Retrospective analysis of stroke code activation in the emergency department of a large tertiary care center in Saudi Arabia

Retrospective analysis of stroke code activation in the emergency department of a large tertiary care center in Saudi Arabia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Retrospective analysis of stroke code activation in the emergency department of a large tertiary care center in Saudi Arabia Abdulaziz Omar AlSebiheen, Muhammad Nauman Qureshi, Asma Waqit AlGhamdi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6697218/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Stroke, a major cerebrovascular disorder with a high mortality that can lead to permanent disability, is the third leading cause of death in Saudi Arabia. Quick recognition of stroke symptoms and initiation of time-sensitive treatment can significantly change the course of stroke, and stroke code activation in the emergency department (ED) can expedite patient management. This study aimed to analyze the stroke code activation protocol against the set hospital standards in the ED of a tertiary care center in Saudi Arabia. Methods The data of patients aged ≥ 14 years who were admitted to the ED between January 2021 and January 2022, for whom the stroke code was activated in the ED, were retrospectively analyzed, and the time intervals from ED triage to stroke code activation, neurologist review, computed tomography (CT) imaging/reporting, and thrombolysis were determined. Results The study included 409 patients with a mean age of 60.12 ± 18.1 years and a mean weight of 73.4 ± 17 kg. Additionally, 61% of the patients were male, 26% of the patients were transported to the ED by ambulance, 63% of the patients were diagnosed with stroke based on CT imaging, and 43% of the patients were managed by mechanical thrombectomy. Furthermore, 91.12% of the patients with stroke had neurologic symptoms whereas 8.89% of the patients with stroke had atypical presentations. The mean time from ED triage to stroke code activation was 44.7 ± 49.6 min, the mean time from code activation to neurologist review was 12.1 ± 28.1 min, and the mean time from code activation to CT imaging was 51.9 ± 38.2 min, respectively. Conclusions Implementing the stroke code protocol in the ED can accelerate the diagnosis and treatment of patients with stroke. Delays in various stages in managing patients with stroke can be resolved with training and robust teamwork. Utilizing ambulance services to transport patients with stroke to appropriate centers can play a key role in expediting care. Ambulances Cause of Death Neurologists Retrospective Studies Tertiary Care Centers Thrombectomy Thrombolytic Therapy Triage Background Stroke, a leading global cause of death and disability, is a major public health concern in Saudi Arabia. The World Health Organization estimates that 15 million people suffer stroke annually and further predict that one-third of the patients with stroke die and that another one-third become permanently disabled. (1). Stroke is a major contributor to severe, long-term neurologic impairment and functional disability [ 1 – 4 ]. Stroke is broadly classified into ischemic and hemorrhagic stroke, which comprise 85% and 15% of the cases, respectively. Risk factors associated with stroke include arterial hypertension, cigarette smoking, diabetes mellitus, hyperlipidemia, older age, human immunodeficiency virus infection, sickle cell disease, and cerebral malaria [ 1 ]. Stroke is a time-sensitive clinical presentation; thus, its management requires rapid and accurate diagnosis with prompt treatment. In emergency department (ED) settings, stroke code protocols have been developed to expedite the diagnosis and treatment of patients with stroke. These protocols involve a coordinated effort by various healthcare professionals, including emergency physicians, neurologists, radiologists, and nurses [ 1 ]. In many hospitals across Saudi Arabia, stroke code protocols have been implemented to improve the quality of care. These protocols have been shown to reduce the time to diagnosis and treatment, which can improve patient outcomes. However, the implementation of stroke code protocols in Saudi Arabia continues facing challenges, including the lack of trained personnel and the limited availability of stroke and rehabilitation centers [ 1 , 4 ]. At King Faisal Specialist Hospital and Research Center in Riyadh, the key components of the stroke code protocol include the rapid identification of stroke symptoms, timely notification of the stroke team, rapid diagnostic workup, and prompt initiation of appropriate treatment. The stroke team includes emergency physicians, neurologists, radiologists, and nurses, all of who are trained in the management of patients with stroke [ 4 ]. The present study aimed to analyze the time spent to complete each component of the stroke code protocol in patients with stroke admitted to the ED of King Faisal Specialist Hospital and Research Center. Methods Study design, setting, and population This was a retrospective study including all patients aged ≥ 14 years for whom the stroke code was activated in the ED of King Faisal Specialist Hospital and Research Center between January 2021 and January 2022. Patients transferred from another hospital and those with stroke symptoms lasting more than 24 h were excluded. Data collection The patient data were collected from the hospital’s medical records and included data on demographics, mode of transportation, time of presentation at the ED, vital signs, time of stroke code activation, time of neurologist review, time of CT imaging, the National Institutes of Health Stroke Scale (NIHSS) score, hospital length of stay, and mortality. Statistical analysis Data were analyzed using SPSS (version 26.0; IBM, Armonk, NY, USA). Categorical variables were presented as numbers with percentages, normally distributed continuous variables were presented as means with standard deviation, and no normally distributed continuous variables were presented as medians with interquartile range (IQR). The chi-square test was used to determine the association between the demographic variables and study outcomes ( mortality rate among stroke patients ) terms of meeting our hospital standards for stroke code protocol activation as primary outcome, and EMS utilization among suspected stroke patients and to determine the distribution of categorical variables within groups. The Chi-square test was used when at least 80% of the expected counts are 5 or more. If the counts are below 5, especially in small samples or rare cases, Fisher's exact test should be used instead. A p value of < 0.05 were considered to indicate statistical significance. The normality of distribution was evaluated for all continuous variables used Shapiro–Wilk test. Two-group comparisons for no normally distributed continuous variables were performed using the Mann–Whitney test., and two-group comparisons for normally distributed continuous variables were performed using the independent-samples t test. Results The study cohort included 409 patients who met the inclusion criteria, including 259 patients (63%) with confirmed stroke based on computed tomography (CT) results. Additionally, 91.2% of the patients with confirmed stroke presented with neurologic symptoms whereas the remaining 8.8% had atypical symptoms. The mean time from ED triage to stroke code activation was 44.7 ± 49.6 min, the mean times from code activation to neurologist review was 12.1 ± 28.1 min, and the mean time from code activation to CT imaging was 51.9 ± 38.2 min, respectively. The mean age was 60.1 ± 18.1 years, the mean weight was 73.4 ± 17 kg, and the median length of hospital stay was 5 (2) days. Additionally, 61% of the patients were males and 26% of the patients were transported by ambulance. The mean temperature was 36.7°C ± 1.8°C, the mean heart rate was 87.4 ± 21.1 beats/min, the mean systolic and blood pressures were 133.5 ± 27.1 and 75.8 ± 14.6 mm Hg, respectively, and the mean respiratory rate was 20.3 ± 3.5 breaths/min (Table 1 ). Table 1 Basic characteristics of patients for whom the stroke code was activated: Age (Year) Mean (± SD) 60.1 (± 18.1) Median (IQR) 63 (23) Weight (Kg) Mean (± SD) 73.4 (± 17) Length of stay (Day) Mean (± SD) 16.1 (± 175.4) Median (IQR) 5 (2) Parameters Category Total Count (n = 409) Percentage Gender Male 251 61.4 Female 158 38.6 Transport Family or relative 115 28.1 Ambulance 106 25.9 Wheelchair 105 25.7 Walking 61 14.9 Other 22 5.4 Positive for stroke Yes 259 63.3 Number of positive for stroke in patients with: Neurological symptoms 236 91.1 Non-neurological symptoms 23 8.8 Type of management (n = 79) Mechanical thrombectomy 34 43 Tissue plasminogen activator 10 12.7 Both 6 7.6 Other 29 36.7 Vital signs of the patients: Temperature (C°) Mean (± SD) 36.7 (± 1.8) Median (IQR) 36.8 (0.4) Heart rate (beats per minute) Mean (± SD) 87.35 (± 21.1) Median (IQR) 84 (25) Systolic blood pressure (mm Hg) Mean (± SD) 133.5 (± 27.1) Diastolic blood pressure (mm Hg) Mean (± SD) 75.8 (± 14.6) Median (IQR) 76 (17) Respiratory rate (breaths per minute) Mean (± SD) 20.3 (± 3.5) Median (IQR) 20 (3) Table 2 showed the timeline of stroke code activation pathway started from triage to activation time which was 44.7 (± 49.6) minutes, from activation to examination by neurology 12.1 (± 28.1) minutes and from activation to performing CT imagine was 51.9 (± 38.2) minutes. Table 2 Time taken in all processes of the stroke code (n = 166) Time from triage to code activation (minute) Mean (± SD) Median (IQR) 44.7 (± 49.6) 30.00 (32) Time from activation to examination by neurology (minute) Mean (± SD) Median (IQR) 12.1 (± 28.1) 0 (14) Time from code activation to CT imaging (minute) Mean (± SD) Median (IQR) 51.9 (± 38.2) 40 (47) The mean NIHSS scores before and after treatment and at discharge were 8.4 ± 7, 6.2 ± 5.6, and 4.8 ± 4, respectively (Table 3 ). Table 3 Total Scores of National Institutes of Health Stroke Scale (NIHSS) Pre-Treatment NIHSS score Mean (± SD) Median (IQR) 8.4 (± 7) 6 (12) Post Treatment NIHSS score Mean (± SD) Median (IQR) 6.16 (± 5.6) 5 (18) NIHSS score upon Discharge Mean (± SD) 4.8 (± 4) In total, 68.2% of the patients with stroke were discharged with approval, and 9.6% died. (Table 4 ) Table 4 Outcome of the stroke among patients. Parameters Category Total Count (n = 408) Percentage Discharge disposition (n = 409) Discharged with approval 279 68.2 Discharged against advice 15 3.7 Discharged for other reason 76 18.6 Deceased 39 9.6 Mortality (n = 409) Yes 39 9.6 No 370 90.4 The analysis of the association between demographic factors and mortality rate in patients with stroke revealed that sex, age, weight, and stroke diagnosis were significantly associated with mortality (Table 6 ). Briefly, the mortality rate was higher in male patients than in female patients (12% versus 5.7%, p = 0.038) and in those without stroke than in those with stroke (12.1% versus 4.3%, p = 0.015). The mean age was significantly higher in patients who died than in those who survived (67 [IQR, 20] versus 62 years [IQR 24], p = 0.037). In addition, the mean weight was significantly higher in patients who died than in those who survived (81.8 ± 18.2 versus 72.8 ± 16.9 kg, p = 0.034). (Table 5 ) Table 5 Effect of demographic factors on mortality rate among stroke patients Factors Mortality P-value Categories Yes No Count % Count % Gender Male 31 12 221 88 0.038* Female 9 5.7 148 94.3 Transport Ambulance 14 13.2 92 86.8 0.138 Private transportation 26 8.5 277 91.7 Positive for stroke Yes 7 4.3 156 95.7 0.015* No 14 12.1 102 87.8 Factors Mortality P-value Yes No Median (IQR) Median (IQR) Age (Year) 67 (20) 62 (24) 0.037* Length of stay (Day) 5 (3) 5 (2) 0.332 Temperature (C°) 36.8 (0.7) 36.8 (0.4) 0.533 Heart rate (beats per minute) 88 (26.8) 84 (24.5) 0.290 Respiratory rate (Breath per minute) 20 (6) 20 (2) 0.978 Diastolic Blood pressure (mm Hg) 72 (19) 76 (17) 0.449 Factors Mortality P-value Yes No Mean (± SD) Mean (± SD) Weight (Kg) 81.8 (± 18.2) 72.8 (± 16.9) 0.034* Systolic Blood pressure (mm Hg) 132.7 (± 34.5) 133.6 (± 26.5) 0.880 The association between demographic factors and mortality rate in stroke patients was calculated. Gender, age, weight, and being positive for stroke were the statistically significant factors. The mortality rate among males (12%) was higher than females (5.7%) (p-value = 0.038). On the other hand, the mortality rate among patients with positive stroke results (4.3%) was lower than those with negative results (12.1%) (p-value = 0.015). The patients who died had a higher median age of 67 years (IQR of 20), while the surviving patients had a median age of 62 years (IQR of 24) (p-value = 0.037). In