An update to the HIV-TRePS system: The development of new computational models that do not require a genotype to predict HIV treatment outcomes

Journal of Antimicrobial Chemotherapy

Volumn 69, Issue 4, 2014, Pages 1104-1110

  Revell, Andrew D ^a   Wang, Dechao ^a   Wood, Robin ^b   Morrow, Carl ^b   Tempelman, Hugo ^c   Hamers, Raph ^d   Alvarez Uria, Gerardo ^e   Streinu Cercel, Adrian ^f   Ene, Luminita ^g   Wensing, Annemarie ^h   Reiss, Peter ^d,i   Van Sighem, Ard I ⁱ   Nelson, Mark ^j   Emery, Sean ^k   Montaner, Julio S G ^l   Lane, H Clifford ^m   Larder, Brendan A ^a   Van sighem, Ard ⁿ   Harrigan, Richard ^o   Rinke de Wit, Tobias ^p   Sigaloff, Kim ^p   Agan, Brian ^q   Marconi, Vincent ^q   Wegner, Scott ^q   Sugiura, Wataru ^r   Zazzi, Maurizio ^p   Gatell, Jose ^s   Lazzari, Elisa ^s   Gazzard, Brian ^j   Pozniak, Anton ^j   Mandalia, Sundhiya ^j   Ruiz, Lidia ^t   Clotet, Bonaventura ^t   Staszewski, Schlomo ^u   Torti, Carlo ^v   Metcalf, Julie ^r   Perez Elias, Maria Jesus ^w   Carr, Andrew ^x   Norris, Richard ^x   Hesse, Karl ^x   Vlahakis, Emanuel ^p   Barth, Roos ^c   Dragovic, Gordana ^y   more..

^a HIV Resistance Response Database Initiative (RDI)   (United Kingdom)

^b UNIVERSITY OF CAPE TOWN   (South Africa)

^c NDLOVU CARE GROUP   (South Africa)

^d ACADEMIC MEDICAL CENTER   (Netherlands)

^e Rural Development Trust (RDT) Hospital   (India)

^f Prof Dr Matei Bals National Institute of Infectious Diseases   (Romania)

^g VICTOR BABES NATIONAL INSTITUTE OF PATHOLOGY   (Romania)

^h UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

ⁱ STICHTING HIV MONITORING   (Netherlands)

^j CHELSEA AND WESTMINSTER HOSPITAL   (United Kingdom)

^k UNIVERSITY OF NEW SOUTH WALES   (Australia)

^l BRITISH COLUMBIA CENTRE FOR EXCELLENCE IN HIV AIDS   (Canada)

^m NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES   (United States)

ⁿ ATHENA   (Netherlands)

^o BC Center for Excellence in HIV and AIDS   (Canada)

^p PASER M cohort ^*  (Australia)

^q ARMED FORCES INSTITUTE OF PATHOLOGY   (United States)

^r NATIONAL INSTITUTES OF HEALTH   (United States)

^s University Hospital   (Spain)

^t Fundacion IrsiCaixa   (Spain)

^u UNIVERSITY HOSPITAL FRANKFURT   (Germany)

^v UNIVERSITY OF BRESCIA   (Italy)

^w INSTITUTO RAMÓN Y CAJAL DE INVESTIGACIÓN SANITARIA IRYCIS   (Spain)

^x ST VINCENT S HOSPITAL   (Australia)

^y UNIVERSITY OF BELGRADE   (Serbia)

more..

Author keywords

Antiretroviral therapy; Genotyping; Resource limited settings

Indexed keywords

ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT; NONNUCLEOSIDE REVERSE TRANSCRIPTASE INHIBITOR; PROTEINASE INHIBITOR; RNA DIRECTED DNA POLYMERASE INHIBITOR; VIRUS RNA;

ACCURACY; ADULT; ANTIVIRAL THERAPY; AREA UNDER THE CURVE; ARTICLE; CD4 LYMPHOCYTE COUNT; COMPUTER MODEL; CONTROLLED STUDY; DRUG TREATMENT FAILURE; FEMALE; FOLLOW UP; GENOTYPE; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; MAJOR CLINICAL STUDY; MALE; MEDICAL HISTORY; PREDICTION; PROBABILITY; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; SOUTH AFRICA; TREATMENT OUTCOME; TREATMENT RESPONSE; VIRUS LOAD;

ANTIRETROVIRAL THERAPY; GENOTYPING; RESOURCE-LIMITED SETTINGS;

ADULT; ANTI-RETROVIRAL AGENTS; ANTIRETROVIRAL THERAPY, HIGHLY ACTIVE; COMPUTER SIMULATION; FEMALE; GENOTYPE; HIV; HIV INFECTIONS; HUMANS; MALE; PROGNOSIS; SALVAGE THERAPY; TREATMENT OUTCOME;

EID: 84896477071     PISSN: 03057453     EISSN: 14602091     Source Type: Journal    
DOI: 10.1093/jac/dkt447     Document Type: Article

References (13)

1. Revell AD, Wang D, Wood R et al. Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings. J Antimicrob Chemother 2013; 68: 1406-14.
2. Larder BA, Revell AD, Mican J 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 STDS 2011; 25: 29-36.
3. Revell AD, Wang D, Boyd MA et al. The development of an expert systemto predict virological response to HIV therapy as part of an online treatment support tool. AIDS 2011; 25: 1855-63.
4. Zazzi M, Kaiser R, Sönnerborg A et al. Prediction of response to antiretroviral therapy by human experts and by the EuResist data-driven expert system (the EVE study). HIV Med 2010; 12: 211-8.
5. Larder BA, Wang D, Revell A et al. The development of artificial neural networks to predict virological response to combination HIV therapy. Antivir Ther 2007; 12: 15-24.
6. Wang D, Larder BA, Revell AD et al. A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy. Artif Intell Med 2009; 47: 63-74.
7. Yazdanpanah Y. Multidrug resistance: a clinical approach. Curr Opin HIV AIDS 2009; 4: 499-506.
8. Ajose O, Mookerjee S, Mills EJ et al. Treatment outcomes of patients on second-line antiretroviral therapy in resource-limited settings: a systematic review and meta-analysis. AIDS 2012; 26: 929-38.
9. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44: 837-45.
10. Revell AD, Wang D, Harrigan R et al. Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings. J Antimicrob Chemother 2010; 65: 605-7.
11. Prosperi MCF, Rosen-Zvi M, Altman A et al. Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models. PLoS One 2010; 5: e13753.
12. WHO. Consolidated Guidelines on the Use of Antiretroviral Drugs For Treating and Preventing HIV Infection: Recommendations For a Public Health Approach. Geneva: WHO, 2013.
13. Stevens WS, Scott LE, Crowe SM. Quantifying HIV for monitoring antiretroviral therapy in resource-poor settings. J Infect Dis 2010; 201(Suppl 1): S16-26.