Computational models that predict response to HIV therapy can reduce virological failure and therapy costs in resource‐limited settings

Computational models that predict response to HIV therapy can reduce virological failure and therapy costs in resource‐limited settings

The results of genotypic HIV drug‐resistance testing are, typically, 60–65% predictive of response to combination antiretroviral therapy (ART) and have proven valuable for guiding treatment changes. However, genotyping is not available in many resource‐limited settings (RLS). The purpose of this stu...

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Veröffentlicht in:Journal of the International AIDS Society 2012-11, Vol.15 (S4), p.1-1
Hauptverfasser: Revell, A, Wang, D, Alvarez‐Uria, G, Streinu‐Cercel, A, Ene, L, Wensing, A, Hamers, R, Morrow, C, Wood, R, Tempelman, H, Dewolf, F, Nelson, M, Montaner, J, Lane, H, Larder, B
Format: Artikel
Sprache:eng
Schlagworte:
Acquired immune deficiency syndrome
AIDS
Antiretroviral agents
Antiretroviral drugs
Drug therapy
Failure
HIV
Human immunodeficiency virus
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Zusammenfassung:The results of genotypic HIV drug‐resistance testing are, typically, 60–65% predictive of response to combination antiretroviral therapy (ART) and have proven valuable for guiding treatment changes. However, genotyping is not available in many resource‐limited settings (RLS). The purpose of this study was to develop computational models that can predict response to ART without a genotype and evaluate their potential as a treatment support tool in RLS. Random forest models were trained to predict the probability of response to ART (
ISSN:1758-2652
1758-2652
DOI:10.7448/IAS.15.6.18114