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 CER

NYU School of Medicine

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 

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 

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

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) 

 

Viral Replication

HIV Mutations

cARTResistance

cARTNondherence

cARTeffectiveness

Viral Load CD4 Count

DEATH FROM HIV/AIDS DEATH FROM OTHER CAUSES

Patient Characteristics

NONADHERENCE INTERVENTIONS

Nonadherencerisk factors

Schematic of computer simulation

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

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. 

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

Cost-Effectiveness of monitoring alternatives: 2 ARV regimens

10.6

10.7

10.8

10.9

11

11.1

11.2

11.3

11.4

11.5

$11 $12 $13 $14 $15 $16 $17

Qua

lity

Adj

uste

d Li

fe Y

ears

Total Lifetime Cost(2008 currency)

Thousand dollars

$320/QALY

$4,250/QALY$6,411/QALY

$9,119/QALY

$11,165/QALY

$25,469/QALY

Triggers

Viral load

Viral load only after CD4

Clinical

Frequency

3612Smaller = statistically close to frontier

Shaded = switch at 500 (not 10000)

Table 2. Value of alternative laboratory monitoring strategies for known HIV-infected patients in Western Kenya, compared to value of earlier treatment initiation (at CD4 of 350 cells/ul compared to CD4 of 200 cells/ul), assuming 2 antiretroviral (ARV) regimens are available. Results are only shown for strategies that maximized health benefits for some budget scenarios or willingness-to-pay for health benefits. Strategies employing CD4 counts alone, or clinical criteria alone, did not maximize health benefits for any budget scenarios. 

Monitoring Strategy

Frequency (months)

Viral load threshold for switching ARV (copies/ml)

5-year outcomes Cost ($2008)

Quality-adjusted life-years (QALY)

Incremental cost-effectiveness Ratio ($/QALY)

Value compared to earlier treatment initiation*

Mean # ARV rounds used

Mean new mut-ations

Median CD4

Median HIV (log units)

Viral load only if CD4 meets WHO criteria†

12 10,000 1.23 1.08 269 2.70 $11,370 10.761 $1100/QALY Better

Viral load only if CD4 meets WHO criteria†

6 10,000 1.24 1.09 270 2.70 $11,547 10.830 $2600/QALY Similar

Viral load only if CD4 meets WHO criteria†

3 10,000 1.25 1.09 270 2.70 $11,741 10.883 $3700/QALY Worse

Viral Load

12 10,000 1.33 1.02 277 2.69 $12,464 11.049 $4400/QALY Worse

Viral Load

3 or 6 ‡ 10,000 1.34 1.01 278 2.69 $12,593 11.062 $9900/QALY Worse

Viral Load

6 or 12 ‡ 500 1.68 0.80 295 2.40 $15,859 11.363 $10900/QALY Worse

* “Better” value is indicated by a numerically lower ICER, and suggests that health benefits would be increased if resources were allocated away from earlier treatment initiation towards this monitoring strategy. “Worse” value is indicated by a numerically higher ICER, and suggests that health benefits would be increased if resources were allocated towards earlier ARV initiation away from this monitoring strategy. † WHO (World Health Organization criteria) for changing ARV regimen based on CD4 count

Cost-Effectiveness of monitoring alternatives: 3 ARV regimens

$1,884/QALY

$4,750/QALY

$6,532/QALY

$9,337/QALY

$22,084/QALY$73,715/QALY

10.7

10.9

11.1

11.3

11.5

11.7

11.9

12.1

$16 $18 $20 $22 $24 $26 $28

Qua

lity

Adj

uste

d Li

fe Y

ears

Total Lifetime Cost(2008 currency)

Thousand dollars

$7,000/QALY

Triggers

Viral load

Viral load only after CD4

Clinical

Frequency

3612Smaller = statistically close to frontier

Shaded = switch at 500 (not 10000)

Table 3. Value of alternative laboratory monitoring strategies for known HIV-infected patients in Western Kenya, compared to value of earlier treatment initiation (at CD4 of 350 cells/ul compared to CD4 of 200 cells/ul), assuming 3 antiretroviral (ARV) regimens are available. Results are only shown for strategies that maximized health benefits for some budget scenarios or willingness-to-pay for health benefits. Strategies employing CD4 counts alone, or clinical criteria alone, did not maximize health benefits for any budget scenarios.

