Development and validation of an automated HIV prediction algorithm to identify candidates for pre-exposure prophylaxis: a modelling study

Publication date: Available online 5 July 2019Source: The Lancet HIVAuthor(s): Douglas S Krakower, Susan Gruber, Katherine Hsu, John T Menchaca, Judith C Maro, Benjamin A Kruskal, Ira B Wilson, Kenneth H Mayer, Michael KlompasSummaryBackgroundHIV pre-exposure prophylaxis (PrEP) is effective but underused, in part because clinicians do not have the tools to identify PrEP candidates. We developed and validated an automated prediction algorithm that uses electronic health record (EHR) data to identify individuals at increased risk for HIV acquisition.MethodsWe used machine learning algorithms to predict incident HIV infections with 180 potential predictors of HIV risk drawn from EHR data from 2007–15 at Atrius Health, an ambulatory group practice in Massachusetts, USA. We included EHRs of all patients aged 15 years or older with at least one clinical encounter during 2007–15. We used ten-fold cross-validated area under the receiver operating characteristic curve (cv-AUC) with 95% CIs to assess the model's performance at identifying individuals with incident HIV and patients independently prescribed PrEP by clinicians. The best-performing model was validated prospectively with 2016 data from Atrius Health and externally with 2011–16 data from Fenway Health, a community health centre specialising in sexual health care in Boston (MA, USA). We calculated HIV risk scores (ie, probability of an incident HIV diagnosis) for every HIV-uninfected patient not on PrEP during 2007–15...

Source: The Lancet HIV - July 6, 2019 Category: Infectious Diseases Source Type: research