addition, patients who died had a mean weight of 81.8 kg (± 18.2), which was significantly higher than the mean weight of patients still alive, which was 72.8 kg (± 16.9) (p-value = 0.034). All details are in Table 6 . Table 6 Effect of demographic factors on mortality rate among stroke patients Factors Mortality P-value Categories Yes No Count % Count % Gender Male 31 12 221 88 0.038* Female 9 5.7 148 94.3 Transport Ambulance 14 13.2 92 86.8 0.138 Private transportation 26 8.5 277 91.7 Positive for stroke Yes 7 4.3 156 95.7 0.015* No 14 12.1 102 87.8 Factors Mortality P-value Yes No Median (IQR) Median (IQR) Age (Year) 67 (20) 62 (24) 0.037* Length of stay (Day) 5 (3) 5 (2) 0.332 Temperature (C°) 36.8 (0.7) 36.8 (0.4) 0.533 Heart rate (beats per minute) 88 (26.8) 84 (24.5) 0.290 Respiratory rate (Breath per minute) 20 (6) 20 (2) 0.978 Diastolic Blood pressure (mm Hg) 72 (19) 76 (17) 0.449 Factors Mortality P-value Yes No Mean (± SD) Mean (± SD) Weight (Kg) 81.8 (± 18.2) 72.8 (± 16.9) 0.034* Systolic Blood pressure (mm Hg) 132.7 (± 34.5) 133.6 (± 26.5) 0.880 The following table (Table 7 ) outlines the King Faisal Specialist Hospital and Research Centre's Stroke Code standards, detailing each step in the management of a suspected stroke patient—from Emergency Department triage to the final decision on definitive treatment by the neurology team, whether thrombolysis or mechanical thrombectomy—presented in chronological order. Table 7 KFSHRC Stroke Code Standards by Area and Time Intervals Area Time (Duty) ED Triage 0–10 minutes (Notifying ED physician) ED Acute Care/Resus 10–25 minutes (Notifying Neurologist On-call) Radiology 25–45 minutes (Perform STAT CT/CTA Brain) ED (Neurologist Decision) 45–60 minutes (Management: Thrombolytic vs Thrombectomy) Angio Suite 60–120 minutes (Intervention to perform mechanical thrombectomy) Discussion Stroke is one of the leading causes of disability and death worldwide [ 7 ]. Timely intervention is crucial in acute stroke management, and prompt treatment is associated with better patient outcomes, including lower rates of symptomatic intracranial hemorrhage, better discharge destinations, and lower in-hospital mortality [ 7 ]. In the ED, stroke code protocols are deployed to expedite the diagnosis and treatment of patients with stroke [ 6 ] and delays in stroke management can be attributed to several factors, including delays in seeking medical attention, diagnosis, and treatment initiation. In some cases, delays may be due to system-level factors, such as inadequate resources and inefficient processes [ 7 ]. Our analysis of the specific stroke code times (Table 2 ) revealed a significant delay in the time from ED triage to code activation compared to the standard time ( Table 7 ), which might be due to the high number of patients presenting with no neurologic symptoms, such as body weakness, unwitnessed fall, and syncope (Table 1 ). Additional factors which might have contributed to the observed delay include failure to recognize stroke symptoms during triage, language barriers, preexisting neurologic conditions, such as dementia, and other associated symptoms taking priority, such as chest pain. In our hospital, neurologists take priority over ED physicians in activating the stroke code, which might have also contributed to the observed delay. However, we did not observe an association between the time from ED triage to code activation and the mortality rate. In the present study, 61.4% of the patients were males, consistent with other studies in Saudi Arabia, with one study reporting a male incidence of 66% [ 4 ]. Another study by Alhazzani et al. reported that 65% of the patients with stroke were males [ 10 ]. This finding might be associated with the higher prevalence of vascular risk factors in male patients. Studies in China reported a higher incidence and mortality rate among males with stroke [ 7 , 8 ], whereas Yim et al. reported a 54% of males have stroke [ 1 ]. A Canadian study by Wan et al. reported a sex difference in the rate of hospitalizations and ED visits, with event rates of 292.2 and 281.3 per 100,000 visits for male and female patients, respectively, although they did not observe other significant disparities between the sexes [ 2 ]. Another comprehensive province-wide cohort study in Canada revealed no discernible disparities between sexes [ 3 ]. In a study from Spain, the incidence rate 55.7% of the patients with stroke were males [ 4 ]. Additionally, the average age for the first stroke event was higher in females than in males (79.07 ± 11.96 versus 72.47 ± 12.48 years). Therefore, the current evidence strongly suggests that the sex disparity in the rate of patients presenting to the ED with stroke varies across countries, highlighting the importance of considering regional factors in understanding healthcare patterns related to stroke incidence. Age is a critical risk factor for stroke [ 13 , 14 ]. In the current cohort, the mean age of the patients with confirm stroke was similar to that reported in a previous study (61–70 years) [ 9 ]. Moreover, a study in China highlighted the critical role of age in stroke incidence [ 11 ]. Ekker et al. described an exponential increase in stroke incidence with increasing age in patients older than 35 years. [ 5 ]. another study reported similar findings, indicating highest stroke incidence in individuals aged older than 65 years [ 6 ]. Additionally, this study observed a rise in stroke incidence in individuals aged 25–44 years. In contrast to the prior study, however, Alhazani et al., identified an increase in stroke incidence among individuals aged 45–64 years. We also found older age as a significant factor associated with mortality, in agreement with a study by He et al., who reported that older age was associated with a higher risk of in-hospital mortality [ 10 ]. The observed association of older age with stroke might be attributed to the higher rates of neurologic and non-neurologic complications of stroke in older patients [ 7 , 8 ]. Increased body mass index (BMI) is associated with a higher all-cause mortality in the general population [ 13 ]. In the present study, weight was significantly associated with mortality in patients with stroke. This has been specifically attributed to the increase in stroke incidence in the younger population, as reported in a case-control study of stroke