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Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods
BACKGROUND: Stroke has become a leading cause of mortality and adult disability in China. The key to treating acute ischemic stroke (AIS) is to open the obstructed blood vessels as soon as possible and save the ischemic penumbra. However, the thrombolytic rate in China is only 2.5%. Research has bee...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691690/ https://www.ncbi.nlm.nih.gov/pubmed/36438229 http://dx.doi.org/10.3389/fpubh.2022.858926 |
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author | Gao, Zihan Liu, Qinqin Yang, Li Zhu, Xuemei |
author_facet | Gao, Zihan Liu, Qinqin Yang, Li Zhu, Xuemei |
author_sort | Gao, Zihan |
collection | PubMed |
description | BACKGROUND: Stroke has become a leading cause of mortality and adult disability in China. The key to treating acute ischemic stroke (AIS) is to open the obstructed blood vessels as soon as possible and save the ischemic penumbra. However, the thrombolytic rate in China is only 2.5%. Research has been devoted to investigating the causes of prehospital delay, but the exact controllable risk factors for prehospital delay remain uncertain, and a consensus is lacking. We aimed to develop a risk assessment tool to identify the most critical risk factors for prehospital delay for AIS patients. METHODS: From November 2018 to July 2019, 450 patients with AIS were recruited. Both qualitative and quantitative data were collected. The Delphi technique was used to obtain expert opinions about the importance of the risk indices in two rounds of Delphi consultation. Then, we used the risk matrix to identify high-risk factors for prehospital delay for AIS patients. RESULTS: The risk matrix identified the following five critical risk factors that account for prehospital delay after AIS: living in a rural area; no bystanders when stroke occurs; patients and their families lacking an understanding of the urgency of stroke treatment; patients and their families not knowing that stroke requires thrombolysis or that there is a thrombolysis time window; and the patient self-medicating, unaware of the seriousness of the symptoms, and waiting for spontaneous remission. CONCLUSIONS: The risk analysis tool used during this study may help prevent prehospital delays for patients with AIS. |
format | Online Article Text |
id | pubmed-9691690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96916902022-11-26 Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods Gao, Zihan Liu, Qinqin Yang, Li Zhu, Xuemei Front Public Health Public Health BACKGROUND: Stroke has become a leading cause of mortality and adult disability in China. The key to treating acute ischemic stroke (AIS) is to open the obstructed blood vessels as soon as possible and save the ischemic penumbra. However, the thrombolytic rate in China is only 2.5%. Research has been devoted to investigating the causes of prehospital delay, but the exact controllable risk factors for prehospital delay remain uncertain, and a consensus is lacking. We aimed to develop a risk assessment tool to identify the most critical risk factors for prehospital delay for AIS patients. METHODS: From November 2018 to July 2019, 450 patients with AIS were recruited. Both qualitative and quantitative data were collected. The Delphi technique was used to obtain expert opinions about the importance of the risk indices in two rounds of Delphi consultation. Then, we used the risk matrix to identify high-risk factors for prehospital delay for AIS patients. RESULTS: The risk matrix identified the following five critical risk factors that account for prehospital delay after AIS: living in a rural area; no bystanders when stroke occurs; patients and their families lacking an understanding of the urgency of stroke treatment; patients and their families not knowing that stroke requires thrombolysis or that there is a thrombolysis time window; and the patient self-medicating, unaware of the seriousness of the symptoms, and waiting for spontaneous remission. CONCLUSIONS: The risk analysis tool used during this study may help prevent prehospital delays for patients with AIS. Frontiers Media S.A. 2022-11-11 /pmc/articles/PMC9691690/ /pubmed/36438229 http://dx.doi.org/10.3389/fpubh.2022.858926 Text en Copyright © 2022 Gao, Liu, Yang and Zhu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Gao, Zihan Liu, Qinqin Yang, Li Zhu, Xuemei Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods |
title | Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods |
title_full | Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods |
title_fullStr | Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods |
title_full_unstemmed | Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods |
title_short | Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods |
title_sort | identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691690/ https://www.ncbi.nlm.nih.gov/pubmed/36438229 http://dx.doi.org/10.3389/fpubh.2022.858926 |
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