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. 2010 Mar 1;50(5):779-86.
doi: 10.1086/650537.

Predicting virologic failure in an HIV clinic

Gregory K Robbins  1 , Kristin L Johnson, Yuchiao Chang, Katherine E Jackson, Paul E Sax, James B Meigs, Kenneth A Freedberg
Affiliations

Predicting virologic failure in an HIV clinic

Gregory K Robbins et al. Clin Infect Dis. .

Abstract

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 < or = 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 (chi(2)= 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 (chi(2)= 1.9; P= .93). The clinical prediction rule included 7 predictors (suboptimal adherence, CD4 cell count < 100 cells/microL, drug and/or alcohol abuse, highly ART experienced, missed > or = 1 appointment, prior virologic failure, and suppressed < or = 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.

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Conflict of interest statement

Conflicts of interest: Dr. Robbins has received research support from Gilead Sciences, Schering-Plough, and consulting fees from Abbott Laboratories, Boehringer Ingelheim Pharmaceuticals and Tibotec. Dr. Sax reports receiving research support from GlaxoSmithKline, Pfizer and Merck Laboratories, and consulting fees from Abbott Laboratories, Bristol-Myers Squibb, Gilead Sciences, GlaxoSmithKline, Merck Laboratories and Tibotec. All other authors have no potential conflicts of interest to disclose.

Figures

Figure 1
Figure 1. One-Year VF Rate by Risk Categories
One-year virologic failure rate and 95% confidence intervals (CI, shown by bars) by risk categories for the derivation and validation datasets.
Figure 2
Figure 2. Receiver-Operator Characteristic Curves
Receiver-operator characteristic (ROC) curves for the derivation and validation datasets. The ability of the risk score and risk category to discriminate, c-statistic (area under the receiver operating characteristic curve), is shown to the right of each curve. For the derivation data set the positive predictive value for the high risk group was 30.6% and the negative predictive was 94.6%, these values for the validation dataset were 28.6% and 92.3%, respectively.
Figure 3
Figure 3. Time to Virologic Failure by Risk Group
Time to virologic failure (HIV RNA >400 copies/mL) for the three risk categories using the validation dataset (Brigham and Women's Hospital). Number of patients included in the analyses at baseline and each 6 month interval are shown in the table. This includes patients with less than one full year of follow-up.
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