Estimating prevalence of accumulated HIV-1 drug resistance in a cohort of patients on antiretroviral therapy

Wendy P Bannister; Alessandro Cozzi-Lepri; Jesper Kjær; Bonaventura Clotet; Adriano Lazzarin; Jean-Paul Viard; Gitte Kronborg; Dan Duiculescu; Marek Beniowski; Ladislav Machala; Andrew Phillips; EuroSIDA group

doi:10.1093/jac/dkr006

Estimating prevalence of accumulated HIV-1 drug resistance in a cohort of patients on antiretroviral therapy

Wendy P Bannister, Alessandro Cozzi-Lepri, Jesper Kjær, Bonaventura Clotet, Adriano Lazzarin, Jean-Paul Viard, Gitte Kronborg, Dan Duiculescu, Marek Beniowski, Ladislav Machala, Andrew Phillips, EuroSIDA group

13 Citations (Scopus)

Abstract

Objectives: Estimating the prevalence of accumulated HIV drug resistance in patients receiving antiretroviral therapy (ART) is difficult due to lack of resistance testing at all occasions of virological failure and in patients with undetectable viral load. A method to estimate this for 6498 EuroSIDA patients who were under followup on ART at 1 July 2008 was therefore developed by imputing data on patients with no prior resistance test results, based on the probability of detecting resistance in tested patients with similar profiles. Methods: Using all resistance test results available, predicted intermediate/high-level resistance to specific drug classes [nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs)] was derived using the Stanford algorithm v5.1.2. Logistic regression models were then employed to estimate predicted probability of resistance to each drug class for given values of current viral load, history of virological failure and previous virological suppression. Based on these predicted probabilities and patients' covariate profiles, estimates of prevalence in 5355 patients with no prior test results were obtained. Overall prevalence of resistance was estimated by pooling these data with those observed in the remaining 1143 tested patients. Results: Prevalence of NRTI, NNRTI and PI resistance was estimated as 43% (95% confidence interval: 39%-46%), 15% (13%-18%) and 25% (22%-28%), respectively. Conclusions: This method provides estimates for the proportion of treated patients in a cohort who harbour resistance on a given date, which are less likely to be affected by selection bias due to missing resistance data and will allow us to estimate prevalence of resistance to different drug classes at specific timepoints in HIV-infected populations on ART.

Original language	English
Journal	Journal of Antimicrobial Chemotherapy
Volume	66
Issue number	4
Pages (from-to)	901-11
Number of pages	11
ISSN	0305-7453
DOIs	https://doi.org/10.1093/jac/dkr006
Publication status	Published - Apr 2011

Keywords

Adult
Anti-HIV Agents
Antiretroviral Therapy, Highly Active
Drug Resistance, Viral
Europe
Female
Genotype
HIV Infections
HIV-1
Humans
Male
Middle Aged
Mutation
Prevalence
RNA, Viral

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1093/jac/dkr006

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Bannister, W. P., Cozzi-Lepri, A., Kjær, J., Clotet, B., Lazzarin, A., Viard, J-P., Kronborg, G., Duiculescu, D., Beniowski, M., Machala, L., Phillips, A., & EuroSIDA group (2011). Estimating prevalence of accumulated HIV-1 drug resistance in a cohort of patients on antiretroviral therapy. Journal of Antimicrobial Chemotherapy, 66(4), 901-11. https://doi.org/10.1093/jac/dkr006

Bannister, WP, Cozzi-Lepri, A, Kjær, J, Clotet, B, Lazzarin, A, Viard, J-P, Kronborg, G, Duiculescu, D, Beniowski, M, Machala, L, Phillips, A & EuroSIDA group 2011, 'Estimating prevalence of accumulated HIV-1 drug resistance in a cohort of patients on antiretroviral therapy', Journal of Antimicrobial Chemotherapy, vol. 66, no. 4, pp. 901-11. https://doi.org/10.1093/jac/dkr006

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abstract = "Objectives: Estimating the prevalence of accumulated HIV drug resistance in patients receiving antiretroviral therapy (ART) is difficult due to lack of resistance testing at all occasions of virological failure and in patients with undetectable viral load. A method to estimate this for 6498 EuroSIDA patients who were under followup on ART at 1 July 2008 was therefore developed by imputing data on patients with no prior resistance test results, based on the probability of detecting resistance in tested patients with similar profiles. Methods: Using all resistance test results available, predicted intermediate/high-level resistance to specific drug classes [nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs)] was derived using the Stanford algorithm v5.1.2. Logistic regression models were then employed to estimate predicted probability of resistance to each drug class for given values of current viral load, history of virological failure and previous virological suppression. Based on these predicted probabilities and patients' covariate profiles, estimates of prevalence in 5355 patients with no prior test results were obtained. Overall prevalence of resistance was estimated by pooling these data with those observed in the remaining 1143 tested patients. Results: Prevalence of NRTI, NNRTI and PI resistance was estimated as 43% (95% confidence interval: 39%-46%), 15% (13%-18%) and 25% (22%-28%), respectively. Conclusions: This method provides estimates for the proportion of treated patients in a cohort who harbour resistance on a given date, which are less likely to be affected by selection bias due to missing resistance data and will allow us to estimate prevalence of resistance to different drug classes at specific timepoints in HIV-infected populations on ART.",

