Evidence map›Paper›PMID 36438229›Full record

ArticleFrontiers in public health2022

Identification of high-risk factors for prehospital delay for patients with stroke using the risk matrix methods.

Zihan Gao, Qinqin Liu, Li Yang, Xuemei Zhu

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Zihan GaoSchool of Nursing, Qingdao University, Qingdao, China.
Qinqin LiuSchool of Nursing, Peking University, Beijing, China.
Li YangSchool of Nursing, Qingdao University, Qingdao, China.
Xuemei ZhuSchool of Nursing, Harbin Medical University, Heilongjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Emergency Medical ServicesIschemic StrokeStrokeHumansRisk FactorsTime Factorsacute ischemic strokeBorda countprehospital delayrisk assessmentrisk matrix

Identifiers

PMID36438229
PMCPMC9691690

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.