Machine learning is a valid method for predicting prehospital delay after acute ischemic stroke

Yang, Li; Liu, Qinqin; Zhao, Qi; Zhu, Xuemei

doi:10.1002/brb3.1794

Cited by 11 publications

(6 citation statements)

References 36 publications

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“…The degree of stroke-related knowledge is closely related to prehospital delay, which has been confirmed in previous studies (Alegiani et al, 2019;Yang et al, 2020). The better the knowledge, the more familiar the subjects are with the premonitory symptoms of stroke and the faster is their reaction speed (Yang et al, 2020). This study confirmed the above views.…”

Section: Discussionsupporting

confidence: 88%

Factors influencing alertness to premonitory symptoms in stroke patients with pre‐hospital delay

Kuang

Zhu

Yang

et al. 2022

Public Health Nursing

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Objective The purpose was to explore the alertness of premonitory symptoms in stroke patients with prehospital delay, and to analyze the influencing factors. Design and Sample A cross‐sectional study using the convenience sampling method was conducted in the neurology department of a general hospital between November 2018 and July 2019. A total of 352 stroke patients were participated in the survey. Measures A hierarchical multiple regression was performed to analyze the factors related to the alertness of premonitory symptoms (0–9 scores) in stroke patients with prehospital delay. Results The alertness score was 6.53 ± 2.377. The lowest score of 0.55 ± 0.498 was for “Continuous yawning occurs continuously despite no tiredness or lack of sleep is okay, and need not be treated.” The hierarchical regression results revealed that symptom onset, symptom change before admission, knowledge, social support were the influencing factors delaying the alertness of premonitory symptoms. Knowledge and support from friends could improve the alertness, while support from family and other support had a notable negative impact. Conclusions Stroke patients need to be more alert toward premonitory symptoms. This alertness is related to stroke knowledge and social support. Nurses should formulate interventions and advise stroke patients to improve their stroke knowledge and expand their social network.

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Section: Discussionsupporting

confidence: 88%

Factors influencing alertness to premonitory symptoms in stroke patients with pre‐hospital delay

Kuang

Zhu

Yang

et al. 2022

Public Health Nursing

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“…Prehospital delay is determined by when the time from the onset of the incidence until the patient's arrival exceeds 3 h (6). Prehospital delay is the main cause of low thrombolysis rate in patients with AIS (7).…”

Section: Introductionmentioning

confidence: 99%

“…Many factors can contribute to the prehospital delay of patients with stroke, including demographic (e.g., age, sex, education, income), clinical (e.g., symptom etiology, symptoms, clinical history, and timing of symptom onset), and cognitivebehavioral (e.g., symptom recognition and perceived severity) factors (6,(8)(9)(10). Various risk factors have been suggested, but controversy persists.…”

Section: Introductionmentioning

confidence: 99%

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

Gao

Liu

Yang

et al. 2022

Front. Public Health

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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.

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“…An example is Goto et al's (46) work with simple, interpretable decision-trees for EMS triage. This solution often, but not always (26,77,96,102,118), results in poorer discrimination compared to more complex methods like neural networks and deep learning (42,62,75). The challenge, then, is appropriately applying AI or non-AI methods in consideration of the clinical context and acceptable limits for performance and interpretability.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

Chee

Huang

et al. 2023

Preprint

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Introduction: The literature on the use of AI in prehospital emergency care (PEC) settings is scattered and diverse, making it difficult to understand the current state of the field. In this scoping review, we aim to provide a descriptive analysis of the current literature and to visualise and identify knowledge and methodological gaps using an evidence map. Methods: We conducted a scoping review from inception until 14 December 2021 on MEDLINE, Embase, Scopus, IEEE Xplore, ACM Digital Library, and Cochrane Central Register of Controlled Trials (CENTRAL). We included peer-reviewed, original studies that applied AI to prehospital data, including applications for cardiopulmonary resuscitation (CPR), automated external defibrillation (AED), out-of-hospital cardiac arrest, and emergency medical service (EMS) infrastructure like stations and ambulances. Results: The search yielded 4350 articles, of which 106 met the inclusion criteria. Most studies were retrospective (n=88, 83.0%), with only one (0.9%) randomised controlled trial. Studies were mostly internally validated (n=96, 90.6%), and only ten studies (9.4%) reported on calibration metrics. While the most studied AI applications were Triage/Prognostication (n=52, 49.1%) and CPR/AED optimisation (n=26, 24.5%), a few studies reported unique use cases of AI such as patient-trial matching for research and Internet-of-Things (IoT) wearables for continuous monitoring. Out of 49 studies that identified a comparator, 39 reported AI performance superior to either clinicians or non-AI status quo algorithms. The minority of studies utilised multimodal inputs (n=37, 34.9%), with few models using text (n=8), audio (n=5), images (n=1), or videos (n=0) as inputs. Conclusion: AI in PEC is a growing field and several promising use cases have been reported, including prognostication, demand prediction, resource optimisation, and IoT continuous monitoring systems. Prospective, externally validated studies are needed before applications can progress beyond the proof-of-concept stage to real-world clinical settings.

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Machine learning is a valid method for predicting prehospital delay after acute ischemic stroke

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 11 publications

References 36 publications

Factors influencing alertness to premonitory symptoms in stroke patients with pre‐hospital delay

Factors influencing alertness to premonitory symptoms in stroke patients with pre‐hospital delay

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

Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

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