Super learner analysis of electronic adherence data improves viral prediction and may provide strategies for selective HIV RNA monitoring

Maya L. Petersen, Erin LeDell, Joshua Schwab, Varada Sarovar, Robert Gross, Nancy Reynolds, Jessica E. Haberer, Kathy Goggin, Carol Golin, Julia Arnsten, Marc I. Rosen, Robert H. Remien, David Etoori, Ira B. Wilson, Jane M. Simoni, Judith A. Erlen, Mark J. Van Der Laan, Honghu Liu, David R. Bangsberg

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)109-118
Number of pages10
JournalJournal of Acquired Immune Deficiency Syndromes
Volume69
Issue number1
DOIs
StatePublished - May 1 2015

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Keywords

  • HIV
  • HIV RNA monitoring
  • Medication Event Monitoring System
  • Super Learner
  • adherence
  • antiretroviral therapy
  • virological failure

ASJC Scopus subject areas

  • Infectious Diseases
  • Pharmacology (medical)

Cite this

Petersen, M. L., LeDell, E., Schwab, J., Sarovar, V., Gross, R., Reynolds, N., Haberer, J. E., Goggin, K., Golin, C., Arnsten, J., Rosen, M. I., Remien, R. H., Etoori, D., Wilson, I. B., Simoni, J. M., Erlen, J. A., Van Der Laan, M. J., Liu, H., & Bangsberg, D. R. (2015). Super learner analysis of electronic adherence data improves viral prediction and may provide strategies for selective HIV RNA monitoring. Journal of Acquired Immune Deficiency Syndromes, 69(1), 109-118. https://doi.org/10.1097/QAI.0000000000000548