Published In

Journal of Acquired Immune Deficiency Syndromes

Document Type


Publication Date



HIV infections -- Treatment, Antiretroviral therapy, Medical informatics, Patient compliance -- Effect of behavior-driven testing on

Physical Description

20 pages


Objective—Regular HIV RNA testing for all HIV positive patients on antiretroviral therapy (ART) is expensive and has low yield since most tests are undetectable. Selective testing of those at higher risk of failure may improve efficiency. We investigated whether a novel analysis of adherence data could correctly classify virological failure and potentially inform a selective testing strategy.

Design—Multisite prospective cohort consortium.

Methods—We evaluated longitudinal data on 1478 adult patients treated with ART and monitored using the Medication Event Monitoring System (MEMS) in 16 United States cohorts contributing to the MACH14 consortium. Since the relationship between adherence and virological failure is complex and heterogeneous, we applied a machine-learning algorithm (Super Learner) to build a model for classifying failure and evaluated its performance using cross-validation.

Results—Application of the Super Learner algorithm to MEMS data, combined with data on CD4+ T cell counts and ART regimen, significantly improved classification of virological failure over a single MEMS adherence measure. Area under the ROC curve, evaluated on data not used in model fitting, was 0.78 (95% CI: 0.75, 0.80) and 0.79 (95% CI: 0.76, 0.81) for failure defined as single HIV RNA level >1000 copies/ml or >400 copies/ml, respectively. Our results suggest 25–31% of viral load tests could be avoided while maintaining sensitivity for failure detection at or above 95%, for a cost savings of $16–$29 per person-month.

Conclusions—Our findings provide initial proof-of-concept for the potential use of electronic medication adherence data to reduce costs through behavior-driven HIV RNA testing.


Author's version of an article that subsequently appeared in Journal of Acquired Immune Deficiency Syndromes, 2015 May 1; 69(1): 109–118. doi:10.1097/QAI.0000000000000548. Published by Lippincott, Williams & Wilkins.

At the time of writing, David Bangsberg was affiliated with Massachusetts General Hospital, Center for Global Health; Harvard School of Public Health, Department of Global Health and Population.



Persistent Identifier


Lippincott, Williams & Wilkins