BACKGROUND AND OBJECTIVES: Guidelines indicate a plasma HIV-1 RNA load of 500-1000 copies/mL as the minimal threshold for antiretroviral drug resistance testing. Resistance testing at lower viral load levels may be useful to guide timely treatment switches, although data on the clinical utility of this remain limited. We report here the influence of viral load levels on the probability of detecting drug resistance mutations (DRMs) and other mutations by routine genotypic testing in a large multicentre European cohort, with a focus on tests performed at a viral load <1000 copies/mL. METHODS: A total of 16 511 HIV-1 reverse transcriptase and protease sequences from 11 492 treatment-experienced patients were identified, and linked to clinical data on viral load, CD4 T cell counts and antiretroviral treatment history. Test results from 3162 treatment-naive patients served as controls. Multivariable analysis was employed to identify predictors of reverse transcriptase and protease DRMs. RESULTS: Overall, 2500/16 511 (15.14%) test results were obtained at a viral load <1000 copies/mL. Individuals with viral load levels of 1000-10000 copies/mL showed the highest probability of drug resistance to any drug class. Independently from other measurable confounders, treatment-experienced patients showed a trend for DRMs and other mutations to decrease at viral load levels <500 copies/mL. CONCLUSIONS: Genotypic testing at low viral load may identify emerging antiretroviral drug resistance at an early stage, and thus might be successfully employed in guiding prompt management strategies that may reduce the accumulation of resistance and cross-resistance, viral adaptive changes, and larger viral load increases.
BACKGROUND AND OBJECTIVES: Guidelines indicate a plasma HIV-1 RNA load of 500-1000 copies/mL as the minimal threshold for antiretroviral drug resistance testing. Resistance testing at lower viral load levels may be useful to guide timely treatment switches, although data on the clinical utility of this remain limited. We report here the influence of viral load levels on the probability of detecting drug resistance mutations (DRMs) and other mutations by routine genotypic testing in a large multicentre European cohort, with a focus on tests performed at a viral load <1000 copies/mL. METHODS: A total of 16 511 HIV-1 reverse transcriptase and protease sequences from 11 492 treatment-experienced patients were identified, and linked to clinical data on viral load, CD4 T cell counts and antiretroviral treatment history. Test results from 3162 treatment-naive patients served as controls. Multivariable analysis was employed to identify predictors of reverse transcriptase and protease DRMs. RESULTS: Overall, 2500/16 511 (15.14%) test results were obtained at a viral load <1000 copies/mL. Individuals with viral load levels of 1000-10000 copies/mL showed the highest probability of drug resistance to any drug class. Independently from other measurable confounders, treatment-experienced patients showed a trend for DRMs and other mutations to decrease at viral load levels <500 copies/mL. CONCLUSIONS: Genotypic testing at low viral load may identify emerging antiretroviral drug resistance at an early stage, and thus might be successfully employed in guiding prompt management strategies that may reduce the accumulation of resistance and cross-resistance, viral adaptive changes, and larger viral load increases.
Authors: M Fabbiani; L Bracciale; E Ragazzoni; R Santangelo; P Cattani; S Di Giambenedetto; G Fadda; P Navarra; R Cauda; A De Luca Journal: Infection Date: 2011-08-25 Impact factor: 3.553
Authors: Gert U van Zyl; Lisa M Frenkel; Michael H Chung; Wolfgang Preiser; John W Mellors; Jean B Nachega Journal: AIDS Date: 2014-11-28 Impact factor: 4.177
Authors: Patrick Ryscavage; Sean Kelly; Jonathan Z Li; P Richard Harrigan; Babafemi Taiwo Journal: Antimicrob Agents Chemother Date: 2014-04-14 Impact factor: 5.191
Authors: Victoria Johnston; Katherine Fielding; Salome Charalambous; Mildred Mampho; Gavin Churchyard; Andrew Phillips; Alison D Grant Journal: PLoS One Date: 2012-05-29 Impact factor: 3.240