Introduction

1. Alternative antiretroviral monitoring strategies for HIV-infected patients in resource-limited settings: Opportunities to save more lives? R Scott Braithwaite, MD, MS, FACPChief, Section of Value and CERNYU School of Medicine

2. Introduction • Should resource constraints impact HIV monitoring strategies for patients on antiretroviral therapy (ARV)? • Some say resource-rich strategies should not change because any resource-limitation could harm care. • Failing to check viral load may allow premature resistance accumulation that decreases ARV effectiveness • Others say lives may be saved by evaluating whether the resources used by intensive monitoring could save more lives if allocated elsewhere. • Money saved by avoiding one single viral load test can buy 3 months of life-saving ARV

3. Objective • To estimate the value of alternative laboratory monitoring strategies for patients with known HIV infection in Western Kenya. • We compare their value with simultaneous resource-constrained decisions

4. Methods: Computer Simulation design • Stochastic, 2nd order Monte Carlo progression model of HIV progression that explicitly represents the two main determinates of treatment failure • Accumulation of genotypic resistance mutations • ARV nonadherence • A key advantage of this design is that is can compare tradeoffs in monitoring aggressiveness • Earlier ARV switching may cause short-term gains, such as greater treatment effectiveness; but long-term harms, such as earlier ARV failure due to resistance

5. Methods • ICER = Incremental cost effectiveness ratio • Higher ICERs (meaning a greater cost per unit of benefit) are less favorable, and correspond to lower value. • Lower ICERs (meaning a lower cost per unit of benefit) are more favorable, and correspond to higher value. • Considerable debate exists over what is an acceptable “threshold” for value, and the extent to which this threshold varies based on resource limitations. • To help interpretation we calculated ICERs corresponding to other common resource-constrained decisions (e.g. whether to delay ARV initiation)

6. Schematic of computer simulation

7. Methods • We calibrated the simulation by testing goodness of fit of simulation results with clinical data from AMPATH • Kaplan Meier curves of mortality and time to treatment switching of first two regimens • Trajectory of median CD4 count. • Because these source data were of varying bias in a manner difficult to quantify, we did not employ a particular algorithm for testing goodness of fit. • We placed the greatest emphasis on the survival curves fit • Survival based on data employing random-sampling of patients lost-to-follow, up with active surveillance and imputation of their overall mortality contribution

8. Methods • We evaluated a matrix of different strategies: • Type of monitoring (clinical versus immunological versus virological versus combinations and conditional strategies), • Viral load threshold for switching (500,1000, 5000, and 10,000) • Frequency of monitoring (3 months, 6 months, and 12 months). • We considered combinations and conditional logic • Only check more expensive test if less expensive test met criteria • We estimated outcomes of life-years, quality-adjusted life-years (QALY), and costs ($2008 US dollars). • QALYs are a preference-weighted metric that incorporate both quantity and quality of life, and reflect the idea that a year of poor-quality life is valued less than a year of high-quality life.

9. Methods • Because space of limitations, we emphasize only those results on the most “efficient frontier.” • Not on efficient frontier • Consume more resources while delivering less benefit than alternatives, and do not maximize benefit, regardless of budget. • On efficient frontier • Deliver the greatest benefit for resources consumed, and therefore may be preferred • We first identified efficient frontiers for scenarios with one, two, and three available ARV regimens • We then identified the efficient frontier for a scenario that does not specify a fixed number of ARV regimens

10. Cost-Effectiveness of monitoring alternatives: 2 ARV regimens

12. Cost-Effectiveness of monitoring alternatives: 3 ARV regimens

14. No fixed # ARV regimens

16. Discussion • Relying on CD4 counts alone is never the preferred strategy, regardless of level of resources available. • Likely attributable to the poor sensitivity and specificity of CD4 for detecting viral rebound • However, employing CD4 counts together with conditional viral load is the preferred strategy under a wide range of willingness to pay scenarios

17. Discussion • Routine viral load testing is only preferred at willing to pay levels far above those for earlier ARV initiation. • A program routinely testing viral loads but starting ARV at a CD4 of 200 rather than 350 will save lives if it reallocates resources away from viral load testing towards earlier ARV initiation.

18. Discussion • When viral load is employed, switching threshold is more likely to be 10,000 copies/ml than 500 copies/ml. • Lower thresholds are only likely to be preferred as numbers of possible ARV regimens increase and/or become less expensive • Most of the additional expense from viral load testing comes from moving people to second-line regimens that are more expensive

19. Discussion • If programs are considering alternative monitoring strategies at the same time as how early to start ARV • Our results suggest more lives saved by offering fewer regimens with less intense monitoring strategies and re-allocating saved resources on earlier ARV.

20. Limitations • Not a transmission model, therefore does not consider how conservative monitoring may spread of ARV resistance • However, increase in resistance accumulation appears to be modest (<1 mutation over 5-year period) • Earlier treatment would lower viral load, making transmission less likely • Our simulation does not incorporate the patient time costs from laboratory tests, which may be substantial • However, considering them would only make lab testing less attractive and would generally reinforce our conclusions.

21. Conclusions • CD4 testing alone never maximized health benefits regardless of resource-limitations. • Programs routinely performing virological testing but deferring ARV initiation may increase health benefits by reallocating monitoring resources towards earlier ARV initiation.