Pre-exposure prophylaxis (PrEP) is a preventive daily medication prescribed to people at risk of contracting HIV, and it’s proven to significantly reduce their risk. Even though it’s an effective treatment, it’s underutilized, in part because clinicians don’t consistently identify PrEP candidates.
A new predictive model, developed by researchers from several Boston-based organizations using data from Atrius Health, was found to effectively identify patients at risk of getting HIV. Integrating the new model into clinical practice could improve PrEP prescribing rates, preventing new infections.
Researchers tested 180 potential HIV risk factors to develop a predictive model that ultimately incorporated the 23 most relevant risk factors. They tested the model’s performance in predicting which patients would contract HIV using anonymized patient data from Epic. The model identified nearly 9,000 new potential PrEP candidates out of more than 500,000 patients seen at Atrius Health in 2016.
“Predictive models can provide actionable information to providers at the point of care,” said Dr. Doug Krakower, infectious disease specialist and lead researcher on the study. “Integrating these models into EHRs to alert providers about patients who might benefit from PrEP could improve prescribing and prevent new HIV infections.”
Read more about this study in The Lancet HIV.