Monitoring Strategy

Frequency (months)

Viral load threshold for switching ARV (copies/ml)

5-year outcomes Cost ($2008)

Quality-adjusted life-years (QALY)

Incremental cost-effectiveness Ratio ($/QALY)

Value compared to earlier treatment initiation*

Mean # ARV rounds used

Mean new mut-ations

Median CD4

Median HIV (log units)

Viral load only if CD4 meets WHO criteria†

12 10,000 1.30 1.10 269 2.70 $16,094 11.010 $1500 Better

Viral load only if CD4 meets WHO criteria†

3 or 6‡ 10,000 1.32 1.11 271 2.70 $16,577 11.119 $4400 Worse

Viral Load

3, 6, or 12‡ 10,000 1.47 1.01 280 2.69 $18,375 11.396 $6500 Worse

Viral Load

12 500 2.07 0.81 288 2.38 $23,840 11.683 $19000 Worse

Viral Load

3 or 6‡ 500 2.16 0.76 290 2.35 $25,408 11.716 $48000 Worse

* “Better” value is indicated by a numerically lower ICER, and suggests that health benefits would be increased if resources were allocated away from earlier treatment initiation towards this monitoring strategy. “Worse” value is indicated by a numerically higher ICER, and suggests that health benefits would be increased if resources were allocated towards earlier ARV initiation away from this monitoring strategy. † WHO (World Health Organization criteria) for changing ARV regimen based on CD4 count

No fixed # ARV regimens

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

$0 $5 $10 $15 $20 $25 $30

Qua

lity

Adj

uste

d Li

fe Y

ears

Total Lifetime Cost(2008 currency)

Thousand dollars

$619/QALY(1)

$5,580QALY (2)

$9,119/QALY (2)$6,412/QALY (2) 

$11,165/QALY (2) $13,431/QALY (3)$22,084/QALY(3) $73,715/QALY(3)

Triggers

Viral load

Viral load only after CD4

Clinical

Frequency

3612Smaller = statistically close to frontier

Shaded = switch at 500 (not 10000)

Table 4. Value of alternative laboratory monitoring strategies for known HIV-infected patients in Western Kenya, compared to value of earlier treatment initiation (at CD4 of 350 cells/ul compared to CD4 of 200 cells/ul), without any fixed assumption about numbers of available antiretroviral (ARV) regimens. Results are only shown for strategies that maximized health benefits for some budget scenarios or willingness-to-pay for health benefits. Strategies employing CD4 counts alone did not maximize health benefits for any budget scenarios, nor did conditional strategies employing viral load only if CD4 results met pre -specified criteria. # ARV regimens

Monitoring Strategy

Frequency (months)

Viral load threshold

5-year outcomes Cost ($2008)

QALY ICER (cost/ QALY)

Value compared to earlier treatment initiation* Mean #

ARV rounds used

Mean new mut-ations

Median CD4

Median HIV (log units)

0

Nothing NA NA 0 0 0 4.50 $809 1.973 NA NA

1

Clinical 3 NA 1.00 1.04 265 2.78 $5,670 9.832 $600 Better

2

Viral Load 12 10,000 1.33 1.02 277 2.69 $12,464 11.049 $5600 Worse

2

Viral Load 3 or 6† 10,000 1.34 1.01 278 2.69 $12,593 11.062 $9900 Worse

2

Viral Load 6 or 12† 500 1.68 0.80 295 2.40 $15,859 11.363 $10900 Worse

3

Viral Load 12 500 2.07 0.81 288 2.38 $23,840 11.683 $24900 Worse

3

Viral Load 3 or 6† 500 2.16 0.76 290 2.35 $25,408 11.716 $48000 Worse

* “Better” value is indicated by a numerically lower ICER, and suggests that health benefits would be increased if resources were allocated away from earlier treatment initiation towards this monitoring strategy. “Worse” value is indicated by a numerically higher ICER, and suggests that health benefits would be increased if resources were allocated towards earlier ARV initiation away from this monitoring strategy.

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

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. 

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

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. 

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. 

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.