Adherence to antiretroviral therapy and its impact on clinical outcome in HIV-infected patients

Abstract

We analyse data on patient adherence to prescribed regimens and surrogate markers of clinical outcome for 168 human immunodeficiency virus infected patients treated with antiretroviral therapy. Data on patient adherence consisted of dose-timing measurements collected for an average of 12 months per patient via electronic monitoring of bottle opening events. We first discuss how such data can be presented to highlight suboptimal adherence patterns and between-patient differences, before introducing two novel methods by which such data can be statistically modelled. Correlations between adherence and subsequent measures of viral load and CD4 C T-cell counts are then evaluated. We show that summary measures of short-term adherence, which incorporate pharmacokinetic and pharmacodynamic data on the monitored regimen, predict suboptimal trends in viral load and CD4 C T-cell counts better than measures based on adherence data alone.

Assessing the impact of adherence to anti-retroviral therapy on treatment failure and resistance evolution in HIV

“…This combination reflects the widespread use of two reverse transcriptase inhibitors, together with a protease inhibitor [15]. The inhibitory effect on a specific genotype 1 gt was calculated using a formulation for multiple synergistically and non-synergistically acting drugs [25].…”

Viral dynamics of an HIV model with pulse antiretroviral therapy and adherence

“…Theorem 2.1. The solutions of the hybrid system (2.1) and (2.2), with the initial value (T S0 , T R0 , T I0 , V I0 , R(0)) ∈ R 5 + are uniformly and ultimately bounded. Furthermore, the compact set J = {(T S , T R , T I , V I ) ∈ R 4 + , (T S , T R , T I , V I ) (π, π, π, π)} is positively invariant and attracts all positive orbits in R 4 + .…”

Adherence Profiles and Therapeutic Responses of Treatment-Naive HIV-Infected Patients Starting Boosted Atazanavir-Based Therapy in the ANRS 134-COPHAR 3 Trial

“…The agreement between the methods for discriminating adherence of Ͼ95% during similar periods was calculated using Cohen's kappa coefficient. The longitudinal data with repeated measurements were analyzed using generalized linear mixed models (22,23). For the continuous outcomes, such as adherence, we used the MIXED procedure in SAS with the same 5 periods defined for DVS.…”