Abstract
BackgroundTo 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.ResultsTo 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.ConclusionsThe 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.
Highlights
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
TCE repository To demonstrate the ability of the TCE Schema to represent data from different clinics, we collaborated with four clinics to create a publicly available TCE repository
For the purposes of our collaboration we selected TCEs sharing each of the following criteria: (i) evidence for virological failure prior to a change in therapy defined a plasma HIV-1 RNA level of >1,000 copies/ml obtained within 8 weeks before the change; (ii) a complete list of ARVs received prior to baseline; (iii) a change in ARVs occurring within 24 weeks of a baseline genotypic resistance test; (iv) a new salvage regimen administered for at least four weeks; (v) one or more CD4 counts within 24 weeks prior to the ARV change; and (vi) two or more plasma HIV-1 RNA levels within the first 36 weeks while taking the salvage regimen
Summary
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, often differ in their inclusion and virological response criteria making direct comparisons of study results problematic. 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. To identify determinants of successful antiretroviral (ARV) therapy in HIV-1-infected patients for whom a previous ARV treatment regimen has failed, researchers study clinical data associated with treatment-change episodes (TCEs) [1]. To demonstrate the utility of such a repository for hypothesis generation and knowledge discovery, we analyzed a subset of the repository to obtain insights into the optimal use of second-line therapy in resource-limited settings
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