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BioMed Research International
Volume 2013 (2013), Article ID 579741, 6 pages
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.
- World Health Organisation, Antiretroviral Therapy for HIV Infection in Adolescents and Adults: Recommendations for a Public Health Approach—2010 Revision, WHO, Geneva, Switzerland, 2010.
- DART Trial Team, “Routine versus clinically driven laboratory monitoring of HIV antiretroviral therapy in Africa (DART): a randomised non-inferiority trial,” The Lancet, vol. 375, no. 9709, pp. 123–131, 2010.
- M. C. Hosseinipour, J. J. G. van Oosterhout, R. Weigel et al., “The public health approach to identify antiretroviral therapy failure: high-level nucleoside reverse transcriptase inhibitor resistance among Malawians failing first-line antiretroviral therapy,” AIDS, vol. 23, no. 9, pp. 1127–1134, 2009.
- K. C. E. Sigaloff, R. L. Hamers, C. L. Wallis et al., “Unnecessary antiretroviral treatment switches and accumulation of HIV resistance mutations; two arguments for viral load monitoring in Africa,” Journal of Acquired Immune Deficiency Syndromes, vol. 58, no. 1, pp. 23–31, 2011.
- J. Zhou, P. C. K. Li, N. Kumarasamy et al., “Deferred modification of antiretroviral regimen following documented treatment failure in Asia: results from the TREAT Asia HIV Observational Database (TAHOD),” HIV Medicine, vol. 11, no. 1, pp. 31–39, 2010.
- O. Keiser, B. H. Chi, T. Gsponer et al., “Outcomes of antiretroviral treatment in programmes with and without routine viral load monitoring in southern Africa,” AIDS, vol. 25, no. 14, pp. 1761–1769, 2011.
- R. E. Barth, S. C. Aitken, H. Tempelman et al., “Accumulation of drug resistance and loss of therapeutic options precede commonly used criteria for treatment failure in HIV-1 subtype-C-infected patients,” Antiviral Therapy, vol. 17, no. 2, pp. 377–386, 2012.
- M. A. Thompson, J. A. Aberg, J. F. Hoy, et al., “Antiretroviral treatment of adult HIV infection: 2012 recommendations of the International Antiviral Society-USA panel,” The Journal of the American Medical Association, vol. 308, no. 4, pp. 387–402, 2012.
- B. A. Larder, V. DeGruttola, S. Hammer, et al., “The international HIV resistance response database initiative: a new global collaborative approach to relating viral genotype treatment to clinical outcome.,” Antiviral Therapy, vol. 7, article S111, 2002.
- A. D. Revell, D. Wang, M. A. Boyd et al., “The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool,” AIDS, vol. 25, no. 15, pp. 1855–1863, 2011.
- A. D. Revell, D. Wang, R. Wood, et al., “Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings,” Journal of Antimicrobial Chemotherapy, vol. 68, no. 6, pp. 1406–1414, 2013.
- B. A. Larder, A. Revell, J. M. Mican et al., “Clinical evaluation of the potential utility of computational modeling as an HIV treatment selection tool by physicians with considerable HIV experience,” AIDS Patient Care and STDs, vol. 25, no. 1, pp. 29–36, 2011.
- A. D. Revell, D. Wang, R. Harrigan et al., “Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings,” Journal of Antimicrobial Chemotherapy, vol. 65, no. 4, Article ID dkq032, pp. 605–607, 2010.
- A. D. Revell, L. Ene, D. A. Duiculescu, et al., “The use of computational models to predict response to HIV therapy for clinical cases in Romania,” Germs, vol. 2, no. 1, pp. 6–11, 2012.
- A. D. Revell, D. Wang, R. Harrigan, et al., “Computational models developed without a genotype for resource-poor countries predict response to HIV treatment with 82% accuracy,” Antiviral Therapy, vol. 14, Supplement 1, article A38, 2009.
- B. A. Larder, A. D. Revell, D. Wang, et al., “Modelling response to antiretroviral therapy with a genotype as a clinical tool for resource-limited settings,” Antiviral Therapy, vol. 16, Supplement 1, article A42, 2011.
- A. D. Revell, D. Wang, A. Streinu-Cercel, et al., “Models that accurately predict response to HIV therapy are generalisable to unfamiliar datasets and settings,” Antiviral Therapy, vol. 17, Supplement 1, article A145, 2012.
- G. Alvarez-Uria, M. Midde, R. Pakam, and P. K. Naik, “Gender differences, routes of transmission, socio-demographic characteristics and prevalence of HIV related infections of adults and children in an HIV cohort from a rural district of India,” Infectious Disease Reports, vol. 4, article e19, 2012.
- G. Alvarez-Uria, M. Midde, R. Pakam, S. Kannan, L. Bachu, and P. K. Naik, “Factors associated with late presentation of HIV and estimation of antiretroviral treatment need according to CD4 lymphocyte count in a resource-limited setting: data from an HIV cohort study in India,” Interdisciplinary Perspectives on Infectious Diseases, vol. 2012, Article ID 293795, 7 pages, 2012.
- B. A. Larder, A. D. Revell, D. Wang, et al., “Accurate prediction of response to HIV therapy without a genotype a potential tool for therapy optimisation in resource-limited settings,” Antiviral Therapy, vol. 18, Supplement 1, article A20, 2013.
- World Health Organisation, Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection: Recommendations for a Public Health Approach, WHO, Geneva, Switzerland, 2013.