Development of a 2-step algorithm to identify emergency department patients for HIV pre-exposure prophylaxis

Am J Emerg Med. 2022 Jan:51:6-12. doi: 10.1016/j.ajem.2021.09.084. Epub 2021 Oct 6.

Abstract

Background: Expanded access to HIV PrEP is a central pillar of the "Ending the HIV Epidemic" initiative. Identification of PrEP eligible individuals in EDs remains understudied. Our goal was to estimate the accuracy of the Denver HIV Risk Score (DHRS), a quantitative HIV risk tool, for determining PrEP eligibility, and to incorporate it into a novel screening algorithm to optimize sensitivity and specificity.

Methods: We performed a prospective cross-sectional study in two urban EDs. Patients were eligible if ≥18 years of age and without HIV. Research staff collected individual HIV risk, components of the DHRS, and PrEP eligibility per 2017 CDC guidelines. Accuracy estimates were calculated for the DHRS alone and the DHRS plus additional PrEP-specific questions.

Results: 1002 patients were enrolled with a median age of 39 years; 54.8% were male, 29.5% Black/non-Hispanic, and 22.5% Hispanic. Overall, 119 (11.9%, 95% CI: 9.9%-14.0%) were PrEP eligible; 5% endorsed history of sex with a partner at higher risk for HIV or condomless sex with multiple partners, 4% an STI, and 2% sharing IDU equipment. A DHRS ≥25 had a sensitivity of 92.4% (95% CI: 86.1%-96.5%) and a specificity of 17.2% (95% CI: 14.8%-19.9%) for PrEP eligibility. A 2-step algorithm, "DHRS-PrEP", beginning with a DHRS ≥25, followed by a step with questions specific to IDU, STI, and sexual partners improved the specificity to 100% (95% CI: 99.6%-100%).

Conclusions: Among a heterogeneous ED sample, a substantial proportion was identified as PrEP eligible, and a 2-step algorithm had high sensitivity and specificity for identifying PrEP-eligible patients.

Keywords: Denver HIV risk score; Emergency department; HIV; HIV risk; Identification; Pre-exposure prophylaxis; Prediction; Prevention.

Publication types

  • Multicenter Study
  • Observational Study

MeSH terms

  • Adult
  • Algorithms*
  • Cross-Sectional Studies
  • Emergency Service, Hospital
  • Ethnicity / statistics & numerical data
  • Female
  • HIV Infections / epidemiology*
  • HIV Infections / prevention & control*
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Pre-Exposure Prophylaxis*
  • Prospective Studies
  • Risk Factors
  • Sensitivity and Specificity