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Article
Validation of an algorithm to identify antiretroviral-naive status at time of entry into a large, observational cohort of HIV-infected patients
Women’s Health Research Faculty Publications
  • Neel R. Gandhi, Albert Einstein College of Medicine
  • Janet P. Tate, Yale University
  • Maria C. Rodriguez-Barradas, Baylor College of Medicine
  • David Rimland, Emory University
  • Matthew Bidwell Goetz, University of California - Los Angeles
  • Cynthia Gibert, George Washington University
  • Sheldon T. Brown, Mount Sinai School of Medicine
  • Kristin M Mattocks, University of Massachusetts Medical School Worcester
  • Amy C. Justice, Yale University
UMMS Affiliation
Department of Quantitative Health Sciences
Date
9-1-2013
Document Type
Article
Subjects
*Algorithms; Anti-Retroviral Agents; Cohort Studies; Female; HIV Infections; Humans; Male; Medical Records; Middle Aged; Observational Study as Topic; Pharmacoepidemiology; Predictive Value of Tests; Questionnaires; Reproducibility of Results; Retrospective Studies; Viral Load
Abstract
PURPOSE: Large, observational HIV cohorts play an important role in answering questions which are difficult to study in randomized trials; however, they often lack detailed information regarding previous antiretroviral treatment (ART). Knowledge of ART treatment history is important when ascertaining the long-term impact of medications, co-morbidities, or adverse reactions on HIV outcomes. METHODS: We performed a retrospective study to validate a prediction algorithm for identifying ART-naive patients using the Veterans Aging Cohort Study's Virtual Cohort-an observational cohort of 40 594 HIV-infected veterans nationwide. Medical records for 3070 HIV-infected patients were reviewed to determine history of combination ART treatment. An algorithm using Virtual Cohort laboratory data was used to predict ART treatment status and compared to medical record review. RESULTS: Among 3070 patients' medical records reviewed, 1223 were eligible for analysis. Of these, 990 (81%) were ART naive at cohort entry based on medical record review. The prediction algorithm's sensitivity was 86%, specificity 47%, positive predictive value (PPV) 87%, and negative predictive value 45%, using a viral load threshold of /ml. Sensitivity analysis revealed that PPV would be maximized by increasing the viral load threshold, whereas sensitivity would be maximized by lowering the viral load threshold. CONCLUSIONS: A prediction algorithm using available laboratory data can be used to accurately identify ART-naive patients in large, observational HIV cohorts. Use of this algorithm will allow investigators to accurately limit analyses to ART-naive patients when studying the contribution of ART to outcomes and adverse events.
Rights and Permissions
Citation: 2013 Jul 9. Link to article on publisher's site
Related Resources
Link to Article in PubMed
PubMed ID
23836591
Citation Information
Neel R. Gandhi, Janet P. Tate, Maria C. Rodriguez-Barradas, David Rimland, et al.. "Validation of an algorithm to identify antiretroviral-naive status at time of entry into a large, observational cohort of HIV-infected patients" Vol. 22 Iss. 9 (2013) ISSN: 1053-8569 (Linking)
Available at: http://works.bepress.com/kristin_mattocks/22/