Analysis of the Factors Intervening in the Prehospital Time in a Stroke Code
Abstract
:1. Introduction
1.1. Concept and Classification of the Stroke Code
1.2. Concept and Classification of Strokes
1.3. Stroke Treatment
2. Materials and Methods
2.1. Information Sources and Search Strategy
2.2. Inclusion Criteria and Exclusion Criteria
2.3. Methodological Evaluation of the Data Used
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
ACV | cerebrovascular accident |
ABVD | basic activities of daily living |
HTA | arterial hypertension |
ECG | electrocardiogram |
EMS | emergency medical services |
NP | prior notification |
PA | primary care |
Appendix A. Selected Scientific Articles Table
Authors; Year | Type of Study | Patients | Objective (O)/Conclusion (C) |
Golden AP and Odoi A; 2015 [14]. | Retrospective observational study | 3900 patients | O: study the factors that produce prehospital delays. C: geographic area affects access time. |
Gorchs-Molist, Montse et al., 2020 [16]. | Observational study | 4000 health professionals | O: determine the impact of training health professionals on the stroke code protocol for early recognition. C: the training of health professionals is crucial to avoid delays in response times. |
Alabdali, Abdullah et al., 2020 [20]. | Prospective cohort study | 220 patients | O: demonstrate that transport in the EMS of patients with suspected stroke shortens the time until activation of the stroke team. C: the objective is met. |
McKinney, James S et al., 2013 [24]. | Comparative study | 229 patients | O: demonstrate that PN to hospitals of cases with suspected strokes by EMS reduces evaluation, diagnostic testing, and treatment times. C: PN in hospitals by EMS in stroke cases reduces response times. |
Soto-Cámara R et al., 2021 [31]. | Systematic review | 101 articles | O: analyze the factors that influenced prehospital time in stroke care. C: some factors decrease delays in prehospital time and others increase them. |
Al Kasab, Sami et al., 2021 [27]. | Comparative study | 101 patients | O: evaluate telestroke consultations in EMS equipped with videoconferencing technology. C: telestroke consultations may be the solution to optimize prehospital stroke triage in rural areas and reduce delays in the administration of thrombolytic and endovascular therapies. |
Seo, Ah Ram et al., 2021 [17]. | Multicenter study | 480 patients | O: check the factors that cause a delay in the activation of the EMS. C: prehospital delay occurs mainly due to the population’s difficulty in recognizing stroke symptoms. |
Naguib, Rania et al., 2020 [19]. | Observational study | 600 patients | O: test whether delay in recognizing stroke warning symptoms and seeking medical advice results in delayed treatment and increased morbidity. C: it is important to raise awareness among the population about stroke and the warning symptoms. |
Gonzalez-Aquines, Alejandro et al., 2019 [21]. | Observational study | 189 patients | O: identify the factors associated with the delay in start-to-alarm time. C: it is necessary to carry out education programs for PC physicians to quickly identify a possible stroke. |
Cáceres, J Alfredo et al., 2013 [26]. | Observational study | 52,282 cases | O: demonstrate the importance of good training for medical dispatchers. C: the use of EMS and prehospital notification by paramedics is crucial to reducing prehospital delay. |
García Ruiz, Rafael et al., 2018 [22]. | Observational study | 328 patients | O: identify the factors that cause prehospital delay in patients with stroke. C: stroke awareness is very important, as is knowing the factors that affect prehospital delay. |
Cooley, S Regan et al., 2021 [28]. | Comparative study | 411 patients | O: demonstrate that stroke units improved response times. C: mobile stroke units facilitate prehospital management. |
Kim, Dae-Hyun et al., 2016 [25]. | Comparative study | 274 patients | O: check the impact of prehospital intervention on the delay time until receiving appropriate treatment. C: the use of EMS and prehospital notification by paramedics is crucial to reducing prehospital delay. |
Belt, Gary H et al., 2016 [29]. | Observational study | 111 patients | O: analyze whether telemedicine during patient transport accelerates the stroke treatment process. C: equipped mobile stroke transport units decrease prehospital delay time |
Andrew, Benjamin Y et al., 2017 [30]. | Comparative study | 2589 cases | O: evaluate the effect of using a mobile coordination application for stroke care. C: the use of smartphone technology improves the care of stroke patients. |