incidence and mortality among patients under 45 years of age with central obesity across 32 countries [ 9 ]. Jo et al. also reported obesity as a significant risk factor in this age group, which had an obesity prevalence of 44.8% [ 10 ]. Conversely, recent studies have reported improved mortality in patients with a higher BMI, illustrating the “obesity paradox.” The National Institutes of Health FAST-MAG (Field Administration of Stroke Therapy–Magnesium) acute stroke trial revealed that a high BMI was associated with a consistent increase in survival rates, showing that the relationship of BMI with disability and stroke-related quality of life followed a U- or J-shaped pattern, indicating decreased survival with low or very high BMIs [ 11 ]. Aparicio et al. reported similar findings; they found that 10-year survival rates after stroke were better in patients classified as mildly obese or overweight than in those with normal weight [ 15 ]. However, other studies disagree with these conclusions, attributing the results to potential confounders, such as age, sex, smoking, and obesity phenotypes [ 12 , 13 ]. Several studies have reported that the use of ambulance services was associated with earlier arrival for care [ 13 , 14 ]. In the present study, only 25.9% of the patients arrived via ambulance, highlighting the underutilization of emergency medical services (EMS) for the transportation of patients with stroke in Saudi Arabia. Indeed, one study reported that only 34.1% of the Saudi population utilized EMS [ 5 ] whereas another study found that 18.5% of the patients with stroke used ambulance services after the onset of symptoms [ 13 ]. Prenotification by EMS has also been associated with decreased in-hospital mortality in patients with stroke [ 14 ]. It is evident that the majority of patients with stroke remain dependent on private transportation, consistent with the general population practices regarding stroke in North Africa and the Middle East [ 13 ]. Several studies found that the failure of family members in recognizing stroke symptoms led to delays in ED presentation and subsequent diagnosis [ 13 , 14 , 17 ]. Stroke can lead to serious adverse outcomes; therefore, patients with stroke should be prioritized, particularly in emergency settings where stroke management yields the best outcomes [ 13 ]. In the present study, mechanical thrombectomy was the prevalent type of management used in 43% of the patients whereas tissue plasminogen activator was used in 12.7% of the patients [ 16 ]. Due to the retrospective study design and the presence of incomplete data, we could not determine whether stroke management was effective in improving the NIHSS score. We acknowledge the limitations of our study. The retrospective study design introduced the risk of incomplete or missing information. Additionally, the study was conducted in a single center, limiting its generalizability to other healthcare settings. A multicenter study is warranted to more comprehensively and reliably evaluate the impact of the stroke code activation protocol on patient outcomes and to determine factors contributing to delays in its activation. We acknowledge that the data presented in the current study, which covers the period 2021–2022, may not fully reflect the present-day efficiency and responsiveness of stroke code activation protocols, considering the advancements and system improvements implemented since then. To address this limitation, we plan to conduct a follow-up study covering the 2025–2026 period. This will allow for a more accurate evaluation of contemporary stroke code activation performance and its alignment with current standards of care. Conclusions Implementing stroke code activation protocols in the ED can accelerate the diagnosis and treatment of patients with stroke. Delays encountered due to the lack of symptom recognition in patients with stroke presenting to the ED highlight areas that can benefit from training of the frontline triage staff. Activation of the stroke code by the ED physicians instead of the neurologists may improve patient outcomes. The underutilization of ambulance services in transporting patients with stroke in Saudi Arabia should be addressed by increasing public awareness. Abbreviations AA Abolyazid AY BMI Body mass index CT Computed tomography ED Emergency department EMS Emergency medical services HJ Himali JJ NIHSS National Institutes of Health Stroke Scale Declarations Ethics approval and consent to participate Informed consent was obtained and ethically approved from King Faisal hospital and research center #RAC: 2221151 Consent for publication The paper have been approved by an appropriate King Faisal hospital and research center ethics committee. Availability of data and materials The raw data supporting the conclusions of this article will be made available by the authors on request Competing interests No financial support or funding was received from private entities or international parties. Funding This research was conducted without any external funding Authors’ contributions Abdulaziz Omar AlSebiheen: Conceptualization, methodology, ethical approval writing-original draft, supervision, manuscript writing. Muhammad Nauman Qureshi: Methodology & discussion writing, writing - reviewing and editing of the manuscript. Asma Waqit AlGhamdi: Software – Data curation, Data review and editing, manuscript writing Ahmed Gamal Syed: Data collection, supervision of co-authors progress, manuscript writing Raghad Mohammed Hijazi: Data collection, manuscript writing Jibran Ahmed Khan: Data collection, manuscript writing Ohoud Turki Alsudairi: Data collection Aya Arwadi: Data collection Mohammed Bassel AlSarraj: Co- Conceptualization, manuscript reviewing. References Asirvatham AR, Marwan MZ. Stroke in Saudi Arabia: a review of the recent literature. Pan Afr Med J. 2014;17:14. Jowi JO, Mativo PM. Pathological sub-types, risk factors and outcome of stroke at the Nairobi Hospital, Kenya. East Afr Med J. 2008;85:572-81. Benamer HT, Grosset D. Stroke in Arab countries: a systematic literature review. J Neurol Sci. 2009;284:18-23. Alahmari K, Paul SS. Prevalence of stroke in Kingdom of Saudi Arabia-through a physiotherapist diary. Mediterr J Soc Sci. 2016;7:228. 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Aparicio HJ, Himali JJ, Beiser AS, Davis‐Plourde KL, Vasan RS, Kase CS, et al. Overweight, obesity, and survival after stroke in the Framingham heart study. J Am Heart Assoc. 