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A data-driven algorithm to support the clinical decision-making of patient extrication following a road traffic collision

BACKGROUND: Some patients involved in a road traffic collision (RTC) are physically entrapped and extrication is required to provide critical interventions. This can be performed either in an expedited way, or in a more controlled manner. In this study we aimed to derive a data-driven extrication al...

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Autores principales: Vaughan-Huxley, Eyston, Griggs, Joanne, Mohindru, Jasmit, Russell, Malcolm, Lyon, Richard, Avest, Ewoud ter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696863/
https://www.ncbi.nlm.nih.gov/pubmed/38049830
http://dx.doi.org/10.1186/s13049-023-01153-2
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author Vaughan-Huxley, Eyston
Griggs, Joanne
Mohindru, Jasmit
Russell, Malcolm
Lyon, Richard
Avest, Ewoud ter
author_facet Vaughan-Huxley, Eyston
Griggs, Joanne
Mohindru, Jasmit
Russell, Malcolm
Lyon, Richard
Avest, Ewoud ter
author_sort Vaughan-Huxley, Eyston
collection PubMed
description BACKGROUND: Some patients involved in a road traffic collision (RTC) are physically entrapped and extrication is required to provide critical interventions. This can be performed either in an expedited way, or in a more controlled manner. In this study we aimed to derive a data-driven extrication algorithm intended to be used as a decision-support tool by on scene emergency service providers to decide on the optimal method of patient extrication from the vehicle. METHODS: A retrospective observational study was performed of all trauma patients trapped after an RTC who were attended by a Helicopter Emergency Medical Service (HEMS) in the United Kingdom between March 2013 and December 2021. Variables were identified that were associated with the need for HEMS interventions (as a surrogate for the need for expedited extrication), based on which a practical extrication algorithm was devised. RESULTS: During the study period 12,931 patients were attended, of which 920 were physically trapped. Patients who scored an “A” on the AVPU score (n = 531) rarely required HEMS interventions (3%). Those who did were characterised by a shorter than average (29 vs. 37 min) 999/112 emergency call to HEMS on-scene arrival interval. A third of all patients responding to voice required HEMS interventions. Absence of a patent airway (OR 6.98 [1.74–28.03] p < .001) and the absence of palpable radial pulses (OR 9.99 [2.48–40.18] p < .001) were independently associated with the need for (one or more) HEMS interventions in this group. Patients only responding to pain and unresponsive patients almost invariably needed HEMS interventions post extrication (90% and 86% respectively). Based on these findings, a practical and easy to remember algorithm “APEX” was derived. CONCLUSION: A simple, data-driven algorithm, remembered by the acronym “APEX”, may help emergency service providers on scene to determine the preferred method of extrication for patients who are trapped after a road traffic collision. This has the potential to facilitate earlier recognition of a ‘sick’ critical patient trapped in an RTC, decrease entrapment and extrication time, and may contribute to an improved outcome for these patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13049-023-01153-2.
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spelling pubmed-106968632023-12-06 A data-driven algorithm to support the clinical decision-making of patient extrication following a road traffic collision Vaughan-Huxley, Eyston Griggs, Joanne Mohindru, Jasmit Russell, Malcolm Lyon, Richard Avest, Ewoud ter Scand J Trauma Resusc Emerg Med Original Research BACKGROUND: Some patients involved in a road traffic collision (RTC) are physically entrapped and extrication is required to provide critical interventions. This can be performed either in an expedited way, or in a more controlled manner. In this study we aimed to derive a data-driven extrication algorithm intended to be used as a decision-support tool by on scene emergency service providers to decide on the optimal method of patient extrication from the vehicle. METHODS: A retrospective observational study was performed of all trauma patients trapped after an RTC who were attended by a Helicopter Emergency Medical Service (HEMS) in the United Kingdom between March 2013 and December 2021. Variables were identified that were associated with the need for HEMS interventions (as a surrogate for the need for expedited extrication), based on which a practical extrication algorithm was devised. RESULTS: During the study period 12,931 patients were attended, of which 920 were physically trapped. Patients who scored an “A” on the AVPU score (n = 531) rarely required HEMS interventions (3%). Those who did were characterised by a shorter than average (29 vs. 37 min) 999/112 emergency call to HEMS on-scene arrival interval. A third of all patients responding to voice required HEMS interventions. Absence of a patent airway (OR 6.98 [1.74–28.03] p < .001) and the absence of palpable radial pulses (OR 9.99 [2.48–40.18] p < .001) were independently associated with the need for (one or more) HEMS interventions in this group. Patients only responding to pain and unresponsive patients almost invariably needed HEMS interventions post extrication (90% and 86% respectively). Based on these findings, a practical and easy to remember algorithm “APEX” was derived. CONCLUSION: A simple, data-driven algorithm, remembered by the acronym “APEX”, may help emergency service providers on scene to determine the preferred method of extrication for patients who are trapped after a road traffic collision. This has the potential to facilitate earlier recognition of a ‘sick’ critical patient trapped in an RTC, decrease entrapment and extrication time, and may contribute to an improved outcome for these patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13049-023-01153-2. BioMed Central 2023-12-04 /pmc/articles/PMC10696863/ /pubmed/38049830 http://dx.doi.org/10.1186/s13049-023-01153-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Original Research
Vaughan-Huxley, Eyston
Griggs, Joanne
Mohindru, Jasmit
Russell, Malcolm
Lyon, Richard
Avest, Ewoud ter
A data-driven algorithm to support the clinical decision-making of patient extrication following a road traffic collision
title A data-driven algorithm to support the clinical decision-making of patient extrication following a road traffic collision
title_full A data-driven algorithm to support the clinical decision-making of patient extrication following a road traffic collision
title_fullStr A data-driven algorithm to support the clinical decision-making of patient extrication following a road traffic collision
title_full_unstemmed A data-driven algorithm to support the clinical decision-making of patient extrication following a road traffic collision
title_short A data-driven algorithm to support the clinical decision-making of patient extrication following a road traffic collision
title_sort data-driven algorithm to support the clinical decision-making of patient extrication following a road traffic collision
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696863/
https://www.ncbi.nlm.nih.gov/pubmed/38049830
http://dx.doi.org/10.1186/s13049-023-01153-2
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