12 Nov 2014
Puttkammer N, Zeliadt S, Balan JG, Baseman J, Destiné R, Domerçant JW, et al. (2014). Development of an electronic medical record based alert for risk of HIV treatment failure in a low-resource setting. PLoS One. 9(11), e112261. doi: 10.1371/journal.pone.0112261
The adoption of electronic medical record systems in resource-limited settings can help clinicians monitor patients’ adherence to HIV antiretroviral therapy (ART) and identify patients at risk of future ART failure, allowing resources to be targeted to those most at risk.
Among adult patients enrolled on ART from 2005–2013 at two large, public-sector hospitals in Haiti, ART failure was assessed after 6–12 months on treatment, based on the World Health Organization’s immunologic and clinical criteria. We identified models for predicting ART failure based on ART adherence measures and other patient characteristics. We assessed performance of candidate models using area under the receiver operating curve, and validated results using a randomly-split data sample. The selected prediction model was used to generate a risk score, and its ability to differentiate ART failure risk over a 42-month follow-up period was tested using stratified Kaplan Meier survival curves.
Among 923 patients with CD4 results available during the period 6–12 months after ART initiation, 196 (21.2%) met ART failure criteria. The pharmacy-based proportion of days covered (PDC) measure performed best among five possible ART adherence measures at predicting ART failure. Average PDC during the first 6 months on ART was 79.0% among cases of ART failure and 88.6% among cases of non-failure (p<0.01). When additional information including sex, baseline CD4, and duration of enrollment in HIV care prior to ART initiation were added to PDC, the risk score differentiated between those who did and did not meet failure criteria over 42 months following ART initiation.
Pharmacy data are most useful for new ART adherence alerts within iSanté. Such alerts offer potential to help clinicians identify patients at high risk of ART failure so that they can be targeted with adherence support interventions, before ART failure occurs.