title: Outcomes 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.
abstract: BACKGROUND
  In lower-income countries, WHO recommends a population-based approach to antiretroviral treatment with standardised regimens and clinical decision making based on clinical status and, where available CD4 cell count, rather than viral load. Our aim was to study the potential consequences of such monitoring strategies, especially in terms of survival and resistance development.
  
  
  METHODS
  A validated computer simulation model of HIV infection and the effect of antiretroviral therapy was used to compare survival, use of second-line regimens, and development of resistance that result from different strategies-based on viral load, CD4 cell count, or clinical observation alone-for determining when to switch people starting antiretroviral treatment with the WHO-recommended first-line regimen of stavudine, lamivudine, and nevirapine to second-line antiretroviral treatment.
  
  
  RESULTS
  Over 5 years, the predicted proportion of potential life-years survived was 83% with viral load monitoring (switch when viral load >500 copies per mL), 82% with CD4 cell count monitoring (switch at 50% drop from peak), and 82% with clinical monitoring (switch when two new WHO stage 3 events or a WHO stage 4 event occur). Corresponding values over 20 years were 67%, 64%, and 64%. Findings were robust to variations in model specification in extensive univariable and multivariable sensitivity analyses. Although survival was slightly longer with viral load monitoring, this strategy was not the most cost effective.
  
  
  CONCLUSIONS
  For patients on the first-line regimen of stavudine, lamivudine, and nevirapine the benefits of viral load or CD4 cell count monitoring over clinical monitoring alone are modest. Development of cheap and robust versions of these assays is important, but widening access to antiretrovirals-with or without laboratory monitoring-is currently the highest priority.
authors.affiliation: HIV Epidemiology and Biostatistics Group, Department of Primary Care and Population Sciences, and Royal Free Centre for HIV Medicine, Royal Free and University College Medical School, University College London, London, UK. a.phillips@pcps.ucl.ac.uk
authors.forename: Andrew N
authors.initials: AN
authors.lastname: Phillips
authors.forename: Deenan
authors.initials: D
authors.lastname: Pillay
authors.forename: Alec H
authors.initials: AH
authors.lastname: Miners
authors.forename: Diane E
authors.initials: DE
authors.lastname: Bennett
authors.forename: Charles F
authors.initials: CF
authors.lastname: Gilks
authors.forename: Jens D
authors.initials: JD
authors.lastname: Lundgren
mesh_terms: D000328:Adult; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D018791:CD4 Lymphocyte Count; D003198:Computer Simulation; D003658:Decision Making, Computer-Assisted; D003906:Developing Countries; D005260:Female; D015658:HIV Infections / Q000097:blood / Q000188:drug therapy* / Q000401:mortality; D006801:Humans; D008297:Male; D018894:Reverse Transcriptase Inhibitors / Q000627:therapeutic use*; D016019:Survival Analysis; D019562:Viral Load
chemical_list: D019380:Anti-HIV Agents; D018894:Reverse Transcriptase Inhibitors