2017;6:e004721. Nedeltchev K, Arnold M, Brekenfeld C, Isenegger J, Remonda L, Schroth G, et al. Pre- and in-hospital delays from stroke onset to intra-arterial thrombolysis. Stroke. 2003;34:1230-4. Williams LS, Bruno A, Rouch D, Marriott DJ, MAS. Stroke patients’ knowledge of stroke: influence on time to presentation. Stroke. 1997;28:912-5. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6697218","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467045349,"identity":"e1a6c16f-9e10-4f0f-9db1-be5e8b07ac3a","order_by":0,"name":"Abdulaziz Omar AlSebiheen","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Abdulaziz","middleName":"Omar","lastName":"AlSebiheen","suffix":""},{"id":467045350,"identity":"4963b7c8-a1de-4a16-af02-cf9c82320381","order_by":1,"name":"Muhammad Nauman Qureshi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYHACNgh1vI3hA2MDmGlApJYzxxhnkKjlRhqRWviunTF78DNnmzzfzWeJjT933JNnYG/ewPijBrcWyds55oa9224bzryddrCZ90yxYQPPsQJmnmO4tRjczjGT4N12m3HD7fT2x4xtCQkMEjkGzDDX4tIi+XfbbfsNN483Nv4EaZF/Y8D44x9+LdJAWxI33GA72MALtoXHgIG3DZ9f0sqkZbfdTp55Ji2xGajFsI0nreAwbx9uLXy3k7dJvt1227bv+DFDkMPk+dkPb3z44xtuLQwH0AXYsAni1zIKRsEoGAWjAB0AAFJIWnnwjlhGAAAAAElFTkSuQmCC","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Nauman","lastName":"Qureshi","suffix":""},{"id":467045351,"identity":"ab55ef30-a6a1-4bf6-9093-c4aa4d0dc067","order_by":2,"name":"Asma Waqit AlGhamdi","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Asma","middleName":"Waqit","lastName":"AlGhamdi","suffix":""},{"id":467045353,"identity":"e7d59090-52be-4647-aa4a-0c019f4efa41","order_by":3,"name":"Ahmed Gamal Sayed","email":"","orcid":"","institution":"Alfaisal University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Gamal","lastName":"Sayed","suffix":""},{"id":467045354,"identity":"b72be130-9e2f-4137-a8ff-3e3d3197ba05","order_by":4,"name":"Raghad Mohammed Hijazi","email":"","orcid":"","institution":"Alfaisal University","correspondingAuthor":false,"prefix":"","firstName":"Raghad","middleName":"Mohammed","lastName":"Hijazi","suffix":""},{"id":467045355,"identity":"221b0fb2-d871-420a-97c2-204bda3ef8bc","order_by":5,"name":"Jibran Ahmed Khan","email":"","orcid":"","institution":"Alfaisal University","correspondingAuthor":false,"prefix":"","firstName":"Jibran","middleName":"Ahmed","lastName":"Khan","suffix":""},{"id":467045356,"identity":"92ed0c58-7ad7-4971-8e67-fec17c2b613f","order_by":6,"name":"Ohoud Turki Alsudairi","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Ohoud","middleName":"Turki","lastName":"Alsudairi","suffix":""},{"id":467045357,"identity":"dd6563dd-39df-49ef-ae4e-e5348f7fb295","order_by":7,"name":"Aya Arwadi","email":"","orcid":"","institution":"Alfaisal University","correspondingAuthor":false,"prefix":"","firstName":"Aya","middleName":"","lastName":"Arwadi","suffix":""},{"id":467045358,"identity":"f6150df2-8574-4a3d-a54b-97188634fd5f","order_by":8,"name":"Mohammed Bassel AlSarraj","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Bassel","lastName":"AlSarraj","suffix":""}],"badges":[],"createdAt":"2025-05-19 09:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6697218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6697218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88747121,"identity":"3ed17685-4fe1-4b98-a00d-14588aef480f","added_by":"auto","created_at":"2025-08-11 04:46:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1121897,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6697218/v1/59d96c16-28a8-4a46-bea1-2e297c0576b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Retrospective analysis of stroke code activation in the emergency department of a large tertiary care center in Saudi Arabia","fulltext":[{"header":"Background","content":"\u003cp\u003eStroke, a leading global cause of death and disability, is a major public health concern in Saudi Arabia. The World Health Organization estimates that 15\u0026nbsp;million people suffer stroke annually and further predict that one-third of the patients with stroke die and that another one-third become permanently disabled. (1). Stroke is a major contributor to severe, long-term neurologic impairment and functional disability [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Stroke is broadly classified into ischemic and hemorrhagic stroke, which comprise 85% and 15% of the cases, respectively. Risk factors associated with stroke include arterial hypertension, cigarette smoking, diabetes mellitus, hyperlipidemia, older age, human immunodeficiency virus infection, sickle cell disease, and cerebral malaria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStroke is a time-sensitive clinical presentation; thus, its management requires rapid and accurate diagnosis with prompt treatment. In emergency department (ED) settings, stroke code protocols have been developed to expedite the diagnosis and treatment of patients with stroke. These protocols involve a coordinated effort by various healthcare professionals, including emergency physicians, neurologists, radiologists, and nurses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn many hospitals across Saudi Arabia, stroke code protocols have been implemented to improve the quality of care. These protocols have been shown to reduce the time to diagnosis and treatment, which can improve patient outcomes. However, the implementation of stroke code protocols in Saudi Arabia continues facing challenges, including the lack of trained personnel and the limited availability of stroke and rehabilitation centers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. At King Faisal Specialist Hospital and Research Center in Riyadh, the key components of the stroke code protocol include the rapid identification of stroke symptoms, timely notification of the stroke team, rapid diagnostic workup, and prompt initiation of appropriate treatment. The stroke team includes emergency physicians, neurologists, radiologists, and nurses, all of who are trained in the management of patients with stroke [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study aimed to analyze the time spent to complete each component of the stroke code protocol in patients with stroke admitted to the ED of King Faisal Specialist Hospital and Research Center.