title: Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.
abstract: OBJECTIVE
  Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs.
  
  
  METHODS
  Random forest models were trained to predict the probability of response to ART (≤400 copies HIV RNA/mL) using the following data from 14 891 treatment change episodes (TCEs) after virological failure, from well-resourced countries: viral load and CD4 count prior to treatment change, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. Models were assessed by cross-validation during development, with an independent set of 800 cases from well-resourced countries, plus 231 cases from Southern Africa, 206 from India and 375 from Romania. The area under the receiver operating characteristic curve (AUC) was the main outcome measure.
  
  
  RESULTS
  The models achieved an AUC of 0.74-0.81 during cross-validation and 0.76-0.77 with the 800 test TCEs. They achieved AUCs of 0.58-0.65 (Southern Africa), 0.63 (India) and 0.70 (Romania). Models were more accurate for data from the well-resourced countries than for cases from Southern Africa and India (P < 0.001), but not Romania. The models identified alternative, available drug regimens predicted to result in virological response for 94% of virological failures in Southern Africa, 99% of those in India and 93% of those in Romania.
  
  
  CONCLUSIONS
  We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.
authors.affiliation: The HIV Resistance Response Database Initiative (RDI), London, UK.
authors.forename: A D
authors.initials: AD
authors.lastname: Revell
authors.forename: D
authors.initials: D
authors.lastname: Wang
authors.forename: R
authors.initials: R
authors.lastname: Wood
authors.forename: C
authors.initials: C
authors.lastname: Morrow
authors.forename: H
authors.initials: H
authors.lastname: Tempelman
authors.forename: R L
authors.initials: RL
authors.lastname: Hamers
authors.forename: G
authors.initials: G
authors.lastname: Alvarez-Uria
authors.forename: A
authors.initials: A
authors.lastname: Streinu-Cercel
authors.forename: L
authors.initials: L
authors.lastname: Ene
authors.forename: A M J
authors.initials: AM
authors.lastname: Wensing
authors.forename: F
authors.initials: F
authors.lastname: DeWolf
authors.forename: M
authors.initials: M
authors.lastname: Nelson
authors.forename: J S
authors.initials: JS
authors.lastname: Montaner
authors.forename: H C
authors.initials: HC
authors.lastname: Lane
authors.forename: B A
authors.initials: BA
authors.lastname: Larder
mesh_terms: D000328:Adult; D017954:Africa South of the Sahara / Q000453:epidemiology; D019380:Anti-HIV Agents / Q000600:supply & distribution / Q000627:therapeutic use; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D005500:Follow-Up Studies; D006678:HIV / Q000235:genetics*; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology; D017320:HIV Protease Inhibitors / Q000600:supply & distribution / Q000627:therapeutic use; D006295:Health Resources; D006801:Humans; D007194:India / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012372:ROC Curve; D018894:Reverse Transcriptase Inhibitors / Q000600:supply & distribution / Q000627:therapeutic use; D012383:Romania / Q000453:epidemiology; D017211:Treatment Failure; D019562:Viral Load
chemical_list: D019380:Anti-HIV Agents; D017320:HIV Protease Inhibitors; D018894:Reverse Transcriptase Inhibitors
keywords: HIV drug resistance; antiretroviral therapy; computer models; predictions; resource-limited settings.; treatment outcomes