title: Clinical evaluation of Rega 8: an updated genotypic interpretation system that significantly predicts HIV-therapy response.
abstract: BACKGROUND
  Clinically evaluating genotypic interpretation systems is essential to provide optimal guidance in designing potent individualized HIV-regimens. This study aimed at investigating the ability of the latest Rega algorithm to predict virological response on a short and longer period.
  
  
  METHODS
  9231 treatment changes episodes were extracted from an integrated patient database. The virological response after 8, 24 and 48 weeks was dichotomized to success and failure. Success was defined as a viral load below 50 copies/ml or alternatively, a 2 log decrease from the baseline viral load at 8 weeks. The predictive ability of Rega version 8 was analysed in comparison with that of previous evaluated version Rega 5 and two other algorithms (ANRS v2011.05 and Stanford HIVdb v6.0.11). A logistic model based on the genotypic susceptibility score was used to predict virological response, and additionally, confounding factors were added to the model. Performance of the models was compared using the area under the ROC curve (AUC) and a Wilcoxon signed-rank test.
  
  
  RESULTS
  Per unit increase of the GSS reported by Rega 8, the odds on having a successful therapy response on week 8 increased significantly by 81% (OR = 1.81, CI = [1.76-1.86]), on week 24 by 73% (OR = 1.73, CI = [1.69-1.78]) and on week 48 by 85% (OR = 1.85, CI = [1.80-1.91]). No significant differences in AUC were found between the performance of Rega 8 and Rega 5, ANRS v2011.05 and Stanford HIVdb v6.0.11, however Rega 8 had the highest sensitivity: 76.9%, 76.5% and 77.2% on 8, 24 and 48 weeks respectively. Inclusion of additional factors increased the performance significantly.
  
  
  CONCLUSIONS
  Rega 8 is a significant predictor for virological response with a better sensitivity than previously, and with rules for recently approved drugs. Additional variables should be taken into account to ensure an effective regimen.
authors.affiliation: Rega Institute for Medical Research, KU Leuven, Leuven, Belgium. jurgen.vercauteren@rega.kuleuven.be
authors.forename: Jurgen
authors.initials: J
authors.lastname: Vercauteren
authors.forename: Gertjan
authors.initials: G
authors.lastname: Beheydt
authors.forename: Mattia
authors.initials: M
authors.lastname: Prosperi
authors.forename: Pieter
authors.initials: P
authors.lastname: Libin
authors.forename: Stijn
authors.initials: S
authors.lastname: Imbrechts
authors.forename: Ricardo
authors.initials: R
authors.lastname: Camacho
authors.forename: Bonaventura
authors.initials: B
authors.lastname: Clotet
authors.forename: Andrea
authors.initials: A
authors.lastname: De Luca
authors.forename: Zehava
authors.initials: Z
authors.lastname: Grossman
authors.forename: Rolf
authors.initials: R
authors.lastname: Kaiser
authors.forename: Anders
authors.initials: A
authors.lastname: Sönnerborg
authors.forename: Carlo
authors.initials: C
authors.lastname: Torti
authors.forename: Eric
authors.initials: E
authors.lastname: Van Wijngaerden
authors.forename: Jean-Claude
authors.initials: JC
authors.lastname: Schmit
authors.forename: Maurizio
authors.initials: M
authors.lastname: Zazzi
authors.forename: Anna-Maria
authors.initials: AM
authors.lastname: Geretti
authors.forename: Anne-Mieke
authors.initials: AM
authors.lastname: Vandamme
authors.forename: Kristel
authors.initials: K
authors.lastname: Van Laethem
mesh_terms: D000328:Adult; D000465:Algorithms; D019380:Anti-HIV Agents / Q000494:pharmacology / Q000627:therapeutic use*; D019992:Databases as Topic; D005260:Female; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology*; D015497:HIV-1 / Q000187:drug effects / Q000235:genetics*; D006801:Humans; D008297:Male; D012372:ROC Curve; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: D019380:Anti-HIV Agents