Standardized representation, visualization and searchable repository of antiretroviral treatment-change episodes

doi:10.1186/1742-6405-9-13

AIDS Res Ther. 2012; 9: 13.

Published online 2012 May 3. doi: 10.1186/1742-6405-9-13

PMCID: PMC3439255

Standardized representation, visualization and searchable repository of antiretroviral treatment-change episodes

Soo-Yon Rhee,^1,¹⁰ Jose Luis Blanco,² Tommy F Liu,¹ Iñaki Pere,² Rolf Kaiser,³ Maurizio Zazzi,⁴ Francesca Incardona,⁵ William Towner,⁶ Josep Maria Gatell,² Andrea De Luca,^7,⁸ W Jeffrey Fessel,⁹ and Robert W Shafer¹

¹Department of Medicine, Stanford University, Stanford, CA, USA

²Hospital Clinic Universitari-IDIBAPS, University of Barcelona, Barcelona, Spain

³Institute of Virology, EuResist Network GEIE, University of Cologne, Cologne, Germany

⁴Department of Medical Biotechnologies, EuResist Network GEIE, University of Siena, Siena, Italy

⁵Informasrl, EuResist Network GEIE, Rome, Italy

⁶Department of Infectious Disease, Kaiser Permanente, Los Angeles, CA, USA

⁷Institute of Clinical Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy

⁸Unit of Infectious Diseases 2, University Hospital of Siena, Siena, Italy

⁹Kaiser Permanente Medical Care Program, South San Francisco, CA, USA

¹⁰Division of Infectious Diseases, Room S-169, Stanford University Medical Center, 300 Pasteur Drive, Stanford, CA, 94305, USA

Corresponding author.

Soo-Yon Rhee: syrhee/at/stanford.edu; Jose Luis Blanco: jlblanco/at/clinic.ub.es; Tommy F Liu: tliu/at/stanford.edu; Iñaki Pere: iperez1/at/clinic.ub.es; Rolf Kaiser: rolf.kaiser/at/uk-koeln.de; Maurizio Zazzi: zazzi/at/unisi.it; Francesca Incardona: f.incardona/at/informacro.info; William Towner: william.j.towner/at/kp.org; Josep Maria Gatell: gatell0/at/attglobal.net; Andrea De Luca: andrea.deluca/at/rm.unicatt.it; W Jeffrey Fessel: jeffrey.fessel/at/kp.org; Robert W Shafer: rshafer/at/stanford.edu

Received October 19, 2011; Accepted March 16, 2012.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

To identify the determinants of successful antiretroviral (ARV) therapy, researchers study the virological responses to treatment-change episodes (TCEs) accompanied by baseline plasma HIV-1 RNA levels, CD4+ T lymphocyte counts, and genotypic resistance data. Such studies, however, often differ in their inclusion and virological response criteria making direct comparisons of study results problematic. Moreover, the absence of a standard method for representing the data comprising a TCE makes it difficult to apply uniform criteria in the analysis of published studies of TCEs.

Results

To facilitate data sharing for TCE analyses, we developed an XML (Extensible Markup Language) Schema that represents the temporal relationship between plasma HIV-1 RNA levels, CD4 counts and genotypic drug resistance data surrounding an ARV treatment change. To demonstrate the adaptability of the TCE XML Schema to different clinical environments, we collaborate with four clinics to create a public repository of about 1,500 TCEs. Despite the nascent state of this TCE XML Repository, we were able to perform an analysis that generated a novel hypothesis pertaining to the optimal use of second-line therapies in resource-limited settings. We also developed an online program (TCE Finder) for searching the TCE XML Repository and another program (TCE Viewer) for generating a graphical depiction of a TCE from a TCE XML Schema document.

Conclusions

The TCE Suite of applications – the XML Schema, Viewer, Finder, and Repository – addresses several major needs in the analysis of the predictors of virological response to ARV therapy. The TCE XML Schema and Viewer facilitate sharing data comprising a TCE. The TCE Repository, the only publicly available collection of TCEs, and the TCE Finder can be used for testing the predictive value of genotypic resistance interpretation systems and potentially for generating and testing novel hypotheses pertaining to the optimal use of salvage ARV therapy.

Keywords: Human immunodeficiency virus, Antiretroviral treatment, Drug resistance, Clinical outcomes, XML schema, Database

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