Evidence map›Paper›PMID 29912005›Full record

ArticleJournal of acquired immune deficiency syndromes (1999)2018

Impact of Changes Over Time in the Stanford University Genotypic Resistance Interpretation Algorithm.

Stephen A Hart, Saran Vardhanabhuti, Sarah A Strobino, Linda J Harrison

Abstract read
In one paragraph

Article in Journal of acquired immune deficiency syndromes (1999), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Stephen A HartFrontier Science Foundation, Amherst, NY.
Saran VardhanabhutiDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Sarah A StrobinoFrontier Science Foundation, Amherst, NY.
Linda J HarrisonDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.

Funding

Leadership and Operations Center (LOC), AIDS Clinical Trials Group (ACTG); LOC 1/UM1AI068636 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Joseph J Eron, RAJESH T GANDHI · 2011 to 2026
$1073.1M
Statistical and Data Management Center (SDMC), AIDS Clinical Trials Group (ACTG)UM1AI068634 · NIAID · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Marlene Ann Cooper, Michael David Hughes · 2011 to 2026
$246.6M
Validation, CLIA and Qualification (VQC): Enhancing the RS ratio as a tool for AIDS Clinical Trial Group (ACTG) tuberculosis trialsUM1AI106701 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Grace M Aldrovandi · 2014 to 2026
$116.8M
Statistical and Data Management Center for the AIDS Clinical Trials GroupU01AI068634 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI HUGHES, MICHAEL DAVID · 2006 to 2010
$71.9M
LC - IMPAACT Leadership GroupUM1AI106716 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Grace M Aldrovandi · 2014 to 2026
$47.2M
NIAID NIH HHS U01 AI068634NIAID NIH HHS UM1 AI068634NIAID NIH HHS UM1 AI068636NIAID NIH HHS UM1 AI106701NIAID NIH HHS UM1 AI106716
6 · The paper itself

Abstract

introductionThe 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.

methodsWe 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.

resultsFor 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.

Indexed as

AlgorithmsGenotypeAnti-HIV AgentsDrug Resistance, ViralHIV-1HumansAnti-HIV Agents

Identifiers

PMID29912005
PMCPMC6241513

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.