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Validation of an algorithm to identify antiretroviral-naïve status at time of entry into a large, observational cohort of HIV-infected patients

Authors

  • Neel R. Gandhi,

    Corresponding author
    1. Division of General Internal Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
    • Departments of Epidemiology, Global Health, and Infectious Diseases, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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  • Janet P. Tate,

    1. Section of General Internal Medicine, Department of Medicine, Yale University School of Medicine and VA Connecticut Healthcare System, New Haven, CT, USA
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  • Maria C. Rodriguez-Barradas,

    1. Infectious Disease Section and Department of Medicine, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX, USA
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  • David Rimland,

    1. Infectious Diseases Section, Department of Medicine, Emory University School of Medicine and Atlanta VA Medical Center, Atlanta, GA, USA
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  • Matthew Bidwell Goetz,

    1. Section of Infectious Diseases, VA Greater Los Angeles Healthcare System and David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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  • Cynthia Gibert,

    1. Section of Infectious Diseases, Washington DC VA Medical Center and George Washington University Medical Center, Washington, District of Columbia, USA
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  • Sheldon T. Brown,

    1. Infectious Disease Section, Department of Medicine, James J. Peters VA Medical Center and Mt. Sinai School of Medicine, New York, NY, USA
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  • Kristin Mattocks,

    1. Quantitative Health Sciences and Psychiatry, VA Central Western Massachusetts Health Care System, Leeds, MA and University of Massachusetts Medical Center, Worcester, MA, USA
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  • Amy C. Justice

    1. Section of General Internal Medicine, Department of Medicine, Yale University School of Medicine and VA Connecticut Healthcare System, New Haven, CT, USA
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Correspondence to: Neel R. Gandhi, MD, Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, CNR 3031, Atlanta, GA 30322, USA. E-mail: neelgandhi@alumni.williams.edu

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-naïve 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 naïve 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 <400 copies/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-naïve patients in large, observational HIV cohorts. Use of this algorithm will allow investigators to accurately limit analyses to ART-naïve patients when studying the contribution of ART to outcomes and adverse events. Copyright © 2013 John Wiley & Sons, Ltd.

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