Cargando…

A Novel Simulation Model for Training Emergency Medicine Residents in the Ultrasound Identification of Landmarks for Cricothyrotomy

Objectives The objective of this study is to describe a simple, replicable method to create neck models for the purpose of education and practice of ultrasound (US) identification of anatomic landmarks for cricothyrotomy. The second objective is to assess the model’s capability in training emergency...

Descripción completa

Detalles Bibliográficos
Autores principales: Acuña, Josie, Pacheco, Garrett, Yarnish, Adrienne A, Andrade, Javier, Haight, Stephen, Coe, Ian, Carter, Jeremy, Adhikari, Srikar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879590/
https://www.ncbi.nlm.nih.gov/pubmed/36712745
http://dx.doi.org/10.7759/cureus.33003
_version_ 1784878725688459264
author Acuña, Josie
Pacheco, Garrett
Yarnish, Adrienne A
Andrade, Javier
Haight, Stephen
Coe, Ian
Carter, Jeremy
Adhikari, Srikar
author_facet Acuña, Josie
Pacheco, Garrett
Yarnish, Adrienne A
Andrade, Javier
Haight, Stephen
Coe, Ian
Carter, Jeremy
Adhikari, Srikar
author_sort Acuña, Josie
collection PubMed
description Objectives The objective of this study is to describe a simple, replicable method to create neck models for the purpose of education and practice of ultrasound (US) identification of anatomic landmarks for cricothyrotomy. The second objective is to assess the model’s capability in training emergency medicine (EM) residents in the US identification of anatomic landmarks for cricothyrotomy. Methods This is a cross-sectional study using a convenience sample of EM residents. Participants were taught to identify the thyroid cartilage, the cricothyroid membrane (CTM), and the cricoid cartilage using US. After an instructional period, participants performed a US examination on gel models designed to overly a live, human neck simulating various scenarios: thin neck, thick neck, anterior neck hematoma, and subcutaneous emphysema. Residents were asked to identify the thyroid cartilage, the CTM, and the cricoid cartilage as quickly as possible. The mean time to successful identification was reported in seconds. Following the scanning session, participants were asked to complete a post-survey. After the session, the video recordings were reviewed by an emergency US fellowship-trained physician to assess the visuomotor skills of each participant. Results A total of 42 residents participated in the study. Ninety-three percent (32/42; 95% CI 80.3% - 98.2%) of residents were able to obtain an optimal sagittal or parasagittal sonographic view of the anterior airway landmarks. Of these residents, 21.4% (9/42; 95% CI 11.5% - 36.2%) required minimal assistance with the initial probe placement. The visuomotor scores were recorded for each participant. Results of the pearson correlation indicated that there was a significant positive relationship between the residents’ year in training with their visuomotor score (r(40) = .41, p = .007). When scanning the thin neck, 90.5% (38/42; 95% CI 77.4% - 96.8%) of residents were able to successfully identify the landmarks. The median time to completion was 27 seconds. When scanning the subcutaneous air model, 88.1% (37/42; 95% CI 74.5% - 95.3%) of residents were able to successfully identify the landmarks. The median time to completion was 26 seconds. When scanning the neck with the fluid collection 95.2% (40/42; 95% CI 83.4% - 99.5%) of residents were able to successfully identify the landmarks with a median time of 20 seconds for identification. When scanning the thick neck model, 73.8% (31/42; 95% CI 58.8% - 84.8%) of residents were able to successfully identify the landmarks taking a median time of 26 seconds. After the training session, 76.2% of residents reported that they felt either “confident” or “extremely confident” in identifying the CTM using US. Conclusion The novel anterior neck gel models used in this study were found to be adequate for training EM residents in the US identification of anterior neck anatomy. Residents were successfully trained in identifying the important anterior neck landmarks that are useful when predicting a difficult anterior airway and planning for surgical cricothyrotomy. Residents overall felt that the models simulated the appropriate anatomic scenarios. The majority felt confident in identifying the CTM using US.
format Online
Article
Text
id pubmed-9879590
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Cureus
record_format MEDLINE/PubMed
spelling pubmed-98795902023-01-27 A Novel Simulation Model for Training Emergency Medicine Residents in the Ultrasound Identification of Landmarks for Cricothyrotomy Acuña, Josie Pacheco, Garrett Yarnish, Adrienne A Andrade, Javier Haight, Stephen Coe, Ian Carter, Jeremy Adhikari, Srikar Cureus Emergency Medicine Objectives The objective of this study is to describe a simple, replicable method to create neck models for the purpose of education and practice of ultrasound (US) identification of anatomic landmarks for cricothyrotomy. The second objective is to assess the model’s capability in training emergency medicine (EM) residents in the US identification of anatomic landmarks for cricothyrotomy. Methods This is a cross-sectional study using a convenience sample of EM residents. Participants were taught to identify the thyroid cartilage, the cricothyroid membrane (CTM), and the