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Comparison of algorithms that interpret genotypic HIV-1 drug resistance to determine the prevalence of transmitted drug resistance

Liu, Lina; May, Susannea; Richman, Douglas Da,b; Hecht, Frederick Mc; Markowitz, Martind; Daar, Eric Se; Routy, Jean-Pierref; Margolick, Joseph Bg; Collier, Ann Ch; Woelk, Christopher Ha; Little, Susan Ja; Smith, Davey Ma,b

AIDS:
doi: 10.1097/QAD.0b013e3282f5ff71
Clinical Science
Abstract

Objective: We compared eight genotypic interpretation methods to determine whether the method used would affect the rates of reported transmitted drug resistance.

Design: 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.

Author Information

From the aUniversity of California San Diego, La Jolla, USA

bVeterans Affairs San Diego Healthcare System, San Diego, USA

cUniversity of California San Francisco, California, USA

dAaron Diamond AIDS Research Center, Rockefeller University, New York, New York, USA

eHarbor-UCLA Medical Center; David Geffen School of Medicine at UCLA, Los Angeles, California, USA

fMcGill University Health Centre, McGill University, Montreal, Quebec, Canada

gJohns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

hUniversity of Washington, Seattle, Washington, USA.

Correspondence to Davey M. Smith, MD, University of California San Diego, 9500 Gilman Drive 0679, La Jolla, CA 92093-0679, USA. Tel: +1 858 552 4339; fax: +1 858 552 7445; e-mail: davey@ucsd.edu

© 2008 Lippincott Williams & Wilkins, Inc.