ArticlesOutcomes from monitoring of patients on antiretroviral therapy in resource-limited settings with viral load, CD4 cell count, or clinical observation alone: a computer simulation model
Introduction
For antiretroviral treatment to be introduced as widely and rapidly as possible in lower-income settings, initial treatment will inevitably be provided without monitoring of viral load and, in many cases, without monitoring of CD4 cell counts; furthermore, the role of resistance testing is likely to be in monitoring emergence of resistance patterns at a population level rather than for individualised care. If second-line regimens are to be available in these lower-income settings the decision of when to switch a patient from a first to a second-line regimen will have to be based on incidence of new HIV-related clinical disease and—if available—CD4 cell count changes, rather than on the basis of viral load (ie, observed virological failure), as is standard in high-income countries.1, 2, 3, 4, 5, 6, 7, 8, 9, 10 Since clinical monitoring is being used for most of the 2 million patients currently receiving antiretrovirals in lower-income countries11 and will continue to be used for many years as the scale-up of antiretroviral therapy continues, it is important to fully consider the potential long-term consequences of its use, especially in terms of survival and resistance development.
Here, we use a model of HIV progression and the effect of antiretroviral therapy that has been extensively tested against clinical data to predict outcomes from the use of various clinical and CD4 cell count-based monitoring strategies for determining when to switch to a second-line regimen, and compare these with strategies based on viral load monitoring.
Section snippets
Methods
The model of HIV infection and the effect of antiretroviral therapy (HIV Synthesis) that we used was originally developed for well-resourced settings and has been described in detail elsewhere.12 In brief, this stochastic computer simulation model generates data on the progression of HIV infection and the effect of antiretroviral therapy on simulated patients. At the time of infection, information generated includes calendar date, age, viral load, and CD4 cell count. Each individual's data are
Results
Table 1 shows the baseline characteristics of the patients generated from the base model. These data are representative of a common situation in many lower-income countries. Sensitivity analyses indicated that changes in the base model that produced variations in the baseline characteristics would not significantly affect the results of our overall comparison of monitoring strategies (webtable 2).
Kaplan-Meier estimates of the risk of virological failure, in terms of viral load being over 500
Discussion
Development of cheap and robust assays to measure viral load and CD4 cell count in settings where antiretroviral therapy is provided is an important priority for the HIV research community. However, in the meantime, provision of antiretrovirals to the millions of people in need is only possible if this is done without viral load monitoring and, in many cases, without CD4 cell count monitoring. In such situations, the decision on when to switch therapy to a second-line antiretroviral regimen has
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