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Predicting short-term interruptions of antiretroviral therapy from summary adherence data: Development and test of a probability model.

Predicting short-term interruptions of antiretroviral therapy from summary adherence data: Development and test of a probability model.
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Harris RA, Haberer JE, Musinguzi N, Chang KM, Schechter CB, Doubeni CA, Gross R,


Harris RA, Haberer JE, Musinguzi N, Chang KM, Schechter CB, Doubeni CA, Gross R, (click to view)

Harris RA, Haberer JE, Musinguzi N, Chang KM, Schechter CB, Doubeni CA, Gross R,

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PloS one 2018 03 2213(3) e0194713 doi 10.1371/journal.pone.0194713

Abstract

Antiretroviral therapy (ART) for HIV is vulnerable to unplanned treatment interruptions-consecutively missed doses over a series of days-which can result in virologic rebound. Yet clinicians lack a simple, valid method for estimating the risk of interruptions. If the likelihood of ART interruption could be derived from a convenient-to-gather summary measure of medication adherence, it might be a valuable tool for both clinical decision-making and research. We constructed an a priori probability model of ART interruption based on average adherence and tested its predictions using data collected on 185 HIV-infected, treatment-naïve individuals over the first 90 days of ART in a prospective cohort study in Mbarara, Uganda. The outcome of interest was the presence or absence of a treatment gap, defined as >72 hours without a dose. Using the pre-determined value of 0.50 probability as the cut point for predicting an interruption, the classification accuracy of the model was 73% (95% CI = 66%- 79%), the specificity was 87% (95% CI = 79%- 93%), and the sensitivity was 59% (95% CI = 48%- 69%). Overall model performance was satisfactory, with an area under the receiver operator characteristic curve (AUROC) of 0.85 (95% CI = 0.80-0.91) and Brier score of 0.20. The study serves as proof-of-concept that the probability model can accurately differentiate patients on the continuum of risk for short-term ART interruptions using a summary measure of adherence. The model may also aid in the design of targeted interventions.

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