INTRODUCTION: The Stanford HIV-1 genotypic resistance interpretation algorithm has changed substantially over its lifetime. In many studies, the algorithm version used is not specified. It is easy to assume that results across versions are comparable, but the effects of version changes on resistance calls are unknown. We evaluate these effects for 20 antiretroviral drugs. METHODS: We calculated resistance interpretations for the same 5993 HIV-1 sequences, from participants in AIDS Clinical Trials Group studies, under 14 versions of the Stanford algorithm from 2002 to 2017. Trends over time were assessed using repeated-measures logistic regression. Changes in rule structure and scoring were examined. RESULTS: For most drugs, the proportion of high-level resistance calls on the same sequences was greater using more recent algorithm versions; 16/20 drugs showed significant upward trends. Some drugs, especially tenofovir, had a substantial increase. Only darunavir had a decrease. Algorithm changes impacted calls for subtype C more than B. For intermediate and high-level resistance combined, effects were weaker and more varied. Over time, rules in the Stanford algorithm have become more complex and contain more subrules. The types of rule changes responsible for trends varied widely by drug. DISCUSSION: Reporting the Stanford algorithm version used for resistance analysis is strongly recommended. Caution should be used when comparing results between studies, unless the same version of the algorithm was used. Comparisons using different Stanford versions may be valid for drugs with few changes over time, but for most comparisons, version matters, and for some drugs, the impact is large.
INTRODUCTION: The Stanford HIV-1 genotypic resistance interpretation algorithm has changed substantially over its lifetime. In many studies, the algorithm version used is not specified. It is easy to assume that results across versions are comparable, but the effects of version changes on resistance calls are unknown. We evaluate these effects for 20 antiretroviral drugs. METHODS: We calculated resistance interpretations for the same 5993 HIV-1 sequences, from participants in AIDS Clinical Trials Group studies, under 14 versions of the Stanford algorithm from 2002 to 2017. Trends over time were assessed using repeated-measures logistic regression. Changes in rule structure and scoring were examined. RESULTS: For most drugs, the proportion of high-level resistance calls on the same sequences was greater using more recent algorithm versions; 16/20 drugs showed significant upward trends. Some drugs, especially tenofovir, had a substantial increase. Only darunavir had a decrease. Algorithm changes impacted calls for subtype C more than B. For intermediate and high-level resistance combined, effects were weaker and more varied. Over time, rules in the Stanford algorithm have become more complex and contain more subrules. The types of rule changes responsible for trends varied widely by drug. DISCUSSION: Reporting the Stanford algorithm version used for resistance analysis is strongly recommended. Caution should be used when comparing results between studies, unless the same version of the algorithm was used. Comparisons using different Stanford versions may be valid for drugs with few changes over time, but for most comparisons, version matters, and for some drugs, the impact is large.
Authors: Joke Snoeck; Rami Kantor; Robert W Shafer; Kristel Van Laethem; Koen Deforche; Ana Patricia Carvalho; Brian Wynhoven; Marcelo A Soares; Patricia Cane; John Clarke; Candice Pillay; Sunee Sirivichayakul; Koya Ariyoshi; Africa Holguin; Hagit Rudich; Rosangela Rodrigues; Maria Belen Bouzas; Françoise Brun-Vézinet; Caroline Reid; Pedro Cahn; Luis Fernando Brigido; Zehava Grossman; Vincent Soriano; Wataru Sugiura; Praphan Phanuphak; Lynn Morris; Jonathan Weber; Deenan Pillay; Amilcar Tanuri; Richard P Harrigan; Ricardo Camacho; Jonathan M Schapiro; David Katzenstein; Anne-Mieke Vandamme Journal: Antimicrob Agents Chemother Date: 2006-02 Impact factor: 5.191
Authors: Lin Liu; Susanne May; Douglas D Richman; Frederick M Hecht; Martin Markowitz; Eric S Daar; Jean-Pierre Routy; Joseph B Margolick; Ann C Collier; Christopher H Woelk; Susan J Little; Davey M Smith Journal: AIDS Date: 2008-04-23 Impact factor: 4.177
Authors: Claudia C Barreto; Anna Nishyia; Luciano V Araújo; João E Ferreira; Michael P Busch; Ester C Sabino Journal: J Acquir Immune Defic Syndr Date: 2006-03 Impact factor: 3.731
Authors: Bernard Masquelier; Krishnan Bhaskaran; Deenan Pillay; Robert Gifford; Eric Balestre; Louise Bruun Jørgensen; Court Pedersen; Lia van der Hoek; Maria Prins; Claudia Balotta; Benedetta Longo; Claudia Kücherer; Gabriele Poggensee; Marta Ortiz; Carmen de Mendoza; John Gill; Hervé Fleury; Kholoud Porter Journal: J Acquir Immune Defic Syndr Date: 2005-12-15 Impact factor: 3.731
Authors: Ravindra K Gupta; Michael R Jordan; Binta J Sultan; Andrew Hill; Daniel H J Davis; John Gregson; Anthony W Sawyer; Raph L Hamers; Nicaise Ndembi; Deenan Pillay; Silvia Bertagnolio Journal: Lancet Date: 2012-07-23 Impact factor: 79.321