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

ABSTRACT:

Background

HIV 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.

Methods

We 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

SUBMITTER: Krakower DS 

PROVIDER: S-EPMC7522919 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature