1. Alternative antiretroviral monitoring strategies for HIV-infected patients in resource-limited settings: Opportunities to save more lives? R Scott Braithwaite, MD, MS, FACP Chief, Section of Value and CER NYU 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, 2 nd 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.