Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study

Publication date: Available online 5 July 2019Source: The Lancet HIVAuthor(s): Julia L Marcus, Leo B Hurley, Douglas S Krakower, Stacey Alexeeff, Michael J Silverberg, Jonathan E VolkSummaryBackgroundThe limitations of existing HIV risk prediction tools are a barrier to implementation of pre-exposure prophylaxis (PrEP). We developed and validated an HIV prediction model to identify potential PrEP candidates in a large health-care system.MethodsOur study population was HIV-uninfected adult members of Kaiser Permanente Northern California, a large integrated health-care system, who were not yet using PrEP and had at least 2 years of previous health plan enrolment with at least one outpatient visit from Jan 1, 2007, to Dec 31, 2017. Using 81 electronic health record (EHR) variables, we applied least absolute shrinkage and selection operator (LASSO) regression to predict incident HIV diagnosis within 3 years on a subset of patients who entered the cohort in 2007–14 (development dataset), assessing ten-fold cross-validated area under the receiver operating characteristic curve (AUC) and 95% CIs. We compared the full model to simpler models including only men who have sex with men (MSM) status and sexually transmitted infection (STI) positivity, testing, and treatment. Models were validated prospectively with data from an independent set of patients who entered the cohort in 2015–17. We computed predicted probabilities of incident HIV diagnosis within 3 years (risk scores), cat...
Source: The Lancet HIV - Category: Infectious Diseases Source Type: research