Department of Medicine, University of Chicago, Chicago, Illinois, USA.
Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
AIDS Patient Care STDS. 2021 Jan;35(1):5-8. doi: 10.1089/apc.2020.0231.
We previously developed an electronic medical record-based algorithm for identifying patients at risk for HIV in the emergency department (ED). The aim of this study was to evaluate the performance of the HIV risk algorithm for identifying cisgender women with a pre-exposure prophylaxis (PrEP) indication. To retrospectively evaluate the HIV risk algorithm, we identified cisgender women with HIV diagnosed in the ED and retrospectively calculated the HIV risk algorithm output. To prospectively validate the algorithm, we surveyed cisgender women seeking care in the ED regarding behavioral risks for HIV. We prospectively determined whether the algorithm identified them as PrEP candidates. In the retrospective evaluation, 9.4% (2/21) of women with incident HIV infection were identified as at risk for HIV by the algorithm. In the prospective evaluation, 24% (59/245) of women who completed the survey had a PrEP indication based on self-report of behavioral risk factors for HIV. The sensitivity of the algorithm for identifying cisgender female PrEP candidates was 10%, and the specificity was 96%. PrEP indications missed by the electronic algorithm included condomless sex in a high HIV prevalence area, multiple sex partners, male partners who have sex with men, and recent bacterial sexually transmitted infections diagnosed at outside clinics. An electronic algorithm to identify PrEP candidates in the ED has low sensitivity for identifying cisgender women with PrEP indications. More research is needed to identify electronic data that can improve the algorithm sensitivity among cisgender women.
我们之前开发了一种基于电子病历的算法,用于在急诊室(ED)识别有 HIV 风险的患者。本研究的目的是评估该 HIV 风险算法在识别有暴露前预防(PrEP)指征的跨性别女性方面的性能。为了回顾性评估 HIV 风险算法,我们确定了在 ED 诊断出 HIV 的跨性别女性,并回顾性计算了 HIV 风险算法的输出。为了前瞻性验证该算法,我们调查了在 ED 寻求护理的跨性别女性,了解其 HIV 风险行为。我们前瞻性地确定了算法是否将她们识别为 PrEP 候选者。在回顾性评估中,9.4%(2/21)感染 HIV 的女性被算法识别为 HIV 风险。在前瞻性评估中,24%(59/245)完成调查的女性根据性行为风险因素的自我报告有 PrEP 指征。该算法识别跨性别女性 PrEP 候选者的敏感性为 10%,特异性为 96%。电子算法错过的 PrEP 指征包括在 HIV 高流行地区无保护性行为、多个性伴侣、男性性伴侣有男男性行为以及最近在外部诊所诊断出的细菌性性传播感染。ED 中用于识别 PrEP 候选者的电子算法在识别有 PrEP 指征的跨性别女性方面的敏感性较低。需要进一步研究以确定可以提高电子数据在跨性别女性中的算法敏感性的方法。