title: Potential impact of a free online HIV treatment response prediction system for reducing virological failures and drug costs after antiretroviral therapy failure in a resource-limited setting.
abstract: OBJECTIVE
  Antiretroviral drug selection in resource-limited settings is often dictated by strict protocols as part of a public health strategy. The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings.
  
  
  METHODS
  The HIV-TRePS computational models were used to predict the probability of response to therapy for 206 cases of treatment change following failure in India. The models were used to identify alternative locally available 3-drug regimens, which were predicted to be effective. The costs of these regimens were compared to those actually used in the clinic.
  
  
  RESULTS
  The models predicted the responses to treatment of the cases with an accuracy of 0.64. The models identified alternative drug regimens that were predicted to result in improved virological response and lower costs than those used in the clinic in 85% of the cases. The average annual cost saving was $364 USD per year (41%).
  
  
  CONCLUSIONS
  Computational models that do not require a genotype can predict and potentially avoid treatment failure and may reduce therapy costs. The use of such a system to guide therapeutic decision-making could confer health economic benefits in resource-limited settings.
authors.affiliation: The HIV Resistance Response Database Initiative (RDI), 14 Union Square, London N1 7DH, UK.
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Revell
authors.forename: Gerardo
authors.initials: G
authors.lastname: Alvarez-Uria
authors.forename: Dechao
authors.initials: D
authors.lastname: Wang
authors.forename: Anton
authors.initials: A
authors.lastname: Pozniak
authors.forename: Julio S
authors.initials: JS
authors.lastname: Montaner
authors.forename: H Clifford
authors.initials: HC
authors.lastname: Lane
authors.forename: Brendan A
authors.initials: BA
authors.lastname: Larder
mesh_terms: D019380:Anti-HIV Agents / Q000191:economics* / Q000627:therapeutic use; D003198:Computer Simulation; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy / Q000191:economics*; D015497:HIV-1 / Q000187:drug effects / Q000472:pathogenicity; D017048:Health Care Costs; D006801:Humans; D015233:Models, Statistical; D012189:Retrospective Studies; D017211:Treatment Failure
chemical_list: D019380:Anti-HIV Agents