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      Predicting viral load suppression by self-reported adherence, pharmacy refill counts and real time medication monitoring among people living with HIV in Tanzania

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          Abstract

          Introduction

          Monitoring of adherence to antiretroviral treatment (ART) is of utmost importance to prevent treatment failure. Several measures to monitor adherence have been applied in low-resource settings and they all have pros and cons. Our objective was to examine whether any of the following adherence measures is a better predictor of participants’ viral load suppression: (1) self-report, (2) pharmacy refill count, (3) Real Time Medication Monitoring (RTMM), (4) a combination of self-report and pharmacy refill count or (5) all three adherence assessment methods combined.

          Methodology

          This was a post-hoc analysis of data from our 48-week REMIND-HIV randomized controlled trial in which adherence to ART was measured using self-report, pharmacy refill counts and RTMM among ART-experienced adults living with HIV subjectively judged to be nonadherent to ART. For each adherence measure, we calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for predicting virological failure defined as a viral load (VL) of > 20 copies/mL. To determine at which percentage of adherence the prediction was strongest, we evaluated adherence cut-offs of 80%, 85%, 90%, 95% and 100% using receiver operating characteristic (ROC) curves. VL data were obtained after 48 weeks of follow-up in the trial.

          Results

          A total of 233 people living with HIV (PLHIV) were included in this analysis. When comparing the ability of self-reported adherence with pharmacy refill count and RTMM adherence to predict viral load > 20 copies/ml, self-reported adherence had the lowest sensitivity, ranging from 6 to 17%, but the highest specificity, ranging from 100 to 86%, depending on cut-off values from 80 to 100%. Area under the ROC curves (AUC) were 0.54 for RTMM, 0.56 for pharmacy refill count and 0.52 for self-report, indicating low discriminatory capacity for each of the adherence measures. When we combined the self-report and pharmacy refill count measures, sensitivity increased, ranging from 28 to 57% but specificity decreased, ranging from 83 to 53%. When all three measures were combined, we observed the highest value of sensitivity, ranging from 46 to 92%, and PPV, ranging from 32 to 36%, at high cut-offs ranging from 80 to 100%. Upon combination of three adherence measures, the AUC increased to 0.59.

          Conclusion

          Our results show that adherence assessed exclusively by self-report, pharmacy refill count or RTMM were insufficiently sensitive to predict virologic failure. Sensitivity markedly improved by combining all three measures, but the practical feasibility of such an approach would need to be studied.

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          Most cited references40

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            Adherence to protease inhibitor therapy and outcomes in patients with HIV infection.

            Combination antiretroviral therapy with protease inhibitors has transformed HIV infection from a terminal condition into one that is manageable. However, the complexity of regimens makes adherence to therapy difficult. To assess the effects of different levels of adherence to therapy on virologic, immunologic, and clinical outcome; to determine modifiable conditions associated with suboptimal adherence; and to determine how well clinicians predict patient adherence. Prospective, observational study. HIV clinics in a Veterans Affairs medical center and a university medical center. 99 HIV-infected patients who were prescribed a protease inhibitor and who neither used a medication organizer nor received their medications in an observed setting (such as a jail or nursing home). Adherence was measured by using a microelectronic monitoring system. The adherence rate was calculated as the number of doses taken divided by the number prescribed. Patients were followed for a median of 6 months (range, 3 to 15 months). During the study period, 45,397 doses of protease inhibitor were monitored in 81 evaluable patients. Adherence was significantly associated with successful virologic outcome (P < 0.001) and increase in CD4 lymphocyte count (P = 0.006). Virologic failure was documented in 22% of patients with adherence of 95% or greater, 61% of those with 80% to 94.9% adherence, and 80% of those with less than 80% adherence. Patients with adherence of 95% or greater had fewer days in the hospital (2.6 days per 1000 days of follow-up) than those with less than 95% adherence (12.9 days per 1000 days of follow-up; P = 0.001). No opportunistic infections or deaths occurred in patients with 95% or greater adherence. Active psychiatric illness was an independent risk factor for adherence less than 95% (P = 0.04). Physicians predicted adherence incorrectly for 41% of patients, and clinic nurses predicted it incorrectly for 30% of patients. Adherence to protease inhibitor therapy of 95% or greater optimized virologic outcome for patients with HIV infection. Diagnosis and treatment of psychiatric illness should be further investigated as a means to improve adherence to therapy.
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              Self-report measures of medication adherence behavior: recommendations on optimal use.

              Medication adherence plays an important role in optimizing the outcomes of many treatment and preventive regimens in chronic illness. Self-report is the most common method for assessing adherence behavior in research and clinical care, but there are questions about its validity and precision. The NIH Adherence Network assembled a panel of adherence research experts working across various chronic illnesses to review self-report medication adherence measures and research on their validity. Self-report medication adherence measures vary substantially in their question phrasing, recall periods, and response items. Self-reports tend to overestimate adherence behavior compared with other assessment methods and generally have high specificity but low sensitivity. Most evidence indicates that self-report adherence measures show moderate correspondence to other adherence measures and can significantly predict clinical outcomes. The quality of self-report adherence measures may be enhanced through efforts to use validated scales, assess the proper construct, improve estimation, facilitate recall, reduce social desirability bias, and employ technologic delivery. Self-report medication adherence measures can provide actionable information despite their limitations. They are preferred when speed, efficiency, and low-cost measures are required, as is often the case in clinical care.
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                Author and article information

                Contributors
                k.ngowi@kcri.ac.tz
                Journal
                AIDS Res Ther
                AIDS Res Ther
                AIDS Research and Therapy
                BioMed Central (London )
                1742-6405
                15 November 2022
                15 November 2022
                2022
                : 19
                : 51
                Affiliations
                [1 ]GRID grid.412898.e, ISNI 0000 0004 0648 0439, Kilimanjaro Clinical Research Institute, ; Moshi, United Republic of Tanzania
                [2 ]GRID grid.7177.6, ISNI 0000000084992262, Department of Medical Psychology, , Amsterdam University Medical Center, University of Amsterdam, ; Amsterdam, The Netherlands
                [3 ]GRID grid.450091.9, ISNI 0000 0004 4655 0462, Amsterdam Institute for Global Health and Development, ; Amsterdam, the Netherlands
                [4 ]GRID grid.10417.33, ISNI 0000 0004 0444 9382, Radboudumc, Radboud Institute for Health Sciences and Department of Pharmacy, ; Nijmegen, The Netherlands
                [5 ]GRID grid.415218.b, ISNI 0000 0004 0648 072X, Kilimanjaro Christian Medical Centre, ; Moshi, United Republic of Tanzania
                [6 ]GRID grid.412898.e, ISNI 0000 0004 0648 0439, Kilimanjaro Christian Medical University College, ; Moshi, United Republic of Tanzania
                [7 ]GRID grid.7177.6, ISNI 0000000084992262, Department of Global Health, and Amsterdam Institute for Global Health and Development Amsterdam UMC, , University of Amsterdam, ; Amsterdam, The Netherlands
                Article
                475
                10.1186/s12981-022-00475-y
                9664713
                36380383
                ea91799a-addf-487f-9317-72b1e8e56666
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 15 April 2022
                : 7 October 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001713, European and Developing Countries Clinical Trials Partnership;
                Award ID: CDF972
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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