Comparison of six methods to estimate adherence in an ART-naïve cohort in a resource-poor setting: which best predicts virological and resistance outcomes?

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dc.contributor.authorOrrell, Catherine
dc.contributor.authorCohen, Karen
dc.contributor.authorLeisegang, Rory
dc.contributor.authorBangsberg, David R
dc.contributor.authorWood, Robin
dc.contributor.authorMaartens, Gary
dc.date.accessioned2021-10-08T11:00:53Z
dc.date.available2021-10-08T11:00:53Z
dc.date.issued2017
dc.description.abstractBACKGROUND: Incomplete adherence to antiretroviral therapy (ART) results in virologic failure and resistance. It remains unclear which adherence measure best predicts these outcomes. We compared six patient-reported and objective adherence measures in one ART-naïve cohort in South Africa. METHODS: We recruited 230 participants from a community ART clinic and prospectively collected demographic data, CD4 count and HIV-RNA at weeks 0, 16 and 48. We quantified adherence using 3-day self-report (SR), clinic-based pill count (CPC), average adherence by pharmacy refill (PR-average), calculation of medication-free days (PR-gaps), efavirenz therapeutic drug monitoring (TDM) and an electronic adherence monitoring device (EAMD). Associations between adherence measures and virologic and genotypic outcomes were modelled using logistic regression, with the area under the curve (AUC) from the receiver operator characteristic (ROC) analyses derived to assess performance of adherence measures in predicting outcomes. RESULTS: At week 48 median (IQR) adherence was: SR 100% (100-100), CPC 100% (95-107), PR-average 103% (95-105), PR-gaps 100% (95-100) and EAMD 86% (59-94), and efavirenz concentrations were therapeutic (>1 mg/L) in 92%. EAMD, PR-average, PR-gaps and CPC best predicted virological outcome at week 48 with AUC ROC of 0.73 (95% CI 0.61-0.83), 0.73 (95% CI 0.61-0.85), 0.72 (95% CI 0.59-0.84) and 0.64 (95% CI 0.52-0.76) respectively. EAMD, PR-gaps and PR-average were highly predictive of detection of resistance mutations at week 48, with AUC ROC of 0.92 (95% CI 0.87-0.97), 0.86 (0.67-1.0) and 0.83 (95% CI 0.65-1.0) respectively. SR and TDM were poorly predictive of outcomes at week 48. CONCLUSION: EAMD and both PR measures predicted resistance and virological failure similarly. Pharmacy refill data is a pragmatic adherence measure in resource-limited settings where electronic monitoring is unavailable. Trial registration The trial was retrospectively registered in the Pan African Clinical Trials Registry, number PACTR201311000641402, on the 13 Sep 2013 ( www.pactr.org ). The first participant was enrolled on the 12th July 2012. The last patient last visit (week 48) was 15 April 2014.
dc.identifier.apacitationOrrell, C., Cohen, K., Leisegang, R., Bangsberg, D. R., Wood, R., & Maartens, G. (2017). Comparison of six methods to estimate adherence in an ART-naïve cohort in a resource-poor setting: which best predicts virological and resistance outcomes?. <i>AIDS Research and Therapy</i>, 14(1), 174 - 177. http://hdl.handle.net/11427/35042en_ZA
dc.identifier.chicagocitationOrrell, Catherine, Karen Cohen, Rory Leisegang, David R Bangsberg, Robin Wood, and Gary Maartens "Comparison of six methods to estimate adherence in an ART-naïve cohort in a resource-poor setting: which best predicts virological and resistance outcomes?." <i>AIDS Research and Therapy</i> 14, 1. (2017): 174 - 177. http://hdl.handle.net/11427/35042en_ZA
dc.identifier.citationOrrell, C., Cohen, K., Leisegang, R., Bangsberg, D.R., Wood, R. & Maartens, G. 2017. Comparison of six methods to estimate adherence in an ART-naïve cohort in a resource-poor setting: which best predicts virological and resistance outcomes?. <i>AIDS Research and Therapy.</i> 14(1):174 - 177. http://hdl.handle.net/11427/35042en_ZA
dc.identifier.issn1742-6405
