Evidence map›Paper›PMID 29672136›Full record

ArticleAIDS patient care and STDs2018

Which Patients in the Emergency Department Should Receive Preexposure Prophylaxis? Implementation of a Predictive Analytics Approach.

Jessica P Ridgway, Ellen A Almirol, Alvie Bender, Andrew Richardson, Jessica Schmitt, Eleanor Friedman, Nicola Lancki, Ivan Leroux, Nina Pieroni, Jessica Dehlin and 1 more

Open access · greenAbstract read
In one paragraph

Article in AIDS patient care and STDs, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 1 institution in 1 country.

Jessica P Ridgway1 Department of Medicine, University of Chicago , Chicago, Illinois.
Ellen A Almirol2 Chicago Center for HIV Elimination, University of Chicago , Chicago, Illinois.
Alvie Bender1 Department of Medicine, University of Chicago , Chicago, Illinois.
Andrew Richardson1 Department of Medicine, University of Chicago , Chicago, Illinois.
Jessica Schmitt1 Department of Medicine, University of Chicago , Chicago, Illinois.
Eleanor Friedman2 Chicago Center for HIV Elimination, University of Chicago , Chicago, Illinois.
Nicola Lancki2 Chicago Center for HIV Elimination, University of Chicago , Chicago, Illinois.
Ivan Leroux2 Chicago Center for HIV Elimination, University of Chicago , Chicago, Illinois.
Nina Pieroni1 Department of Medicine, University of Chicago , Chicago, Illinois.
Jessica Dehlin2 Chicago Center for HIV Elimination, University of Chicago , Chicago, Illinois.
John A Schneider1 Department of Medicine, University of Chicago , Chicago, Illinois.
University of Chicago · US

Funding

Virology and Immunology Technology CoreP30AI117943 · NIAID · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Michael E Newcomb · 2015 to 2026
$38.4M
NIAID NIH HHS P30 AI117943
6 · The paper itself

Abstract

Emergency Departments (EDs) have the potential to play a crucial role in HIV prevention by identifying and linking high-risk HIV-negative clients to preexposure prophylaxis (PrEP) care, but it is difficult to perform HIV risk assessment for all ED patients. We aimed to develop and implement an electronic risk score to identify ED patients who are potential candidates for PrEP. Using electronic medical record (EMR) data, we used logistic regression to model the outcome of PrEP eligibility. We converted the model into an electronic risk score and incorporated it into the EMR. The risk score is automatically calculated at triage. For patients whose risk score is above a given threshold, an automated electronic alert is sent to an HIV prevention counselor who performs real time HIV prevention counseling, risk assessment, and PrEP linkage as appropriate. The electronic risk score includes the following EMR variables: age, gender, gender of sexual partner, chief complaint, and positive test for sexually transmitted infection in the prior 6 months. A risk score ≥21 has specificity of 80.6% and sensitivity of 50%. In the first 5.5 months of implementation, the alert fired for 180 patients, 34.4% (62/180) of whom were women. Of the 51 patients who completed risk assessment, 68.6% (35/51) were interested in PrEP, 17.6% (9/51) scheduled a PrEP appointment, and 7.8% (4/51) successfully initiated PrEP. The measured number of successful PrEP initiations is likely an underestimate, as it does include patients who initiated PrEP with outside providers or referred acquaintances for PrEP care.

Indexed as

Emergency Service, HospitalAdultAge FactorsDisease Transmission, InfectiousElectronic Health RecordsFemaleHIV InfectionsHumansLogistic ModelsMalePre-Exposure ProphylaxisRisk AssessmentSexual BehaviorSexually Transmitted DiseasesSexual Partnersclinical informaticsemergency departmentHIV preventionpredictive analyticspreexposure prophylaxis

Identifiers

PMID29672136
PMCPMC6939581
OpenAlexW2800698351

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.