Journal Article

Predicting Virologic Failure in an HIV Clinic

Gregory K. Robbins, Kristin L. Johnson, Yuchiao Chang, Katherine E. Jackson, Paul E. Sax, James B. Meigs and Kenneth A. Freedberg

in Clinical Infectious Diseases

Published on behalf of Infectious Diseases Society of America

Volume 50, issue 5, pages 779-786
Published in print March 2010 | ISSN: 1058-4838
Published online March 2010 | e-ISSN: 1537-6591 | DOI:
Predicting Virologic Failure in an HIV Clinic

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Background. We sought to use data captured in the electronic health record (EHR) to develop and validate a prediction rule for virologic failure among patients being treated for infection with human immunodeficiency virus (HIV).

Methods. We used EHRs at 2 Boston tertiary care hospitals, Massachusetts General Hospital and Brigham and Women's Hospital, to identify HIV-infected patients who were virologically suppressed (HIV RNA level ⩽400 copies/mL) on antiretroviral therapy (ART) during the period from 1 January 2005 through 31 December 2006. We used a multivariable logistic model with data from Massachusetts General Hospital to derive a 1-year virologic failure prediction rule. The model was validated using data from Brigham and Women's Hospital. We then simplified the scoring scheme to develop a clinical prediction rule.

Results. The 1-year virologic failure prediction model, using data from 712 patients from Massachusetts General Hospital, demonstrated good discrimination (C statistic, 0.78) and calibration (X2=6.6; P=.58). The validation model, based on 362 patients from Brigham and Women's Hospital, also showed good discrimination (C statistic, 0.79) and calibration (X2=1.9; P=.93). The clinical prediction rule included 7 predictors (suboptimal adherence, CD4 cell count <100 cells/µL, drug and/or alcohol abuse, highly ART experienced, missed ⩾1 appointment, prior virologic failure, and suppressed ⩽12 months) and appropriately stratified patients in the validation data set into low-, medium-, and high-risk groups, with 1-year virologic failure rates of 3.0%, 13.0%, and 28.6%, respectively.

Conclusions. A risk score based on 7 variables available in the EHR predicts HIV virologic failure at 1 year and could be used for targeted interventions to improve outcomes in HIV infection.

Journal Article.  4605 words.  Illustrated.

Subjects: Infectious Diseases ; Immunology ; Public Health and Epidemiology ; Microbiology

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