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

Revell, Andrew D., Wang, Dechao, Wood, Robin, Morrow, Carl, Tempelman, Hugo, Hamers, Raph, Alvarez-Uria, Gerardo, Streinu-Cercel, Adrian, Ene, Luminita, Wensing, Annemarie, Reiss, Peter, van Sighem, Ard I., Nelson, Mark, Emery, Sean, Montaner, Julio S. G., Lane, H. Clifford and Larder, Brendan A. (2014) 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, 69 4: 1104-1110. doi:10.1093/jac/dkt447


Author Revell, Andrew D.
Wang, Dechao
Wood, Robin
Morrow, Carl
Tempelman, Hugo
Hamers, Raph
Alvarez-Uria, Gerardo
Streinu-Cercel, Adrian
Ene, Luminita
Wensing, Annemarie
Reiss, Peter
van Sighem, Ard I.
Nelson, Mark
Emery, Sean
Montaner, Julio S. G.
Lane, H. Clifford
Larder, Brendan A.
Title 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 name Journal of Antimicrobial Chemotherapy   Check publisher's open access policy
ISSN 1460-2091
0305-7453
Publication date 2014-04-01
Sub-type Article (original research)
DOI 10.1093/jac/dkt447
Open Access Status Not yet assessed
Volume 69
Issue 4
Start page 1104
End page 1110
Total pages 7
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Language eng
Formatted abstract
Objectives
The optimal individualized selection of antiretroviral drugs in resource-limited settings is challenging because of the limited availability of drugs and genotyping. Here we describe the development of the latest computational models to predict the response to combination antiretroviral therapy without a genotype, for potential use in such settings.

Methods
Random forest models were trained to predict the probability of a virological response to therapy (<50 copies HIV RNA/mL) following virological failure using the following data from 22 567 treatment-change episodes including 1090 from southern Africa: baseline viral load and CD4 cell count, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. The models were assessed during cross-validation and with an independent global test set of 1000 cases including 100 from southern Africa. The models' accuracy [area under the receiver-operating characteristic curve (AUC)] was evaluated and compared with genotyping using rules-based interpretation systems for those cases with genotypes available.

Results
The models achieved AUCs of 0.79–0.84 (mean 0.82) during cross-validation, 0.80 with the global test set and 0.78 with the southern African subset. The AUCs were significantly lower (0.56–0.57) for genotyping.

Conclusions
The models predicted virological response to HIV therapy without a genotype as accurately as previous models that included a genotype. They were accurate for cases from southern Africa and significantly more accurate than genotyping. These models will be accessible via the online treatment support tool HIV-TRePS and have the potential to help optimize antiretroviral therapy in resource-limited settings where genotyping is not generally available.
Keyword Antiretroviral therapy
Genotyping
Resource-limited settings
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Medicine Publications
 
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