Literature DB >> 18598191

Initiatives for developing and comparing genotype interpretation systems: external validation of existing systems for didanosine against virological response.

Lambert Assoumou1, Françoise Brun-Vézinet, Alessandro Cozzi-Lepri, Daniel Kuritzkes, Andrew Phillips, Andrew Zolopa, Victor Degruttola, Veronica Miller, Dominique Costagliola.   

Abstract

BACKGROUND: This study was performed to investigate the concordance between commonly used human immunodeficiency virus type 1 (HIV-1) drug resistance interpretation systems for didanosine (ddI) and their ability to predict responses at weeks 8 and 24.
METHODS: The study included drug-experienced HIV-infected patients who had viral loads >500 copies/mL and who underwent a genotypic resistance test when beginning a ddI-containing therapy. The interpretations of the level of resistance to ddI were compared for the 6 interpretation systems. Linear and logistic regression were used to assess their ability to predict responses for weeks 8 and 24, respectively.
RESULTS: The 1453 patients had a median viral load of 4.3 log10 copies/mL, and 31% were preexposed to ddI. Complete concordance was found for 19% of samples, partial discordance for 49%, and complete discordance for 32%. The median viral load reduction at week 8 was 1.36 log10 copies/mL, and 56% of patients had viral loads > 400 copies/mL at week 24. At week 8, all systems correctly predicted a greater viral load reduction in patients with susceptible viruses than in those with resistant viruses, but only the Stanford system was able to discriminate between patients with resistant, intermediately resistant, and susceptible viruses. No systems predicted virological response correctly at week 24.
CONCLUSIONS: Our results show the need for standardized methods to establish genotypic interpretation systems.

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Year:  2008        PMID: 18598191     DOI: 10.1086/590156

Source DB:  PubMed          Journal:  J Infect Dis        ISSN: 0022-1899            Impact factor:   5.226


  5 in total

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

Authors:  Stephen A Hart; Saran Vardhanabhuti; Sarah A Strobino; Linda J Harrison
Journal:  J Acquir Immune Defic Syndr       Date:  2018-09-01       Impact factor: 3.731

2.  Predictive value of HIV-1 genotypic resistance test interpretation algorithms.

Authors:  Soo-Yon Rhee; W Jeffrey Fessel; Tommy F Liu; Natalia M Marlowe; Charles M Rowland; Richard A Rode; Anne-Mieke Vandamme; Kristel Van Laethem; Françoise Brun-Vezinet; Vincent Calvez; Jonathan Taylor; Leo Hurley; Michael Horberg; Robert W Shafer
Journal:  J Infect Dis       Date:  2009-08-01       Impact factor: 5.226

3.  Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data.

Authors:  Allal Houssaïni; Lambert Assoumou; Anne Geneviève Marcelin; Jean Michel Molina; Vincent Calvez; Philippe Flandre
Journal:  AIDS Res Treat       Date:  2012-04-03

4.  Can linear regression modeling help clinicians in the interpretation of genotypic resistance data? An application to derive a lopinavir-score.

Authors:  Alessandro Cozzi-Lepri; Mattia C F Prosperi; Jesper Kjær; David Dunn; Roger Paredes; Caroline A Sabin; Jens D Lundgren; Andrew N Phillips; Deenan Pillay
Journal:  PLoS One       Date:  2011-11-16       Impact factor: 3.240

5.  Scoring methods for building genotypic scores: an application to didanosine resistance in a large derivation set.

Authors:  Allal Houssaini; Lambert Assoumou; Veronica Miller; Vincent Calvez; Anne-Geneviève Marcelin; Philippe Flandre
Journal:  PLoS One       Date:  2013-03-21       Impact factor: 3.240

  5 in total

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