Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring

J Acquir Immune Defic Syndr. 2015 May 1;69(1):109-18. doi: 10.1097/QAI.0000000000000548.


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 US cohorts contributing to the MACH14 consortium. Because 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 receiver operating characteristic curve, evaluated on data not used in model fitting, was 0.78 (95% confidence interval: 0.75 to 0.80) and 0.79 (95% confidence interval: 0.76 to 0.81) for failure defined as single HIV RNA level >1000 copies per milliliter or >400 copies per milliliter, respectively. Our results suggest that 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.

Publication types

  • Multicenter Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Anti-HIV Agents / therapeutic use*
  • Artificial Intelligence*
  • Cohort Studies
  • Decision Support Techniques*
  • Drug Monitoring / methods
  • Female
  • HIV Infections / drug therapy*
  • Humans
  • Longitudinal Studies
  • Male
  • Medication Adherence / statistics & numerical data*
  • Middle Aged
  • Prospective Studies
  • RNA, Viral / blood*
  • Treatment Failure


  • Anti-HIV Agents
  • RNA, Viral

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