cricoid cartilage using US. After an instructional period, participants performed a US examination on gel models designed to overly a live, human neck simulating various scenarios: thin neck, thick neck, anterior neck hematoma, and subcutaneous emphysema. Residents were asked to identify the thyroid cartilage, the CTM, and the cricoid cartilage as quickly as possible. The mean time to successful identification was reported in seconds. Following the scanning session, participants were asked to complete a post-survey. After the session, the video recordings were reviewed by an emergency US fellowship-trained physician to assess the visuomotor skills of each participant. Results A total of 42 residents participated in the study. Ninety-three percent (32/42; 95% CI 80.3% - 98.2%) of residents were able to obtain an optimal sagittal or parasagittal sonographic view of the anterior airway landmarks. Of these residents, 21.4% (9/42; 95% CI 11.5% - 36.2%) required minimal assistance with the initial probe placement. The visuomotor scores were recorded for each participant. Results of the pearson correlation indicated that there was a significant positive relationship between the residents’ year in training with their visuomotor score (r(40) = .41, p = .007). When scanning the thin neck, 90.5% (38/42; 95% CI 77.4% - 96.8%) of residents were able to successfully identify the landmarks. The median time to completion was 27 seconds. When scanning the subcutaneous air model, 88.1% (37/42; 95% CI 74.5% - 95.3%) of residents were able to successfully identify the landmarks. The median time to completion was 26 seconds. When scanning the neck with the fluid collection 95.2% (40/42; 95% CI 83.4% - 99.5%) of residents were able to successfully identify the landmarks with a median time of 20 seconds for identification. When scanning the thick neck model, 73.8% (31/42; 95% CI 58.8% - 84.8%) of residents were able to successfully identify the landmarks taking a median time of 26 seconds. After the training session, 76.2% of residents reported that they felt either “confident” or “extremely confident” in identifying the CTM using US. Conclusion The novel anterior neck gel models used in this study were found to be adequate for training EM residents in the US identification of anterior neck anatomy. Residents were successfully trained in identifying the important anterior neck landmarks that are useful when predicting a difficult anterior airway and planning for surgical cricothyrotomy. Residents overall felt that the models simulated the appropriate anatomic scenarios. The majority felt confident in identifying the CTM using US. Cureus 2022-12-27 /pmc/articles/PMC9879590/ /pubmed/36712745 http://dx.doi.org/10.7759/cureus.33003 Text en Copyright © 2022, Acuña et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Emergency Medicine
Acuña, Josie
Pacheco, Garrett
Yarnish, Adrienne A
Andrade, Javier
Haight, Stephen
Coe, Ian
Carter, Jeremy
Adhikari, Srikar
A Novel Simulation Model for Training Emergency Medicine Residents in the Ultrasound Identification of Landmarks for Cricothyrotomy
title A Novel Simulation Model for Training Emergency Medicine Residents in the Ultrasound Identification of Landmarks for Cricothyrotomy
title_full A Novel Simulation Model for Training Emergency Medicine Residents in the Ultrasound Identification of Landmarks for Cricothyrotomy
title_fullStr A Novel Simulation Model for Training Emergency Medicine Residents in the Ultrasound Identification of Landmarks for Cricothyrotomy
title_full_unstemmed A Novel Simulation Model for Training Emergency Medicine Residents in the Ultrasound Identification of Landmarks for Cricothyrotomy
title_short A Novel Simulation Model for Training Emergency Medicine Residents in the Ultrasound Identification of Landmarks for Cricothyrotomy
title_sort novel simulation model for training emergency medicine residents in the ultrasound identification of landmarks for cricothyrotomy
topic Emergency Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879590/
https://www.ncbi.nlm.nih.gov/pubmed/36712745
http://dx.doi.org/10.7759/cureus.33003
work_keys_str_mv AT acunajosie anovelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT pachecogarrett anovelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT yarnishadriennea anovelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT andradejavier anovelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT haightstephen anovelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT coeian anovelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT carterjeremy anovelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT adhikarisrikar anovelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT acunajosie novelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT pachecogarrett novelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT yarnishadriennea novelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT andradejavier novelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT haightstephen novelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT coeian novelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT carterjeremy novelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy
AT adhikarisrikar novelsimulationmodelfortrainingemergencymedicineresidentsintheultrasoundidentificationoflandmarksforcricothyrotomy