Evidence map›Paper›PMID 33000054›Full record

ReviewJournal of the American College of Emergency Physicians open2020

Predicting cardiac arrest in the emergency department.

Oscar J L Mitchell, Dana P Edelson, Benjamin S Abella

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of the American College of Emergency Physicians open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

15 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
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  14. Predicting cardiac arrest in the emergency department.Journal of the American College of Emergency Physicians open · 2020
    Review
  15. A proposal for selective resuscitation of adult cardiac arrest patients in a pandemic.Journal of the American College of Emergency Physicians open · 2020
    Article
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

3 authors at 3 institutions in 1 country.

Oscar J L MitchellDivision of Pulmonary, Allergy, and Critical Care Medicine and the Center for Resuscitation Science Hospital of the University of Pennsylvania Philadelphia Pennsylvania.ORCID https://orcid.org/0000-0002-5331-5562
Dana P EdelsonDepartment of Medicine University of Chicago Chicago Illinois.
Benjamin S AbellaDepartment of Emergency Medicine and the Center for Resuscitation Science University of Pennsylvania School of Medicine Philadelphia Pennsylvania.
Hospital of the University of Pennsylvania · USUniversity of Chicago · USUniversity of Pennsylvania · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In-hospital cardiac arrest remains a leading cause of death: roughly 300,000 in-hospital cardiac arrests occur each year in the United States, ≈10% of which occur in the emergency department. ED-based cardiac arrest may represent a subset of in-hospital cardiac arrest with a higher proportion of reversible etiologies and a higher potential for neurologically intact survival. Patients presenting to the ED have become increasingly complex, have a high burden of critical illness, and face crowded departments with thinly stretched resources. As a result, patients in the ED are vulnerable to unrecognized clinical deterioration that may lead to ED-based cardiac arrest. Efforts to identify patients who may progress to ED-based cardiac arrest have traditionally been approached through identification of critically ill patients at triage and the identification of patients who unexpectedly deteriorate during their stay in the ED. Interventions to facilitate appropriate triage and resource allocation, as well as earlier identification of patients at risk of deterioration in the ED, could potentially allow for both prevention of cardiac arrest and optimization of outcomes from ED-based cardiac arrest. This review will discuss the epidemiology of ED-based cardiac arrest, as well as commonly used approaches to predict ED-based cardiac arrest and highlight areas that require further research to improve outcomes for this population.

Indexed as

cardiac arrestdeteriorationearly warning scoresmachine learningpredictionquality improvementtriage

Identifiers

PMID33000054
PMCPMC7493514
OpenAlexW2998635629

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.