Evidence map›Paper›PMID 36814287›Full record

ArticleCritical care (London, England)2023

Oxygenation thresholds for invasive ventilation in hypoxemic respiratory failure: a target trial emulation in two cohorts.

Christopher J Yarnell, Federico Angriman, Bruno L Ferreyro, Kuan Liu, Harm Jan De Grooth, Lisa Burry, Laveena Munshi, Sangeeta Mehta, Leo Celi, Paul Elbers and 6 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Critical care (London, England), 2023. 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
5.4field-weighted citation impact, top 4% 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, 21 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

16 authors at 7 institutions in 3 countries.

Christopher J YarnellInterdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada. christopher.yarnell@uhn.ca.ORCID 0000-0001-5657-9398
Federico AngrimanInterdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada.ORCID 0000-0003-0971-386X
Bruno L FerreyroInterdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada.ORCID 0000-0001-7485-3741
Kuan LiuInstitute of Health Policy, Management and Evaluation, University of Toronto, Medical-Surgical ICU, 10th floor, 585 University Avenue, Toronto, ON, M5G 1X5, Canada.
Harm Jan De GroothDepartment of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.ORCID 0000-0002-7499-076X
Lisa BurryInterdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada.ORCID 0000-0002-6545-3890
Laveena MunshiInterdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada.
Sangeeta MehtaInterdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada.ORCID 0000-0002-7073-4769
Leo CeliInstitute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA.
Paul ElbersDepartment of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
Patrick ThoralDepartment of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
Laurent BrochardKeenan Research Centre for Biomedical Research, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, Canada.
Hannah WunschInstitute for Clinical Evaluative Sciences, Toronto, Canada.
Robert A FowlerInterdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada.
Lillian SungInstitute of Health Policy, Management and Evaluation, University of Toronto, Medical-Surgical ICU, 10th floor, 585 University Avenue, Toronto, ON, M5G 1X5, Canada.
George TomlinsonDepartment of Medicine, University Health Network and Sinai Health System, Toronto, Canada.
University Health Network · CAAmsterdam University Medical Centers · NLSunnybrook Health Science Centre · CAUniversity of Toronto · CA3M (United States) · USBeth Israel Deaconess Medical Center · USSt. Michael's Hospital · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe optimal thresholds for the initiation of invasive ventilation in patients with hypoxemic respiratory failure are unknown. Using the saturation-to-inspired oxygen ratio (SF), we compared lower versus higher hypoxemia severity thresholds for initiating invasive ventilation.

methodsThis target trial emulation included patients from the Medical Information Mart for Intensive Care (MIMIC-IV, 2008-2019) and the Amsterdam University Medical Centers (AmsterdamUMCdb, 2003-2016) databases admitted to intensive care and receiving inspired oxygen fraction ≥ 0.4 via non-rebreather mask, noninvasive ventilation, or high-flow nasal cannula. We compared the effect of using invasive ventilation initiation thresholds of SF < 110, < 98, and < 88 on 28-day mortality. MIMIC-IV was used for the primary analysis and AmsterdamUMCdb for the secondary analysis. We obtained posterior means and 95% credible intervals (CrI) with nonparametric Bayesian G-computation.

resultsWe studied 3,357 patients in the primary analysis. For invasive ventilation initiation thresholds SF < 110, SF < 98, and SF < 88, the predicted 28-day probabilities of invasive ventilation were 72%, 47%, and 19%. Predicted 28-day mortality was lowest with threshold SF < 110 (22.2%, CrI 19.2 to 25.0), compared to SF < 98 (absolute risk increase 1.6%, CrI 0.6 to 2.6) or SF < 88 (absolute risk increase 3.5%, CrI 1.4 to 5.4). In the secondary analysis (1,279 patients), the predicted 28-day probability of invasive ventilation was 50% for initiation threshold SF < 110, 28% for SF < 98, and 19% for SF < 88. In contrast with the primary analysis, predicted mortality was highest with threshold SF < 110 (14.6%, CrI 7.7 to 22.3), compared to SF < 98 (absolute risk decrease 0.5%, CrI 0.0 to 0.9) or SF < 88 (absolute risk decrease 1.9%, CrI 0.9 to 2.8).

conclusionInitiating invasive ventilation at lower hypoxemia severity will increase the rate of invasive ventilation, but this can either increase or decrease the expected mortality, with the direction of effect likely depending on baseline mortality risk and clinical context.

Indexed as

Noninvasive VentilationRespiratory InsufficiencyBayes TheoremHumansHypoxiaIntubation, IntratrachealOxygenOxygen Inhalation TherapyRespirationOxygenBayesian analysisHypoxemic respiratory failureIntensive care medicineMechanical ventilationNoninvasive ventilationStatistical methodsTarget trial emulationThresholds for invasive ventilation

Identifiers

PMID36814287
PMCPMC9944781
OpenAlexW4321480330

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.