title: Comparison of algorithms that interpret genotypic HIV-1 drug resistance to determine the prevalence of transmitted drug resistance.
abstract: OBJECTIVE
  We compared eight genotypic interpretation methods to determine whether the method used would affect the rates of reported transmitted drug resistance.
  
  
  METHODS
  Retrospective cohort study.
  
  
  METHODS
  For the International AIDS Society-USA method we classified a mutation as resistant if it was a 'major' resistance-associated mutation. For the Stanford algorithm, we classified a mutation as resistant if the score was at least 60 (Stanford 60), and alternatively, if the score was at least 30 (Stanford 30). For Agence Nationale de Recherches sur le SIDA and Rega, we interpreted resistance as either 'intermediate resistance' or 'resistance' (ANRS 1 and Rega 1), and 'resistance' only (ANRS 2 and Rega 2). We also used the calibrated population resistance algorithm. We then determined the rates of transmitted drug resistance within the Acute Infection Early Disease Research Program cohort (n = 1311) enrolled between March 1995 and August 2006 using each method; agreement was assessed using kappa coefficients.
  
  
  RESULTS
  Differences in estimated rates of transmitted drug resistance using International AIDS Society-USA, calibrated population resistance, Stanford 30, ANRS 1, Rega 1 and Rega 2 methods were mostly minor for resistance to protease and non-nucleoside reverse transcriptase inhibitors (1% range) and more pronounced for nucleoside reverse transcriptase inhibitors (5% range). For these methods kappa agreement was substantial or almost perfect across all drug classes. The Stanford 60 was most conservative.
  
  
  CONCLUSIONS
  The persistent high rates of transmitted drug resistance support the need for continued genotypic surveillance. The currently available interpretation algorithms can be used for this purpose.
authors.affiliation: University of California San Diego, La Jolla, California 92093-0679, USA.
authors.forename: Lin
authors.initials: L
authors.lastname: Liu
authors.forename: Susanne
authors.initials: S
authors.lastname: May
authors.forename: Douglas D
authors.initials: DD
authors.lastname: Richman
authors.forename: Frederick M
authors.initials: FM
authors.lastname: Hecht
authors.forename: Martin
authors.initials: M
authors.lastname: Markowitz
authors.forename: Eric S
authors.initials: ES
authors.lastname: Daar
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Routy
authors.forename: Joseph B
authors.initials: JB
authors.lastname: Margolick
authors.forename: Ann C
authors.initials: AC
authors.lastname: Collier
authors.forename: Christopher H
authors.initials: CH
authors.lastname: Woelk
authors.forename: Susan J
authors.initials: SJ
authors.lastname: Little
authors.forename: Davey M
authors.initials: DM
authors.lastname: Smith
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D019380:Anti-HIV Agents / Q000627:therapeutic use; D015331:Cohort Studies; D024882:Drug Resistance, Viral / Q000235:genetics*; D005260:Female; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy / Q000635:transmission*; D017320:HIV Protease Inhibitors; D015497:HIV-1 / Q000235:genetics*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D018894:Reverse Transcriptase Inhibitors
chemical_list: D019380:Anti-HIV Agents; D017320:HIV Protease Inhibitors; D018894:Reverse Transcriptase Inhibitors