Evidence map›Paper›PMID 37819660›Full record

SynthesisJAMA network open2023

Generation of Aerosols by Noninvasive Respiratory Support Modalities: A Systematic Review and Meta-Analysis.

Madeline X Zhang, Thijs A Lilien, Faridi S van Etten-Jamaludin, Carl-Johan Fraenkel, Daniel Bonn, Alexander P J Vlaar, Jakob Löndahl, Michael Klompas, Reinout A Bem

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
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.

Madeline X ZhangInstitute of Physics, Van der Waals-Zeeman Institute, University of Amsterdam, Amsterdam, the Netherlands.
Thijs A LilienDepartment of Pediatric Intensive Care, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Faridi S van Etten-JamaludinMedical Library AMC, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Carl-Johan FraenkelDivision of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden.
Daniel BonnInstitute of Physics, Van der Waals-Zeeman Institute, University of Amsterdam, Amsterdam, the Netherlands.
Alexander P J VlaarDepartment of Intensive Care, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Jakob LöndahlDivision of Ergonomics and Aerosol Technology, Department of Design Sciences, Lund University, Lund, Sweden.
Michael KlompasDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.
Reinout A BemDepartment of Pediatric Intensive Care, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Infection control guidelines have historically classified high-flow nasal oxygen and noninvasive ventilation as aerosol-generating procedures that require specialized infection prevention and control measures. Objective: To evaluate the current evidence that high-flow nasal oxygen and noninvasive ventilation are associated with pathogen-laden aerosols and aerosol generation. Data Sources: A systematic search of EMBASE and PubMed/MEDLINE up to March 15, 2023, and CINAHL and ClinicalTrials.gov up to August 1, 2023, was performed. Study Selection: Observational and (quasi-)experimental studies of patients or healthy volunteers supported with high-flow nasal oxygen or noninvasive ventilation were selected. Data Extraction and Synthesis: Three reviewers were involved in independent study screening, assessment of risk of bias, and data extraction. Data from observational studies were pooled using a random-effects model at both sample and patient levels. Sensitivity analyses were performed to assess the influence of model choice. Main Outcomes and Measures: The main outcomes were the detection of pathogens in air samples and the quantity of aerosol particles. Results: Twenty-four studies were included, of which 12 involved measurements in patients and 15 in healthy volunteers. Five observational studies on SARS-CoV-2 detection in a total of 212 air samples during high-flow nasal oxygen in 152 patients with COVID-19 were pooled for meta-analysis. There was no association between high-flow nasal oxygen and pathogen-laden aerosols (odds ratios for positive samples, 0.73 [95% CI, 0.15-3.55] at the sample level and 0.80 [95% CI, 0.14-4.59] at the patient level). Two studies assessed SARS-CoV-2 detection during noninvasive ventilation (84 air samples from 72 patients). There was no association between noninvasive ventilation and pathogen-laden aerosols (odds ratios for positive samples, 0.38 [95% CI, 0.03-4.63] at the sample level and 0.43 [95% CI, 0.01-27.12] at the patient level). None of the studies in healthy volunteers reported clinically relevant increases in aerosol particle production by high-flow nasal oxygen or noninvasive ventilation. Conclusions and Relevance: This systematic review and meta-analysis found no association between high-flow nasal oxygen or noninvasive ventilation and increased airborne pathogen detection or aerosol generation. These findings argue against classifying high-flow nasal oxygen or noninvasive ventilation as aerosol-generating procedures or differentiating infection prevention and control practices for patients receiving these modalities.

Indexed as

COVID-19Noninvasive VentilationHumansOxygenRespiratory Aerosols and DropletsSARS-CoV-2Oxygen

Identifiers

PMID37819660
PMCPMC10568354

What OpenQuestion holds

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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.