title: Rules-based HIV-1 genotypic resistance interpretation systems predict 8 week and 24 week virological antiretroviral treatment outcome and benefit from drug potency weighting.
abstract: OBJECTIVE
  To test retrospectively the ability of four freely available rules-based expert systems to predict short- and medium-term virological outcome following an antiretroviral treatment switch in pre-treated HIV-1 patients.
  
  
  METHODS
  The HIV-1 genotype interpretation systems (GISs) HIVdb, ANRS, Rega and AntiRetroScan were tested for their accuracy in predicting response to highly active antiretroviral therapy using 8 week (n = 765) and 24 week (n = 634) follow-up standardized treatment change episodes extracted from the Italian Antiretroviral Resistance Cohort Analysis (ARCA) database. A genotypic sensitivity score (GSS) was derived for each genotype-treatment pair for the different GISs and tested as a predictor of virological treatment outcome by univariable and multivariable logistic regression as well as by receiver operating characteristic curve analysis. The two systems implementing drug potency weights (AntiRetroScan and Rega) were evaluated with and without this correction factor.
  
  
  RESULTS
  All four GSSs were strong predictors of virological treatment outcome at both 8 and 24 weeks after adjusting for baseline viro-immunological parameters and previous drug exposure (odds ratios ranging from 2.04 to 2.43 per 1 unit GSS increase; P < 0.001 for all the systems). The accuracy of AntiRetroScan and Rega was significantly increased by drug potency weighting with respect to the unweighted versions (P <or= 0.001). HIVdb and ANRS also increased their performance with the same drug potency weighting adopted by AntiRetroScan and Rega, respectively (P < 0.001 for both analyses).
  
  
  CONCLUSIONS
  Currently available GISs are valuable tools for assisting antiretroviral treatment choices. Drug potency weighting can increase the accuracy of all systems.
authors.affiliation: Department of Molecular Biology, University of Siena, Siena, Italy.
authors.forename: Maurizio
authors.initials: M
authors.lastname: Zazzi
authors.forename: Mattia
authors.initials: M
authors.lastname: Prosperi
authors.forename: Ilaria
authors.initials: I
authors.lastname: Vicenti
authors.forename: Simona
authors.initials: S
authors.lastname: Di Giambenedetto
authors.forename: Annapaola
authors.initials: A
authors.lastname: Callegaro
authors.forename: Bianca
authors.initials: B
authors.lastname: Bruzzone
authors.forename: Fausto
authors.initials: F
authors.lastname: Baldanti
authors.forename: Angela
authors.initials: A
authors.lastname: Gonnelli
authors.forename: Enzo
authors.initials: E
authors.lastname: Boeri
authors.forename: Elisabetta
authors.initials: E
authors.lastname: Paolini
authors.forename: Stefano
authors.initials: S
authors.lastname: Rusconi
authors.forename: Andrea
authors.initials: A
authors.lastname: Giacometti
authors.forename: Franco
authors.initials: F
authors.lastname: Maggiolo
authors.forename: Stefano
authors.initials: S
authors.lastname: Menzo
authors.forename: Andrea
authors.initials: A
authors.lastname: De Luca
mesh_terms: D000328:Adult; D000818:Animals; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D005260:Female; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology*; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics; D006801:Humans; D007558:Italy; D016015:Logistic Models; D008297:Male; D008826:Microbial Sensitivity Tests / Q000379:methods*; D008875:Middle Aged; D011379:Prognosis; D012367:RNA, Viral / Q000235:genetics*; D012372:ROC Curve; D012189:Retrospective Studies; D016896:Treatment Outcome
chemical_list: D019380:Anti-HIV Agents; D012367:RNA, Viral