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.
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.
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.
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.
*Frontier Science Foundation, Amherst, NY; and
†Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Correspondence to: S. A. Hart, Frontier Science Foundation, 4033 Maple Road, Amherst, NY 14226 (e-mail: firstname.lastname@example.org).
Supported by, as part of the AIDS Clinical Trials Group, the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award numbers UM1 AI068634, UM1 AI068636, and UM1 AI106701. The content is solely the responsibility of the authors and does not necessarily represent the views of the National Institutes of Health.
Presented in part at XXV International HIV Drug Resistance Workshop; February 20–21, 2016; Boston, MA.
The authors have no conflicts of interest to disclose.
Received December 09, 2017
Accepted June 11, 2018