Evidence map›Paper›PMID 35764346›Full record

Trial reportRespiratory care2022

Predictors of Respiratory Support Use in Emergency Department Patients With COVID-19-Related Respiratory Failure.

Neha N Goel, Erin Eschbach, Daniel McConnell, Bryan Beattie, Sean Hickey, John Rozehnal, Evan Leibner, Gary Oldenburg, Kusum S Mathews

Open access · greenAbstract readClinical TrialMulticenter Study
In one paragraph

Trial report in Respiratory care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 86% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

9 authors at 2 institutions in 1 country.

Neha N GoelDivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York. neha.goel@mountsinai.org.
Erin EschbachDivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Daniel McConnellDepartment of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Bryan BeattieDepartment of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Sean HickeyDepartment of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
John RozehnalDepartment of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Evan LeibnerDepartment of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Gary OldenburgRespiratory Care Services, Mount Sinai Hospital, New York, New York.
Kusum S MathewsDivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; and Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Icahn School of Medicine at Mount Sinai · USMount Sinai Hospital · US

Funding

Optimizing the use of noninvasive respiratory support in the Emergency DepartmentK23HL161316 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Neha Neelam Goel · 2023 to 2026
$757k
NHLBI NIH HHS K23 HL161316
6 · The paper itself

Abstract

backgroundGiven the known downstream implications of choice of respiratory support on patient outcomes, all factors influencing these decisions, even those not limited to the patient, warrant close consideration. We examined the effect of emergency department (ED)-specific system factors, such as work load and census, on the use of noninvasive versus invasive respiratory support.

methodsWe conducted a multi-center retrospective cohort study of all adult subjects with severe COVID-19 requiring an ICU admission from 5 EDs within a single urban health care system. Subject demographics, severity of illness, and the type of respiratory support used were obtained. Using continuous measures of ED census, boarding, and active management, we estimated ED work load for each subjects' ED stay. The subjects were categorized by type(s) of respiratory support used: low-flow oxygen, noninvasive respiratory support (eg, noninvasive ventilation [NIV] and/or high-flow nasal cannula [HFNC]), invasive mechanical ventilation, or invasive mechanical ventilation after trial of NIV/HFNC. We used multivariable logistic regression to examine system factors associated with the type of respiratory support used in the ED.

resultsA total of 634 subjects were included. Of these, 431 (70.0%) were managed on low-flow oxygen alone, 108 (17.0%) on NIV/HFNC, 54 (8.5%) on invasive mechanical ventilation directly, and 41 (6.5%) on NIV/HFNC prior to invasive mechanical ventilation in the ED. Higher severity of illness and underlying lung disease increased the odds of requiring invasive mechanical ventilation compared to low-flow oxygen (odds ratio 1.05 [95% CI 1.03-1.07] and odds ratio 3.47 [95% CI 1.37-8.78], respectively). Older age decreased odds of being on invasive mechanical ventilation compared to low-flow oxygen (odds ratio 0.96 [95% CI 0.94-0.99]). As ED work load increased, the odds for subjects to be managed initially with NIV/HFNC prior to invasive mechanical ventilation increased 6-8-fold.

conclusionsHigh ED work load was associated with higher odds on HFNC/NIV prior to invasive mechanical ventilation.

Indexed as

COVID-19Noninvasive VentilationRespiratory InsufficiencyAdultCannulaEmergency Service, HospitalHumansOxygen Inhalation TherapyRetrospective StudiesED workloadnoninvasive ventilationrespiratory supportsystem factors

Identifiers

PMID35764346
PMCPMC9994335
OpenAlexW4283687757

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.