Skip Navigation



JAC Advance Access published online on December 19, 2003
Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkh024
© 2003 by The British Society for Antimicrobial Chemotherapy
This Article
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
53/2/140    most recent
dkh024v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Vergu, E.
Right arrow Articles by Golmard, J.-L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Vergu, E.
Right arrow Articles by Golmard, J.-L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2003 The British Society for Antimicrobial Chemotherapy

Leading article

Available clinical markers of treatment outcome integrated in mathematical models to guide therapy in HIV infection

Elisabeta Vergu 1 *, Alain Mallet 1 , and Jean-Louis Golmard 1

1 INSERM U436, Mathematical and Statistical Modelling in Biology and Medicine, CHU Pitié-Salpêtrière, Paris, France

* Corresponding author. E-mail: eve{at}biomath.jussieu.fr.

Abstract

Because treatment failure in many HIV-infected persons may be due to multiple causes, including resistance to antiretroviral agents, it is important to better tailor drug therapy to individual patients. This improvement requires the prediction of treatment outcome from baseline immunological or virological factors, and from results of resistance tests. Here, we review briefly the available clinical factors that have an impact on therapy outcome, and discuss the role of a predictive modelling approach integrating these factors proposed in a previous work. Mathematical and statistical models could become essential tools to address questions that are difficult to study clinically and experimentally, thereby guiding decisions in the choice of individualized drug regimens.

Keywords: resistance tests, modelling, therapy outcome
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.