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

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Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.

OBJECTIVES Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs....

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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

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The use of computational models to predict response to HIV therapy for clinical cases in Romania.

INTRODUCTION A major challenge in Romania is the optimisation of antiretroviral therapy for the many HIV-infected adults with, on average, a decade of treatment experience. The RDI has developed computational models that predict virological response to therapy but these require a genotype, which is not routinely available in Romania. Moreover the models, which were trained without any Romanian ...

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ژورنال

عنوان ژورنال: Journal of the International AIDS Society

سال: 2012

ISSN: 1758-2652

DOI: 10.7448/ias.15.6.18114