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J Antimicrob Chemother. 2016 Oct;71(10):2928-37. doi: 10.1093/jac/dkw217. Epub 2016 Jun 20.

An update to the HIV-TRePS system: the development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype.

Author information

1
The HIV Resistance Response Database Initiative (RDI), London, UK andrewrevell@hivrdi.org.
2
The HIV Resistance Response Database Initiative (RDI), London, UK.
3
Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa.
4
Ndlovu Care Group, Elandsdoorn, South Africa.
5
Departments of Internal Medicine and Global Health, Academic Medical Centre of the University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands.
6
Departments of Internal Medicine and Global Health, Academic Medical Centre of the University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands Stichting HIV Monitoring, Amsterdam, The Netherlands.
7
Stichting HIV Monitoring, Amsterdam, The Netherlands.
8
Chelsea and Westminster Hospital, London, UK.
9
BC Centre for Excellence in HIV/AIDS, Vancouver, Canada.
10
National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA.

Abstract

OBJECTIVES:

Optimizing antiretroviral drug combination on an individual basis in resource-limited settings is challenging because of the limited availability of drugs and genotypic resistance testing. Here, we describe our latest computational models to predict treatment responses, with or without a genotype, and compare the potential utility of global and local models as a treatment tool for South Africa.

METHODS:

Global random forest models were trained to predict the probability of virological response to therapy following virological failure using 29 574 treatment change episodes (TCEs) without a genotype, 3179 of which were from South Africa and were used to develop local models. In addition, 15 130 TCEs including genotypes were used to develop another set of models. The 'no-genotype' models were tested with an independent global test set (n = 1700) plus a subset from South Africa (n = 222). The genotype models were tested with 750 independent cases.

RESULTS:

The global no-genotype models achieved area under the receiver-operating characteristic curve (AUC) values of 0.82 and 0.79 with the global and South African tests sets, respectively, and the South African models achieved AUCs of 0.70 and 0.79. The genotype models achieved an AUC of 0.84. The global no-genotype models identified more alternative, locally available regimens that were predicted to be effective for cases that failed their new regimen in the South African clinics than the local models. Both sets of models were significantly more accurate predictors of outcomes than genotyping with rules-based interpretation.

CONCLUSIONS:

These latest global models predict treatment responses accurately even without a genotype, out-performed the local South African models and have the potential to help optimize therapy, particularly in resource-limited settings.

PMID:
27330070
PMCID:
PMC5031919
DOI:
10.1093/jac/dkw217
[Indexed for MEDLINE]
Free PMC Article

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