ArticleJournal of acquired immune deficiency syndromes (1999)2015
Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring.
Article in Journal of acquired immune deficiency syndromes (1999), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Comparison of six methods to estimate adherence in an ART-naïve cohort in a resource-poor setting: which best predicts virological and resistance outcomes?AIDS research and therapy · 2017Trial
- A comparative analysis in a clinical cohort: multiple imputation by chained equations and a novel super learner-based imputation approach.BMC medical research methodology · 2026Article
- Practical Considerations for Variable Screening in the Super Learner.The New England Journal of Statistics in Data Science · 2025Article
- Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea).Frontiers in artificial intelligence · 2025Article
- Opportunities and challenges of 5G network technology toward precision medicine.Clinical and translational science · 2023Review
- SpiderLearner: An ensemble approach to Gaussian graphical model estimation.Statistics in medicine · 2023Article
- The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions.The international journal of biostatistics · 2023Article
- Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell count.Journal of family medicine and primary care · 2023Article
- Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals.International journal of general medicine · 2023Article
- MISL: Multiple imputation by super learning.Statistical methods in medical research · 2022Article
- Determinants of COVID-19 Outcome as Predictors of Delayed Healthcare Services among Adults ≥50 Years during the Pandemic: 2006-2020 Health and Retirement Study.International journal of environmental research and public health · 2022Article
- Predictors of Covid-19 level of concern among older adults from the health and retirement study.Scientific reports · 2022Article
- Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.Current HIV/AIDS reports · 2021Review
- Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression.Translational vision science & technology · 2021Article
- Adherence predictor variables in AIDS patients: an empirical study using the data mining-based RFM model.AIDS research and therapy · 2021Article
- Comparison of machine learning methods for predicting viral failure: a case study using electronic health record data.Statistical communications in infectious diseases · 2020Article
- Economic evaluations of big data analytics for clinical decision-making: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic.Current HIV/AIDS reports · 2020Review
- Flexible model selection for mechanistic network models.Journal of complex networks · 2020Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
objectiveRegular 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.
designMultisite prospective cohort consortium.
methodsWe 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.
resultsApplication 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.
conclusionsOur findings provide initial proof of concept for the potential use of electronic medication adherence data to reduce costs through behavior-driven HIV RNA testing.
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