Document Type

Journal Article

Date of this Version

5-20-2008

Publication Source

PLoS Medicine

Volume

5

Issue

5

Start Page

e109

DOI

10.1371/journal.pmed.0050109

Abstract

Background

World Health Organization (WHO) guidelines for monitoring HIV-infected individuals taking combination antiretroviral therapy (cART) in resource-limited settings recommend using CD4+ T cell (CD4) count changes to monitor treatment effectiveness. In practice, however, falling CD4 counts are a consequence, rather than a cause, of virologic failure. Adherence lapses precede virologic failure and, unlike CD4 counts, data on adherence are immediately available to all clinics dispensing cART. However, the accuracy of adherence assessments for predicting future or detecting current virologic failure has not been determined. The goal of this study therefore was to determine the accuracy of adherence assessments for predicting and detecting virologic failure and to compare the accuracy of adherence-based monitoring approaches with approaches monitoring CD4 count changes.

Methodology and Findings

We conducted an observational cohort study among 1,982 of 4,984 (40%) HIV-infected adults initiating non-nucleoside reverse transcriptase inhibitor-based cART in the Aid for AIDS Disease Management Program, which serves nine countries in southern Africa. Pharmacy refill adherence was calculated as the number of months of cART claims submitted divided by the number of complete months between cART initiation and the last refill prior to the endpoint of interest, expressed as a percentage. The main outcome measure was virologic failure defined as a viral load > 1,000 copies/ml (1) at an initial assessment either 6 or 12 mo after cART initiation and (2) after a previous undetectable (i.e., < 400 copies/ml) viral load (breakthrough viremia). Adherence levels outperformed CD4 count changes when used to detect current virologic failure in the first year after cART initiation (area under the receiver operating characteristic [ROC] curves [AUC] were 0.79 and 0.68 [difference = 0.11; 95% CI 0.06 to 0.16; χ2 = 20.1] respectively at 6 mo, and 0.85 and 0.75 [difference = 0.10; 95% CI 0.05 to 0.14; χ2 = 20.2] respectively at 12 mo; p < 0.001 for both comparisons). When used to detect current breakthrough viremia, adherence and CD4 counts were equally accurate (AUCs of 0.68 versus 0.67, respectively [difference = 0.01; 95% CI −0.06 to 0.07]; χ2 = 0.1, p > 0.5). In addition, adherence levels assessed 3 mo prior to viral load assessments were as accurate for virologic failure occurring approximately 3 mo later as were CD4 count changes calculated from cART initiation to the actual time of the viral load assessments, indicating the potential utility of adherence assessments for predicting future, rather than simply detecting current, virologic failure. Moreover, combinations of CD4 count and adherence data appeared useful in identifying patients at very low risk of virologic failure.

Conclusions

Pharmacy refill adherence assessments were as accurate as CD4 counts for detecting current virologic failure in this cohort of patients on cART and have the potential to predict virologic failure before it occurs. Approaches to cART scale-up in resource-limited settings should include an adherence-based monitoring approach.

Copyright/Permission Statement

© 2008 Bisson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Keywords

AUC, area(s) under the ROC curve, cART, combination antiretroviral therapy, CI, confidence interval, IQR, interquartile range, NNRTI, non-nucleoside reverse transcriptase inhibitor, NPV, negative predictive value, OR, odds ratio, PPV, positive predictive value, ROC, receiver operating characteristic, WHO, World Health Organization

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Date Posted: 02 December 2014

This document has been peer reviewed.