Evidence map›Paper›PMID 35711857›Full record

ArticleJournal of community hospital internal medicine perspectives2022

A Retrospective Analysis of Respiratory Complications under General Anesthesia during EBUS-TBNA.

Emily A S Bergbower, Caron Hong, Miranda Gibbons, Ashutosh Sachdeva, Peter Rock, Megan G Anders

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Article in Journal of community hospital internal medicine perspectives, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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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

6 authors.

Emily A S BergbowerDepartment of Anesthesiology, University of Maryland Medical Center, USA.
Caron HongDepartment of Anesthesiology, University of Maryland Medical Center, USA.
Miranda GibbonsDepartment of Anesthesiology, University of Maryland Medical Center, USA.
Ashutosh SachdevaDepartment of Medicine, University of Maryland Medical Center, USA.
Peter RockDepartment of Anesthesiology, University of Maryland Medical Center, USA.
Megan G AndersDepartment of Anesthesiology, University of Maryland Medical Center, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: EBUS-TBNA is an established technique for diagnostically sampling intrathoracic masses and lymph nodes. While the procedure is commonly conducted under general anesthesia (GA), little is known regarding the association between anesthetic management and perioperative respiratory complications. Here, we aim to evaluate this association among patients presenting for EBUS-TBNA. Methods: 586 patients receiving GA for EBUS-TBNA between 2012 and 2018 were retrospectively evaluated. The primary endpoint was the occurrence of perioperative respiratory complications and the secondary endpoint was procedure end to OR exit time (minutes). Respiratory complications were defined as episodes of severe (SpO Results: Among all patients, 79 (13.5%) had respiratory complications. Four patient characteristics were associated with respiratory complications: home oxygen use (OR 2.39; 95% CI 1.26-4.45; P = 0.007), pre-existing respiratory disease (OR 2.01; CI 1.21-3.29; P = 0.005), ASA class (P = 0.03), and albuterol administration intra-operatively (OR 2.22; CI 1.23-3.92; P = 0.007). No anesthetic factors were found to be statistically significant. Procedures with respiratory complications had a longer duration (mean time 88.7 min vs. 111.8 min; P = 0.00009), prolonged time to extubation (mean time 11.9 min vs. 14.2 min; P = 0.039), and stayed in the room longer after extubation (mean time 18.4 min vs. 23.1 min; P = 0.0016). When comparing types of GA, there were no significant differences between volatile anesthetics versus TIVA (12.7% vs. 14.6%, P = 0.54). Conclusions: Pre-existing patient characteristics, as opposed to anesthetic factors, are associated with respiratory complications during EBUS-TBNA.

Indexed as

EBUS-TBNAGeneral anesthesiaInterventional pulmonologyRespiratory complications

Identifiers

PMID35711857
PMCPMC9195124

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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.