Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage

Background and Purpose: Large vessel occlusion (LVO) recognition scales were developed to identify patients with LVO-related acute ischemic stroke (AIS) on the scene of emergency. Thus, they may enable direct transport to a comprehensive stroke centre (CSC). In this study, we aim to validate a smart...

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Autores principales:	Benedikt Frank, Felix Fabian, Bastian Brune, Bessime Bozkurt, Cornelius Deuschl, Raul G. Nogueira, Christoph Kleinschnitz, Martin Köhrmann
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/deb786cd4a724e5d862bc772d8696cef
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spelling	oai:doaj.org-article:deb786cd4a724e5d862bc772d8696cef2021-11-23T23:04:12ZValidation of a shortened FAST-ED algorithm for smartphone app guided stroke triage1756-286410.1177/17562864211057639https://doaj.org/article/deb786cd4a724e5d862bc772d8696cef2021-11-01T00:00:00Zhttps://doi.org/10.1177/17562864211057639https://doaj.org/toc/1756-2864Background and Purpose: Large vessel occlusion (LVO) recognition scales were developed to identify patients with LVO-related acute ischemic stroke (AIS) on the scene of emergency. Thus, they may enable direct transport to a comprehensive stroke centre (CSC). In this study, we aim to validate a smartphone app-based stroke triage with a shortened form of the Field Assessment Stroke Triage for Emergency Destination (FAST-ED). Methods: This retrospective validation study included 2815 patients with confirmed acute stroke and suspected acute stroke but final diagnosis other than stroke (stroke mimics) who were admitted by emergency medical service (EMS) to the CSC of the Neurological University Hospital Essen, Germany. We analysed the predictive accuracy of a shortened digital app-based FAST-ED ( ‘FAST-ED App’) for LVO-related AIS and yield comparison to various other LVO recognition scales. Results: The shortened FAST-ED App had comparable test quality (Area under ROC = 0.887) to predict LVO-related AIS to the original FAST-ED (0.889) and RACE (0.883) and was superior to Cincinnati Prehospital Stroke Severity (CPSS), 3-Item Stroke Scale (3-ISS) and National Institute of Health Stroke Scale (NIHSS). A FAST-ED App ⩾ 4 revealed very good accuracy to detect LVO related AIS (sensitivity of 77% and a specificity 87%) with an area under the curve c-statistics of 0.89 (95% CI: 0.87–0.90). In a hypothetical triage model, the number needed to screen in order to avoid one secondary transportation in an urban setting would be five. Conclusion: This validation study of a shortened FAST-ED assessment for a smartphone-app guided stroke triage yields good quality to identify patients with LVO.Benedikt FrankFelix FabianBastian BruneBessime BozkurtCornelius DeuschlRaul G. NogueiraChristoph KleinschnitzMartin KöhrmannSAGE PublishingarticleNeurology. Diseases of the nervous systemRC346-429ENTherapeutic Advances in Neurological Disorders, Vol 14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neurology. Diseases of the nervous system RC346-429
spellingShingle	Neurology. Diseases of the nervous system RC346-429 Benedikt Frank Felix Fabian Bastian Brune Bessime Bozkurt Cornelius Deuschl Raul G. Nogueira Christoph Kleinschnitz Martin Köhrmann Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage
description	Background and Purpose: Large vessel occlusion (LVO) recognition scales were developed to identify patients with LVO-related acute ischemic stroke (AIS) on the scene of emergency. Thus, they may enable direct transport to a comprehensive stroke centre (CSC). In this study, we aim to validate a smartphone app-based stroke triage with a shortened form of the Field Assessment Stroke Triage for Emergency Destination (FAST-ED). Methods: This retrospective validation study included 2815 patients with confirmed acute stroke and suspected acute stroke but final diagnosis other than stroke (stroke mimics) who were admitted by emergency medical service (EMS) to the CSC of the Neurological University Hospital Essen, Germany. We analysed the predictive accuracy of a shortened digital app-based FAST-ED ( ‘FAST-ED App’) for LVO-related AIS and yield comparison to various other LVO recognition scales. Results: The shortened FAST-ED App had comparable test quality (Area under ROC = 0.887) to predict LVO-related AIS to the original FAST-ED (0.889) and RACE (0.883) and was superior to Cincinnati Prehospital Stroke Severity (CPSS), 3-Item Stroke Scale (3-ISS) and National Institute of Health Stroke Scale (NIHSS). A FAST-ED App ⩾ 4 revealed very good accuracy to detect LVO related AIS (sensitivity of 77% and a specificity 87%) with an area under the curve c-statistics of 0.89 (95% CI: 0.87–0.90). In a hypothetical triage model, the number needed to screen in order to avoid one secondary transportation in an urban setting would be five. Conclusion: This validation study of a shortened FAST-ED assessment for a smartphone-app guided stroke triage yields good quality to identify patients with LVO.
format	article
author	Benedikt Frank Felix Fabian Bastian Brune Bessime Bozkurt Cornelius Deuschl Raul G. Nogueira Christoph Kleinschnitz Martin Köhrmann
author_facet	Benedikt Frank Felix Fabian Bastian Brune Bessime Bozkurt Cornelius Deuschl Raul G. Nogueira Christoph Kleinschnitz Martin Köhrmann
author_sort	Benedikt Frank
title	Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage
title_short	Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage
title_full	Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage
title_fullStr	Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage
title_full_unstemmed	Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage
title_sort	validation of a shortened fast-ed algorithm for smartphone app guided stroke triage
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/deb786cd4a724e5d862bc772d8696cef
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Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage

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