Evidence map›Paper›PMID 36968975›Full record

ArticleClinical medicine insights. Circulatory, respiratory and pulmonary medicine2023

Impact of an Early Warning System Protocol, for Patients Admitted to the Medical Floors with SARS-COV2 Pneumonia, on ICU Admission.

Seife Yohannes, Nitin Seam, Junfeng Sun, Joel McAlduff, Janet L Thorne, Susanne B Lara, Michael Keller

Abstract read
In one paragraph

Article in Clinical medicine insights. Circulatory, respiratory and pulmonary medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

7 authors.

Seife YohannesDepartment of Critical Care, MedStar Washington Hospital Center, Washington DC, USA.ORCID https://orcid.org/0000-0001-6233-3749
Nitin SeamClinical Center, National Institutes of Health, Bethesda, Maryland, USA.
Junfeng SunClinical Center, National Institutes of Health, Bethesda, Maryland, USA.
Joel McAlduffMedstar Health, Columbia, Maryland, USA.
Janet L ThorneMedStar Washington Hospital Center, Washington DC, USA.
Susanne B LaraDepartment of Critical Care, MedStar Washington Hospital Center, Washington DC, USA.
Michael KellerClinical Center, National Institutes of Health, Bethesda, Maryland, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 placed a significant burden on the global healthcare system. Strain in critical care capacity has been associated with increased COVID-19-related ICU mortality. This study evaluates the impact of an early warning system and response team implemented on medical floors to safely triage and care for critically ill patients on the floor and preserve ICU capacity.

methodsWe conducted a multicenter, retrospective cohort study, comparing outcomes between intervention and control hospitals within a US eight-hospital urban network. Patients hospitalized with COVID-19 pneumonia between April 13

resultsAmong 1024 patients, 403 (39%) were admitted to the intervention hospital and 621 (61%) were admitted to one of the control hospitals. Adjusted for potential confounders, patients at the intervention hospital were less likely to be admitted to the ICU (HR = 0.73, 95% CI 0.53, 1.000,

conclusionA protocol to conserve ICU beds by implementing an early warning system with a dedicated response team to manage respiratory distress on the floors reduced ICU admission and was not associated with worse outcomes compared to hospitals that managed similar levels of respiratory distress in the ICU.

Indexed as

ARDSCOVID-19Early warning systemICU capacitypandemicpneumoniaSARS-COV2

Identifiers

PMID36968975
PMCPMC10034308

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.