replication and thereby limiting the number of viruses in the body, neuraminidase inhibitors could reduce the duration of illness and risk of mortality.6
Because IV peramivir was a novel drug, there were no available clinical trials among higher-risk groups such as pregnant women, pediatric patients, and older adults. It was also unclear how viral resistance to other neuraminidase inhibitors may translate to resistance to peramivir.6 Intravenous antiviral agents such as peramivir have several potential advantages. First, they offer an alternative route of administration, which is especially important for patients who cannot take medication by mouth (such as ventilated patients). Second, when heavy demand may deplete inventories of other antiviral agents such as oseltamivir and zanamivir, IV antiviral agents can serve as another available option. Third, there remains the possibility that strains resistant to other antiviral agents may not be completely resistant to newer antiviral agents such as peramivir, although evidence suggests hat oseltamivir-resistant strains may also be resistant to peramivir.7
Intravenous antiviral agents have only recently emerged as potential treatment options, and questions remain about their economic value. Should they be reserved for intensive care unit patients or administered to all hospitalized patients with influenza who cannot take oral antiviral agents? What is a reasonable price for IV antiviral medications? How would the value of IV antiviral agents change with emerging resistance? Should patients be tested for influenza before the initiation of IV antiviral agents, or should IV antiviral treatment be initiated first, followed by confirmatory testing to determine whether treatment should be continued? Will the value be different for seasonal vs pandemic influenza?
We developed 3 computer simulation models to estimate the potential economic effect of using IV antiviral agents to treat hospitalized patients with ILI, as well as different testing and treatment strategies. Various simulation runs explored seasonal and pandemic influenza scenarios and evaluated the effects of varying patient age, probability of having influenza, ventilated vs nonventilated status of the patient, and the probability of different influenza outcomes such as mortality.
Structure of the Model
The Figure shows the general structure of our Monte Carlo decision analytic computer simulation model, constructed using commercially available software (TreeAge Pro 2009; TreeAge Software, Williamstown, Massachusetts). Our model represented the economic value from the societal and thirdparty payer perspectives of the following 4 alternative strategies for a patient hospitalized with ILI and unable to take oral antiviral agents, as per the EUA1-3:
Strategy 1: Administration of IV Antiviral Agents Without Polymerase Chain Reaction (PCR) Influenza Testing. In this strategy, the patient received a full 5-day course of IV antiviral agents regardless of whether the patient actually had influenza. No PCR influenza testing was performed.
Strategy 2: Initiation of IV Antiviral Treatment, Followed by PCR Testing to Determine Whether the Treatment Should Be Continued. This strategy involved initiation of IV antiviral treatment for hospitalized patients with ILI and then performance of PCR testing for influenza, which required a 24-hour turnaround time for results. A negative test result prompted discontinuation of IV antiviral treatment after a single dose.
Strategy 3: Performance of PCR Testing, Followed by Initiation of IV Antiviral Treatment If the Test Results Were Positive. For this strategy, IV antiviral agents would not be initiated until PCR testing was performed and results were available, delaying treatment for 24 hours. A positive test result prompted initiation of IV antiviral treatment.
Strategy 4: Administration of No IV Antiviral Agents. This strategy involved giving no medical interventions as the treatment.
Each simulated adult traveled through the decision tree pictured in the Figure and faced a probability draw (first-order trial) at each chance node. This draw was then compared with a value pulled from the probability parameter distribution to determine down which branch he or she traveled (second-order trials). The costs, utilities, and durations of each resulting outcome also drew from their respective probability distributions (second-order trials). Each patient had a probability of having influenza (baseline, 10%).8,9 Test results depended on whether the patient actually had influenza, and the sensitivity and specificity of the test. Intravenous antiviral treatment had a probability (IV antiviral efficacy) of reducing the duration of illness by 1 day (ie, if the dice roll is less than the efficacy number, then the illness duration is reduced; if it is higher, then there is no effect) and the risk of mortality (ie, mortality is reduced by 1 minus IV antiviral efficacy).
Scenarios from the third-party payer perspective considered only the direct costs of illness, while scenarios from the societal perspective included both direct and indirect costs of illness (ie, productivity losses from caregiver time determined by lost wages from time spent with the patient). Each simulation run sent 1000 simulated adults 1000 times through each model, for a total of 1,000,000 trials per scenario. During each run, each parameter drew from the relevant triangular or beta distributions. Table 1 lists the study sources of data inputs for the model.8,10-24
The following equation calculated the incremental costeffectiveness ratio (ICER) of each strategy vs the comparator strategy: Coststrategy x − Coststrategy y = Effectivenessstrategy x − Effectivenessstrategy y, where x represents the strategy and y represents the comparator strategy.
Each simulation run generated a mean ICER and a 95% confidence interval. A strategy was considered cost-effective if the ICER was less than $50,000 per quality-adjusted life-year (QALY).16 However, because there is some debate about the exact threshold for cost-effectiveness, we report the resulting ICER values so that individual readers may ake their own determination of what constitutes a cost-effective intervention.
The study used various inputs and their corresponding distributions for the probabilities, costs, durations, and utilities in the model. Hospital mortality from influenza drew from a beta distribution. All other variables drew from triangular distributions. A 3% discount rate converted all past and future costs to 2009 values.25
Because PCR testing is unavailable and is not part of standard care in many inpatient settings, our model did not include PCR test costs for the IV antiviral and no IV antiviral strategies.26 The hospitalization costs for these arms did not include PCR testing. The PCR test costs were added to the 2 strategies that included PCR testing.
Effectiveness was measured in QALYs. Influenza caused QALY decrements for the duration of illness.17,18 All QALY accruals were age adjusted based on the quality-of-life utility obtained by Gold et al.16 Patients who survived accrued ageadjusted and discounted (3% discount rate) QALYs based on life-expectancy estimates from the Human Mortality Database; patients who did not survive did not accrue these QALYs.16,27
Probabilistic (Monte Carlo) sensitivity analyses explored all parameters simultaneously using all distributions examined. Sensitivity analysis varied these following key variables: the probability of having influenza (baseline, 10%; range, 10%-30%),9 IV antiviral efficacy (baseline, oral oseltamivir; range, 25%-75%), IV antiviral daily cost (baseline, $20 [oral oseltamivir]; range, $20-$1000), IV antiviral reduction of illness duration (baseline, 1 day; range, 1-2 days), patient age (baseline, 20 years; range, 20-60 years), and influenza mortality (baseline, seasonal influenza mortality; range, up to twice the seasonal influenza mortality for a pandemic scenario). These sensitivity analyses involved fixing the variable of interest and then allowing the rest to pull from the distributions examined. We evaluated seasonal and pandemic influenza scenarios, as well as IV antiviral treatment in ventilated and nonventilated patients.
Table 2 and Table 3 give the results at baseline, while varying IV antiviral efficacy (25%-75%) (ie, the proportion by which IV antiviral agents will reduce mortality) and IV antiviral daily cost from the third-party payer perspective for the seasonal and pandemic scenarios. Each table gives the ICER (compared with the most economically favorable option) for each strategy. Calculated ICER 95% confidence intervals demonstrated no overlaps, suggesting that the differences were significant.