dc.identifier.ris TY - Journal Article AU - Orrell, Catherine AU - Cohen, Karen AU - Leisegang, Rory AU - Bangsberg, David R AU - Wood, Robin AU - Maartens, Gary AB - BACKGROUND: Incomplete adherence to antiretroviral therapy (ART) results in virologic failure and resistance. It remains unclear which adherence measure best predicts these outcomes. We compared six patient-reported and objective adherence measures in one ART-naïve cohort in South Africa. METHODS: We recruited 230 participants from a community ART clinic and prospectively collected demographic data, CD4 count and HIV-RNA at weeks 0, 16 and 48. We quantified adherence using 3-day self-report (SR), clinic-based pill count (CPC), average adherence by pharmacy refill (PR-average), calculation of medication-free days (PR-gaps), efavirenz therapeutic drug monitoring (TDM) and an electronic adherence monitoring device (EAMD). Associations between adherence measures and virologic and genotypic outcomes were modelled using logistic regression, with the area under the curve (AUC) from the receiver operator characteristic (ROC) analyses derived to assess performance of adherence measures in predicting outcomes. RESULTS: At week 48 median (IQR) adherence was: SR 100% (100-100), CPC 100% (95-107), PR-average 103% (95-105), PR-gaps 100% (95-100) and EAMD 86% (59-94), and efavirenz concentrations were therapeutic (>1 mg/L) in 92%. EAMD, PR-average, PR-gaps and CPC best predicted virological outcome at week 48 with AUC ROC of 0.73 (95% CI 0.61-0.83), 0.73 (95% CI 0.61-0.85), 0.72 (95% CI 0.59-0.84) and 0.64 (95% CI 0.52-0.76) respectively. EAMD, PR-gaps and PR-average were highly predictive of detection of resistance mutations at week 48, with AUC ROC of 0.92 (95% CI 0.87-0.97), 0.86 (0.67-1.0) and 0.83 (95% CI 0.65-1.0) respectively. SR and TDM were poorly predictive of outcomes at week 48. CONCLUSION: EAMD and both PR measures predicted resistance and virological failure similarly. Pharmacy refill data is a pragmatic adherence measure in resource-limited settings where electronic monitoring is unavailable. Trial registration The trial was retrospectively registered in the Pan African Clinical Trials Registry, number PACTR201311000641402, on the 13 Sep 2013 ( www.pactr.org ). The first participant was enrolled on the 12th July 2012. The last patient last visit (week 48) was 15 April 2014. DA - 2017 DB - OpenUCT DP - University of Cape Town IS - 1 J1 - AIDS Research and Therapy LK - https://open.uct.ac.za PY - 2017 SM - 1742-6405 T1 - Comparison of six methods to estimate adherence in an ART-naïve cohort in a resource-poor setting: which best predicts virological and resistance outcomes? TI - Comparison of six methods to estimate adherence in an ART-naïve cohort in a resource-poor setting: which best predicts virological and resistance outcomes? UR - http://hdl.handle.net/11427/35042 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/35042
dc.identifier.vancouvercitationOrrell C, Cohen K, Leisegang R, Bangsberg DR, Wood R, Maartens G. Comparison of six methods to estimate adherence in an ART-naïve cohort in a resource-poor setting: which best predicts virological and resistance outcomes?. AIDS Research and Therapy. 2017;14(1):174 - 177. http://hdl.handle.net/11427/35042.en_ZA
dc.language.isoeng
dc.publisher.departmentCentre for Infectious Disease Epidemiology and Research
dc.publisher.facultyFaculty of Health Sciences
dc.sourceAIDS Research and Therapy
dc.source.journalissue1
dc.source.journalvolume14
dc.source.pagination174 - 177
dc.source.urihttps://dx.doi.org/10.1186/s12981-017-0138-y
dc.subject.otherHIV
dc.subject.otherAntiretroviral therapy
dc.subject.otherAdherence
dc.subject.otherElectronic monitoring
dc.subject.otherVirological outcome
dc.subject.otherHIV-1 resistance
dc.subject.otherGenotyping
dc.subject.otherJournal Article
dc.titleComparison of six methods to estimate adherence in an ART-naïve cohort in a resource-poor setting: which best predicts virological and resistance outcomes?
dc.typeJournal Article
uct.type.publicationResearch
uct.type.resourceJournal Article
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