Timeliness of Clinic Attendance Is a Good Predictor of Virological Response and Resistance to Antiretroviral Drugs in HIV-Infected Patients (original) (raw)
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AIDS and Behavior, 2016
Questions remain regarding optimal timeframes for asking about adherence in clinical care. We compared 4-, 7-, 14-, 30-, and 60-day timeframe missed dose items with viral load levels among 1099 patients on antiretroviral therapy in routine care. We conducted logistic and linear regression analyses examining associations between different timeframes and viral load using Bayesian Model Averaging (BMA). We conducted sensitivity analyses with subgroups at increased risk for suboptimal adherence (e.g patients with depression, substance use). The 14-day timeframe had the largest mean difference in adherence levels among those with detectable and undetectable viral loads. BMA estimates suggested the 14-day timeframe was strongest overall and for most subgroups although findings differed somewhat for hazardous alcohol users and those with current depression. Adherence measured by all missed dose timeframes correlated with viral load. Adherence calculated from intermediate timeframes (e.g. 14-day) appeared best able to capture adherence behavior as measured by viral load.
AIDS Research and Therapy, 2008
Background: Newer antiretroviral (ARV) agents have improved pharmacokinetics, potency, and tolerability and have enabled the design of regimens with improved virologic outcomes. Successful antiretroviral therapy is dependent on patient adherence. In previous research, we validated a subset of items from the ACTG adherence battery as prognostic of virologic suppression at 6 months and correlated with adherence estimates from the Medication Event Monitoring System (MEMS). The objective of the current study was to validate the longitudinal use of the Owen Clinic adherence index in analyses of time to initial virologic suppression and maintenance of suppression.
Tropical Medicine & International Health, 2016
objective Combination antiretroviral therapy (cART) suppresses viral replication to an undetectable level if a sufficiently high level of adherence is achieved. We investigated which adherence measurement best distinguishes between patients with and without detectable viral load in a public ART programme without routine plasma viral load monitoring. method We randomly selected 870 patients who started cART between May 2009 and April 2012 in 10 healthcare facilities in Addis Ababa, Ethiopia. Six hundred and sixty-four (76.3%) patients who were retained in HIV care and were receiving cART for at least 6 months were included and 642 had their plasma HIV-1 RNA concentration measured. Patients' adherence to cART was assessed according to self-report, clinician recorded and pharmacy refill measures. Multivariate logistic regression model was fitted to identify the predictors of detectable viremia. Model accuracy was evaluated by computing the area under the receiver operating characteristic (ROC) curve. result A total of 9.2% and 5.5% of the 642 patients had a detectable viral load of ≥40 and ≥400 RNA copies/ml, respectively. In the multivariate analyses, younger age, lower CD4 cell count at cART initiation, being illiterate and widowed, and each of the adherence measures were significantly and independently predictive of having ≥400 RNA copies/ml. The ROC curve showed that these variables altogether had a likelihood of more than 80% to distinguish patients with a plasma viral load of ≥400 RNA copies/ml from those without. conclusion Adherence to cART was remarkably high. Self-report, clinician recorded and pharmacy refill non-adherence were all significantly predictive of detectable viremia. The choice for one of these methods to detect non-adherence and predict a detectable viral load can therefore be based on what is most practical in a particular setting.
Aids Research and Human Retroviruses, 2008
We evaluated the association between two antiretroviral therapy (ART) adherence measurements-the medication possession ratio (MPR) and patient self-report-and detectable HIV viremia in the setting of rapid service scale-up in Lusaka, Zambia. Drug adherence and outcomes were assessed in a subset of patients suspected of treatment failure based on discordant clinical and immunologic responses to ART. A total of 913 patients were included in this analysis, with a median time of 744 days (Q1, Q3: 511, 919 days) from ART initiation to viral load (VL) measurement. On aggregate over the period of follow-up, 531 (58%) had optimal adherence (MPR Ն95%), 306 (34%) had suboptimal adherence (MPR 80-94%), and 76 (8%) had poor adherence (MPR Ͻ80%). Of the 913 patients, 238 (26%) had VL Ն400 copies/ml when tested. When compared to individuals with optimal adherence, there was increasing risk for virologic failure in those with suboptimal adherence [adjusted relative risk (ARR): 1.3; 95% confidence interval (CI): 1.0, 1.6] and those with poor adherence (ARR: 1.7; 95% CI: 1.3, 2.4) based on MPR. During the antiretroviral treatment course, 676 patients (74%) reported no missed doses. The proportion of patients with virologic failure did not differ significantly among those reporting any missed dose from those reporting perfect adherence (26% vs. 26%, p ϭ 0.97). Among patients with suspected treatment failure, a lower MPR was associated with higher rates of detectable viremia. However, the suboptimal sensitivity and specificity of MPR limit its utility as a sole predictor of virologic failure.
