title: An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes.
abstract: OBJECTIVE
  The optimal individualized selection of antiretroviral drugs in resource-limited settings is challenging because of the limited availability of drugs and genotyping. Here we describe the development of the latest computational models to predict the response to combination antiretroviral therapy without a genotype, for potential use in such settings.
  
  
  METHODS
  Random forest models were trained to predict the probability of a virological response to therapy (<50 copies HIV RNA/mL) following virological failure using the following data from 22,567 treatment-change episodes including 1090 from southern Africa: baseline viral load and CD4 cell count, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. The models were assessed during cross-validation and with an independent global test set of 1000 cases including 100 from southern Africa. The models' accuracy [area under the receiver-operating characteristic curve (AUC)] was evaluated and compared with genotyping using rules-based interpretation systems for those cases with genotypes available.
  
  
  RESULTS
  The models achieved AUCs of 0.79-0.84 (mean 0.82) during cross-validation, 0.80 with the global test set and 0.78 with the southern African subset. The AUCs were significantly lower (0.56-0.57) for genotyping.
  
  
  CONCLUSIONS
  The models predicted virological response to HIV therapy without a genotype as accurately as previous models that included a genotype. They were accurate for cases from southern Africa and significantly more accurate than genotyping. These models will be accessible via the online treatment support tool HIV-TRePS and have the potential to help optimize antiretroviral therapy in resource-limited settings where genotyping is not generally available.
authors.affiliation: HIV Resistance Response Database Initiative (RDI), London, UK.
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Revell
authors.forename: Dechao
authors.initials: D
authors.lastname: Wang
authors.forename: Robin
authors.initials: R
authors.lastname: Wood
authors.forename: Carl
authors.initials: C
authors.lastname: Morrow
authors.forename: Hugo
authors.initials: H
authors.lastname: Tempelman
authors.forename: Raph
authors.initials: R
authors.lastname: Hamers
authors.forename: Gerardo
authors.initials: G
authors.lastname: Alvarez-Uria
authors.forename: Adrian
authors.initials: A
authors.lastname: Streinu-Cercel
authors.forename: Luminita
authors.initials: L
authors.lastname: Ene
authors.forename: Annemarie
authors.initials: A
authors.lastname: Wensing
authors.forename: Peter
authors.initials: P
authors.lastname: Reiss
authors.forename: Ard I
authors.initials: AI
authors.lastname: van Sighem
authors.forename: Mark
authors.initials: M
authors.lastname: Nelson
authors.forename: Sean
authors.initials: S
authors.lastname: Emery
authors.forename: Julio S G
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: D000328:Adult; D044966:Anti-Retroviral Agents / Q000627:therapeutic use*; D023241:Antiretroviral Therapy, Highly Active / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D005838:Genotype; D006678:HIV / Q000187:drug effects* / Q000235:genetics*; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology; D006801:Humans; D008297:Male; D011379:Prognosis; D016879:Salvage Therapy / Q000379:methods*; D016896:Treatment Outcome
chemical_list: D044966:Anti-Retroviral Agents
keywords: antiretroviral therapy; genotyping; resource-limited settings