keywords = "Adult, Anti-HIV Agents, Antiretroviral Therapy, Highly Active, Drug Resistance, Viral, Europe, Female, Genotype, HIV Infections, HIV-1, Humans, Male, Middle Aged, Mutation, Prevalence, RNA, Viral",

author = "Bannister, {Wendy P} and Alessandro Cozzi-Lepri and Jesper Kj{\ae}r and Bonaventura Clotet and Adriano Lazzarin and Jean-Paul Viard and Gitte Kronborg and Dan Duiculescu and Marek Beniowski and Ladislav Machala and Andrew Phillips and {EuroSIDA group}",

year = "2011",

month = apr,

doi = "10.1093/jac/dkr006",

language = "English",

volume = "66",

pages = "901--11",

journal = "Journal of Antimicrobial Chemotherapy",

issn = "0305-7453",

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TY - JOUR

T1 - Estimating prevalence of accumulated HIV-1 drug resistance in a cohort of patients on antiretroviral therapy

AU - Bannister, Wendy P

AU - Cozzi-Lepri, Alessandro

AU - Kjær, Jesper

AU - Clotet, Bonaventura

AU - Lazzarin, Adriano

AU - Viard, Jean-Paul

AU - Kronborg, Gitte

AU - Duiculescu, Dan

AU - Beniowski, Marek

AU - Machala, Ladislav

AU - Phillips, Andrew

AU - EuroSIDA group

PY - 2011/4

Y1 - 2011/4

N2 - Objectives: Estimating the prevalence of accumulated HIV drug resistance in patients receiving antiretroviral therapy (ART) is difficult due to lack of resistance testing at all occasions of virological failure and in patients with undetectable viral load. A method to estimate this for 6498 EuroSIDA patients who were under followup on ART at 1 July 2008 was therefore developed by imputing data on patients with no prior resistance test results, based on the probability of detecting resistance in tested patients with similar profiles. Methods: Using all resistance test results available, predicted intermediate/high-level resistance to specific drug classes [nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs)] was derived using the Stanford algorithm v5.1.2. Logistic regression models were then employed to estimate predicted probability of resistance to each drug class for given values of current viral load, history of virological failure and previous virological suppression. Based on these predicted probabilities and patients' covariate profiles, estimates of prevalence in 5355 patients with no prior test results were obtained. Overall prevalence of resistance was estimated by pooling these data with those observed in the remaining 1143 tested patients. Results: Prevalence of NRTI, NNRTI and PI resistance was estimated as 43% (95% confidence interval: 39%-46%), 15% (13%-18%) and 25% (22%-28%), respectively. Conclusions: This method provides estimates for the proportion of treated patients in a cohort who harbour resistance on a given date, which are less likely to be affected by selection bias due to missing resistance data and will allow us to estimate prevalence of resistance to different drug classes at specific timepoints in HIV-infected populations on ART.

AB - Objectives: Estimating the prevalence of accumulated HIV drug resistance in patients receiving antiretroviral therapy (ART) is difficult due to lack of resistance testing at all occasions of virological failure and in patients with undetectable viral load. A method to estimate this for 6498 EuroSIDA patients who were under followup on ART at 1 July 2008 was therefore developed by imputing data on patients with no prior resistance test results, based on the probability of detecting resistance in tested patients with similar profiles. Methods: Using all resistance test results available, predicted intermediate/high-level resistance to specific drug classes [nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs)] was derived using the Stanford algorithm v5.1.2. Logistic regression models were then employed to estimate predicted probability of resistance to each drug class for given values of current viral load, history of virological failure and previous virological suppression. Based on these predicted probabilities and patients' covariate profiles, estimates of prevalence in 5355 patients with no prior test results were obtained. Overall prevalence of resistance was estimated by pooling these data with those observed in the remaining 1143 tested patients. Results: Prevalence of NRTI, NNRTI and PI resistance was estimated as 43% (95% confidence interval: 39%-46%), 15% (13%-18%) and 25% (22%-28%), respectively. Conclusions: This method provides estimates for the proportion of treated patients in a cohort who harbour resistance on a given date, which are less likely to be affected by selection bias due to missing resistance data and will allow us to estimate prevalence of resistance to different drug classes at specific timepoints in HIV-infected populations on ART.

KW - Adult

KW - Anti-HIV Agents

KW - Antiretroviral Therapy, Highly Active

KW - Drug Resistance, Viral

KW - Europe

KW - Female

KW - Genotype

KW - HIV Infections

KW - HIV-1

KW - Humans

KW - Male

KW - Middle Aged

KW - Mutation

KW - Prevalence

KW - RNA, Viral

U2 - 10.1093/jac/dkr006

DO - 10.1093/jac/dkr006

M3 - Journal article

C2 - 21393179

SN - 0305-7453

VL - 66

SP - 901

EP - 911

JO - Journal of Antimicrobial Chemotherapy

JF - Journal of Antimicrobial Chemotherapy

IS - 4

ER -

Estimating prevalence of accumulated HIV-1 drug resistance in a cohort of patients on antiretroviral therapy

Abstract

Keywords

UN SDGs

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