Puolakka, Tuukka et al., 2016 [23]. | Prospective observational study | 308 patients | O: analyze the operation of SME. C: recognition of strokes through calling emergency services is crucial to avoid delays in the prehospital phase. |
Varjoranta, Tuure et al., 2019 [15]. | Retrospective study | 232 patients | O: observe the effect of prehospital and hospital delays in stroke patients. C: continuous training of health professionals is very important to avoid prehospital delays, as well as improve transportation, especially in rural areas. |
Faiz, Kashif W et al., 2019 [18]. | Transversal study | 207 patients | O: evaluate whether knowledge of stroke symptoms and risk factors are associated with early hospital admission. C: knowledge of strokes by the population does not lead to knowing how to act in this case. |
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Studies | Golden and Odoi | Varjorant |
---|---|---|
Sample | 3900 patients. | 232 patients. |
Time Intervals in Rural Environments | Total time: 41 min. Travel time: 23 min | Transport time: 65 min. Door-to-needle time: 45 min. Treatment start time: 194 min. |
Environment Time Interval in Urban Environment Time | Total time: 35 min. Travel time: 15 min. | Transport time: 15 min. Door-to-needle time: 53 min. Treatment start time: 130 min. |
Intervals That Exceed Guidelines in Rural Environments | Total time: 16.4%. Response time: 17.5%. Scene time: 41.5%. | Not applicable. |
Environment Time Intervals That Exceed Guidelines in Urban Environments | Total time: 2.2%. Response time: 21.5%. Scene time: 34.9%. | Not applicable. |
Studies | Sample | SME | Other Transportation Systems |
---|---|---|---|
Alabdali [20] | 220 patients | Activation of the ACV equipment: 8 min. | Activation of the ACV team: 16 min. |
Gonzalez-Aquinas [21] | 189 patients | 22.7% | 65.6% |
García Ruiz [22] | 328 patients | 18.3% | 81.7% |
Puolakka [23] | 308 patients | 7% | 93% |
Studies | Sample | With Prior Notice | Without Prior Notification |
---|---|---|---|
McKinney [24]. | 229 patients | Arrival of the stroke team: 2.5 min. | Arrival of the stroke team: 16.1 min. |
Kim [25]. | 274 patients | Door-to-needle time: 20 min. | Time from door to needle: 29 min. |
Studies | Sample | Activation of Mobile Stroke Units | Non-Activation of Mobile Stroke Units |
---|---|---|---|
Cooley [28]. | 411 patients | Activation start time: 37 min. Time from start to arrival on scene: 19 min. Arrival time to the emergency department: 56 min. | Activation start time: 76 min. Time from start to arrival on scene: 62 min. Arrival time to the emergency department: 121 min. |
Belt [29]. | 111 patients | Needle door time: 28 min. | Needle door time: 41 min. |
Studies | Sample | Reduce Prehospital Time | Increase Prehospital Time |
---|---|---|---|
Soto-Camera [31]. | 110 articles | Recognition of symptoms. Activate SME as first contact. Greater severity of the stroke. Age: being very young or old. Higher educational level. History of CVRF. | Being diabetic. Referral from another health center. Being a woman. Living alone and not having a witness. Living in a rural environment. Contact with the PC doctor first. |
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Share and Cite
Astasio-Picado, Á.; Chueca, Y.C.; López-Sánchez, M.; Lozano, R.R.; González-Chapado, M.T.; Ortega-Trancón, V. Analysis of the Factors Intervening in the Prehospital Time in a Stroke Code. J. Pers. Med. 2023, 13, 1519. https://doi.org/10.3390/jpm13101519
Astasio-Picado Á, Chueca YC, López-Sánchez M, Lozano RR, González-Chapado MT, Ortega-Trancón V. Analysis of the Factors Intervening in the Prehospital Time in a Stroke Code. Journal of Personalized Medicine. 2023; 13(10):1519. https://doi.org/10.3390/jpm13101519
Chicago/Turabian StyleAstasio-Picado, Álvaro, Yolanda Cruz Chueca, Miriam López-Sánchez, Rocio Ruiz Lozano, María Teresa González-Chapado, and Vanesa Ortega-Trancón. 2023. "Analysis of the Factors Intervening in the Prehospital Time in a Stroke Code" Journal of Personalized Medicine 13, no. 10: 1519. https://doi.org/10.3390/jpm13101519
APA StyleAstasio-Picado, Á., Chueca, Y. C., López-Sánchez, M., Lozano, R. R., González-Chapado, M. T., & Ortega-Trancón, V. (2023). Analysis of the Factors Intervening in the Prehospital Time in a Stroke Code. Journal of Personalized Medicine, 13(10), 1519. https://doi.org/10.3390/jpm13101519