Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods
BackgroundStroke 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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.858926/full |
| _version_ | 1798019267894968320 |
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| author | Zihan Gao Qinqin Liu Li Yang Xuemei Zhu |
| author_facet | Zihan Gao Qinqin Liu Li Yang Xuemei Zhu |
| author_sort | Zihan Gao |
| collection | DOAJ |
| description | BackgroundStroke 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.MethodsFrom 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.ResultsThe 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.ConclusionsThe risk analysis tool used during this study may help prevent prehospital delays for patients with AIS. |
| first_indexed | 2024-04-11T16:38:05Z |
| format | Article |
| id | doaj.art-2efb48b39e8247548a8876bb972f5f6b |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-11T16:38:05Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-2efb48b39e8247548a8876bb972f5f6b2022-12-22T04:13:44ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-11-011010.3389/fpubh.2022.858926858926Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methodsZihan Gao0Qinqin Liu1Li Yang2Xuemei Zhu3School of Nursing, Qingdao University, Qingdao, ChinaSchool of Nursing, Peking University, Beijing, ChinaSchool of Nursing, Qingdao University, Qingdao, ChinaSchool of Nursing, Harbin Medical University, Heilongjiang, ChinaBackgroundStroke 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.MethodsFrom 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.ResultsThe 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.ConclusionsThe risk analysis tool used during this study may help prevent prehospital delays for patients with AIS.https://www.frontiersin.org/articles/10.3389/fpubh.2022.858926/fullacute ischemic strokeprehospital delayrisk assessmentrisk matrixBorda count |
| spellingShingle | Zihan Gao Qinqin Liu Li Yang Xuemei Zhu Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods Frontiers in Public Health acute ischemic stroke prehospital delay risk assessment risk matrix Borda count |
| 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 | acute ischemic stroke prehospital delay risk assessment risk matrix Borda count |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.858926/full |
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