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BioMed Research International
Volume 2013 (2013), Article ID 579741, 6 pages
http://dx.doi.org/10.1155/2013/579741
Research Article

Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

1The HIV Resistance Response Database Initiative (RDI), 14 Union Square, London N1 7DH, UK
2Rural Development Trust (RDT) Hospital, Bathalapalli, 515661 AP, India
3Chelsea and Westminster Hospital, London SW10 9NH, UK
4BC Centre for Excellence in HIV/AIDS, Vancouver, Canada
5National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA

Received 22 April 2013; Accepted 9 July 2013

Academic Editor: Marcelo A. Soares

Copyright © 2013 Andrew D. Revell et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective. Antiretroviral drug selection in resource-limited settings is often dictated by strict protocols as part of a public health strategy. The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings. Methods. The HIV-TRePS computational models were used to predict the probability of response to therapy for 206 cases of treatment change following failure in India. The models were used to identify alternative locally available 3-drug regimens, which were predicted to be effective. The costs of these regimens were compared to those actually used in the clinic. Results. The models predicted the responses to treatment of the cases with an accuracy of 0.64. The models identified alternative drug regimens that were predicted to result in improved virological response and lower costs than those used in the clinic in 85% of the cases. The average annual cost saving was $364 USD per year (41%). Conclusions. Computational models that do not require a genotype can predict and potentially avoid treatment failure and may reduce therapy costs. The use of such a system to guide therapeutic decision-making could confer health economic benefits in resource-limited settings.