Evidence map›Paper›PMID 35991745›Full record

ArticleProceedings (Baylor University. Medical Center)2022

Usefulness of preoperative point-of-care ultrasound measurement of the lateral parapharyngeal wall to predict difficulty in mask ventilation.

Nikita Mehta, Esther Lee, Madeline Pence, Wyatt Nice, Ryan Keneally, Raymond Pla, Anita Vincent, Eric Heinz

Abstract read
In one paragraph

Article in Proceedings (Baylor University. Medical Center), 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
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.

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

8 authors.

Nikita MehtaDepartment of Anesthesiology and Critical Care Medicine, George Washington University Hospital, Washington, DC.
Esther LeeDepartment of Anesthesiology and Critical Care Medicine, George Washington University Hospital, Washington, DC.
Madeline PenceDepartment of Anesthesiology and Critical Care Medicine, George Washington University Hospital, Washington, DC.
Wyatt NiceDepartment of Anesthesiology and Critical Care Medicine, George Washington University Hospital, Washington, DC.
Ryan KeneallyDepartment of Anesthesiology and Critical Care Medicine, George Washington University Hospital, Washington, DC.
Raymond PlaDepartment of Anesthesiology and Critical Care Medicine, George Washington University Hospital, Washington, DC.
Anita VincentDepartment of Anesthesiology and Critical Care Medicine, George Washington University Hospital, Washington, DC.
Eric HeinzDepartment of Anesthesiology and Critical Care Medicine, George Washington University Hospital, Washington, DC.ORCID https://orcid.org/0000-0002-3194-8553

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Measurement of the lateral parapharyngeal wall has been shown to correlate with severity of obstructive sleep apnea, which is believed to increase risk of difficulty in mask ventilation (MV). This study aimed to assess the efficacy of using ultrasound to measure the lateral parapharyngeal wall thickness (LPWT) to predict the difficulty of MV. The LPWT was measured as the distance between the inferior border of the carotid artery and the lateral wall of the pharynx. Difficulty of MV was assessed according to an MV scale. A total of 92 patients were enrolled. Measurements of the LPWT ranged from 1.52 to 4.43 cm. There was a significant correlation between LPWT and difficulty of MV (

Indexed as

Difficult airwaylateral parapharyngeal wallmask ventilationobstructive sleep apneaultrasonography

Identifiers

PMID35991745
PMCPMC9373780

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.