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, setting, and population\u003c/h2\u003e \u003cp\u003eThis was a retrospective study including all patients aged\u0026thinsp;\u0026ge;\u0026thinsp;14 years for whom the stroke code was activated in the ED of King Faisal Specialist Hospital and Research Center between January 2021 and January 2022. Patients transferred from another hospital and those with stroke symptoms lasting more than 24 h were excluded.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe patient data were collected from the hospital\u0026rsquo;s medical records and included data on demographics, mode of transportation, time of presentation at the ED, vital signs, time of stroke code activation, time of neurologist review, time of CT imaging, the National Institutes of Health Stroke Scale (NIHSS) score, hospital length of stay, and mortality.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS (version 26.0; IBM, Armonk, NY, USA). Categorical variables were presented as numbers with percentages, normally distributed continuous variables were presented as means with standard deviation, and no normally distributed continuous variables were presented as medians with interquartile range (IQR).\u003c/p\u003e \u003cp\u003eThe chi-square test was used to determine the association between the demographic variables and study outcomes (\u003cb\u003emortality rate among stroke patients\u003c/b\u003e) terms of meeting our hospital standards for stroke code protocol activation as primary outcome, and EMS utilization among suspected stroke patients and to determine the distribution of categorical variables within groups. The Chi-square test was used when at least 80% of the expected counts are 5 or more. If the counts are below 5, especially in small samples or rare cases, Fisher's exact test should be used instead. A p value of \u0026lt;\u0026thinsp;0.05 were considered to indicate statistical significance. The normality of distribution was evaluated for all continuous variables used Shapiro\u0026ndash;Wilk test. Two-group comparisons for no normally distributed continuous variables were performed using the Mann\u0026ndash;Whitney test., and two-group comparisons for normally distributed continuous variables were performed using the independent-samples \u003cem\u003et\u003c/em\u003e test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study cohort included 409 patients who met the inclusion criteria, including 259 patients (63%) with confirmed stroke based on computed tomography (CT) results. Additionally, 91.2% of the patients with confirmed stroke presented with neurologic symptoms whereas the remaining 8.8% had atypical symptoms.\u003c/p\u003e \u003cp\u003eThe mean time from ED triage to stroke code activation was 44.7\u0026thinsp;\u0026plusmn;\u0026thinsp;49.6 min, the mean times from code activation to neurologist review was 12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;28.1 min, and the mean time from code activation to CT imaging was 51.9\u0026thinsp;\u0026plusmn;\u0026thinsp;38.2 min, respectively.\u003c/p\u003e \u003cp\u003eThe mean age was 60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1 years, the mean weight was 73.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17 kg, and the median length of hospital stay was 5 (2) days. Additionally, 61% of the patients were males and 26% of the patients were transported by ambulance.\u003c/p\u003e \u003cp\u003eThe mean temperature was 36.7\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u0026deg;C, the mean heart rate was 87.4\u0026thinsp;\u0026plusmn;\u0026thinsp;21.1 beats/min, the mean systolic and blood pressures were 133.5\u0026thinsp;\u0026plusmn;\u0026thinsp;27.1 and 75.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6 mm Hg, respectively, and the mean respiratory rate was 20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 breaths/min (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of patients for whom the stroke code was activated:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (Year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e60.1 (\u0026plusmn;\u0026thinsp;18.1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e63 (23)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e73.4 (\u0026plusmn;\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLength of stay (Day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16.1 (\u0026plusmn;\u0026thinsp;175.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCategory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTotal Count (n\u0026thinsp;=\u0026thinsp;409)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePercentage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTransport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily or relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmbulance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWheelchair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWalking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePositive for stroke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e259\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e63.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of positive for stroke in patients with:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurological symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-neurological symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eType of management (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanical thrombectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTissue plasminogen activator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVital signs of the patients:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTemperature (C\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e36.7 (\u0026plusmn;\u0026thinsp;1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e36.8 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHeart rate (beats per minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e87.35 (\u0026plusmn;\u0026thinsp;21.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e84 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e133.5 (\u0026plusmn;\u0026thinsp;27.