AIDS research and therapy, 2017
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. 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. At...
Predictors of short-term success of antiretroviral therapy in HIV infection
Journal of Antimicrobial Chemotherapy, 2006
Objectives: The success of highly active antiretroviral therapy (HAART) in HIV infection may be influenced by numerous host factors. There is a lack of data presenting a combined assessment of a variety of these parameters for treatment efficacy in clinical routine practice.
2011 Suboptimal adherence, viral failure and drug resistance, Int Health 2011
This study was conducted to examine the relationship between adherence, viral load (VL) and resistance among outpatients receiving highly active antiretroviral therapy (HAART) in Bangalore, India. In total, 552 outpatients were recruited and VL testing was conducted for all study participants. HIV-1 genotypic resistance testing was performed for 92 participants with a VL > 1000 copies/ml. Interpretation of resistance mutations was performed according to the Stanford database. Past-month adherence and treatment interruptions for >48 h were assessed via self-report. At baseline, 34 participants (6%) reported <95% past-month adherence and 110 (20%) reported a history of >48 h treatment interruptions. Combining the two adherence measures, 22% of participants were classified as 'suboptimally adherent'. In total, 24% of study participants (n = 132) had a detectable VL. Among the 92 samples sent for resistance testing, 68% had at least one nucleoside reverse transcriptase inhibitor (NRTI) mutation, with M184 V being the most common (65%) and with 48% having thymidine analogue mutations. Moreover, 72% had at least one non-nucleoside reverse transcriptase inhibitor (NNRTI) mutation and 23% had three or more NNRTI mutations. Both adherence measures were significantly associated with VL (P < 0.001). Suboptimal adherence was significantly associated with resistance mutations (P < 0.02). The findings illustrate for the first time the strong association between suboptimal adherence, treatment failure and drug resistance to first-line HAART in India. The predictive value of standard adherence measures was improved by including treatment interruption data. The observed mutations can jeopardise future treatment options, especially in light of limited access to second-line treatments. To develop effective adherence interventions, research is needed to examine culturally-specific reasons for treatment interruptions.
An alternative methodology for the prediction of adherence to anti HIV treatment
AIDS Research and Therapy, 2009
Background: Successful treatment of HIV-positive patients is fundamental to controlling the progression to AIDS. Causes of treatment failure are either related to drug resistance and/or insufficient drug levels in the blood. Severe side effects, coupled with the intense nature of many regimens, can lead to treatment fatigue and consequently to periodic or permanent non-adherence. Although non-adherence is a recognised problem in HIV treatment, it is still poorly detected in both clinical practice and research and often based on unreliable information such as self-reports, or in a research setting, Medication Events Monitoring System caps or prescription refill rates. To meet the need for having objective information on adherence, we propose a method using viral load and HIV genome sequence data to identify non-adherence amongst patients.
AIDS and Behavior, 2014
Although the majority of HIV-infected patients who begin potent antiretroviral therapy should expect longterm virologic suppression, the realities in practice are less certain. Durability of viral suppression was examined to define the best timing of targeted adherence strategies and intensive viral load monitoring in an urban clinic population with multiple challenges to ART adherence. We examined the risk of viral rebound for patients who achieved two consecutive viral loads lower than the lower limit of quantification (LLOQ) within 390 days. For 791 patients with two viral loads below the LLOQ, viral rebound [LLOQ from the first viral load was 36.9 % (95 % CI 32.2-41.6) in the first year, 26.9 % (95 % CI 21.7-32.1) in the year following one year of viral suppression, in the year following 2 years of viral suppression. However, for patients with CD4 C300 cells/ll who had 3-6 years of virologic suppression, the risk of viral rebound was very low. At the population level, the risk of viral rebound in a complex urban clinic population is surprisingly high even out to 3 years. Intensified monitoring and adherence efforts should target this high risk period. Thereafter, confidence in truly durable virologic suppression is improved.