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDiastolic blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e75.8 (\u0026plusmn;\u0026thinsp;14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e76 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRespiratory rate (breaths per minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20.3 (\u0026plusmn;\u0026thinsp;3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20 (3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the timeline of stroke code activation pathway started from triage to activation time which was 44.7 (\u0026plusmn;\u0026thinsp;49.6) minutes, from activation to examination by neurology 12.1 (\u0026plusmn;\u0026thinsp;28.1) minutes and from activation to performing CT imagine was 51.9 (\u0026plusmn;\u0026thinsp;38.2) minutes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime taken in all processes of the stroke code (n\u0026thinsp;=\u0026thinsp;166)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from triage to code activation (minute)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.7 (\u0026plusmn;\u0026thinsp;49.6)\u003c/p\u003e \u003cp\u003e30.00 (32)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from activation to examination by neurology (minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1 (\u0026plusmn;\u0026thinsp;28.1)\u003c/p\u003e \u003cp\u003e0 (14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from code activation to CT imaging (minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.9 (\u0026plusmn;\u0026thinsp;38.2)\u003c/p\u003e \u003cp\u003e40 (47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean NIHSS scores before and after treatment and at discharge were 8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7, 6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6, and 4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal Scores of National Institutes of Health Stroke Scale (NIHSS)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-Treatment NIHSS score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.4 (\u0026plusmn;\u0026thinsp;7)\u003c/p\u003e \u003cp\u003e6 (12)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost Treatment NIHSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.16 (\u0026plusmn;\u0026thinsp;5.6)\u003c/p\u003e \u003cp\u003e5 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS score upon\u003c/p\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8 (\u0026plusmn;\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn total, 68.2% of the patients with stroke were discharged with approval, and 9.6% died. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutcome of the stroke among patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Count (n\u0026thinsp;=\u0026thinsp;408)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDischarge disposition (n\u0026thinsp;=\u0026thinsp;409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDischarged with approval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDischarged against advice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDischarged for other reason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeceased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMortality (n\u0026thinsp;=\u0026thinsp;409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis of the association between demographic factors and mortality rate in patients with stroke revealed that sex, age, weight, and stroke diagnosis were significantly associated with mortality (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Briefly, the mortality rate was higher in male patients than in female patients (12% versus 5.7%, p\u0026thinsp;=\u0026thinsp;0.038) and in those without stroke than in those with stroke (12.1% versus 4.3%, p\u0026thinsp;=\u0026thinsp;0.015). The mean age was significantly higher in patients who died than in those who survived (67 [IQR, 20] versus 62 years [IQR 24], p\u0026thinsp;=\u0026thinsp;0.037). In addition, the mean weight was significantly higher in patients who died than in those who survived (81.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.2 versus 72.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9 kg, p\u0026thinsp;=\u0026thinsp;0.034). (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of demographic factors on mortality rate among stroke patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.038*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTransport\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmbulance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate transportation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePositive for stroke\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (Year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e67 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e62 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLength of stay (Day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTemperature (C\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e36.8 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e36.8 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHeart rate (beats per minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e88 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e84 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRespiratory rate (Breath per minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e20 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiastolic Blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e72 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e76 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWeight (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e81.8 (\u0026plusmn;\u0026thinsp;18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e72.8 (\u0026plusmn;\u0026thinsp;16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSystolic Blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e132.7 (\u0026plusmn;\u0026thinsp;34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e133.6 (\u0026plusmn;\u0026thinsp;26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe association between demographic factors and mortality rate in stroke patients was calculated. Gender, age, weight, and being positive for stroke were the statistically significant factors. The mortality rate among males (12%) was higher than females (5.7%) (p-value\u0026thinsp;=\u0026thinsp;0.038). On the other hand, the mortality rate among patients with positive stroke results (4.3%) was lower than those with negative results (12.1%) (p-value\u0026thinsp;=\u0026thinsp;0.015). The patients who died had a higher median age of 67 years (IQR of 20), while the surviving patients had a median age of 62 years (IQR of 24) (p-value\u0026thinsp;=\u0026thinsp;0.037). In addition, patients who died had a mean weight of 81.8 kg (\u0026plusmn;\u0026thinsp;18.2), which was significantly higher than the mean weight of patients still alive, which was 72.8 kg (\u0026plusmn;\u0026thinsp;16.9) (p-value\u0026thinsp;=\u0026thinsp;0.034). All details are in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of demographic factors on mortality rate among stroke patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.038*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTransport\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmbulance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate transportation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePositive for stroke\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (Year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e67 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e62 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLength of stay (Day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTemperature (C\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e36.8 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e36.8 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHeart rate (beats per minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e88 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e84 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRespiratory rate (Breath per minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e20 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiastolic Blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e72 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e76 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWeight (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e81.8 (\u0026plusmn;\u0026thinsp;18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e72.8 (\u0026plusmn;\u0026thinsp;16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSystolic Blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e132.7 (\u0026plusmn;\u0026thinsp;34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e133.6 (\u0026plusmn;\u0026thinsp;26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe following table (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) outlines the King Faisal Specialist Hospital and Research Centre's Stroke Code standards, detailing each step in the management of a suspected stroke patient\u0026mdash;from Emergency Department triage to the final decision on definitive treatment by the neurology team, whether thrombolysis or mechanical thrombectomy\u0026mdash;presented in chronological order.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e KFSHRC Stroke Code Standards by Area and Time Intervals\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime (Duty)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED Triage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;10 minutes (Notifying ED physician)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED Acute Care/Resus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;25 minutes (Notifying Neurologist On-call)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;45 minutes (Perform STAT CT/CTA Brain)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED (Neurologist Decision)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;60 minutes (Management: Thrombolytic vs Thrombectomy)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngio Suite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026ndash;120 minutes (Intervention to perform mechanical thrombectomy)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eStroke is one of the leading causes of disability and death worldwide [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Timely intervention is crucial in acute stroke management, and prompt treatment is associated with better patient outcomes, including lower rates of symptomatic intracranial hemorrhage, better discharge destinations, and lower in-hospital mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In the ED, stroke code protocols are deployed to expedite the diagnosis and treatment of patients with stroke [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and delays in stroke management can be attributed to several factors, including delays in seeking medical attention, diagnosis, and treatment initiation. In some cases, delays may be due to system-level factors, such as inadequate resources and inefficient processes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analysis of the specific stroke code times (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed a significant delay in the time from ED triage to code activation compared to the standard time ( Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), which might be due to the high number of patients presenting with no neurologic symptoms, such as body weakness, unwitnessed fall, and syncope (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additional factors which might have contributed to the observed delay include failure to recognize stroke symptoms during triage, language barriers, preexisting neurologic conditions, such as dementia, and other associated symptoms taking priority, such as chest pain. In our hospital, neurologists take priority over ED physicians in activating the stroke code, which might have also contributed to the observed delay. However, we did not observe an association between the time from ED triage to code activation and the mortality rate.\u003c/p\u003e \u003cp\u003eIn the present study, 61.4% of the patients were males, consistent with other studies in Saudi Arabia, with one study reporting a male incidence of 66% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Another study by Alhazzani et al. reported that 65% of the patients with stroke were males [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This finding might be associated with the higher prevalence of vascular risk factors in male patients. Studies in China reported a higher incidence and mortality rate among males with stroke [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], whereas Yim et al. reported a 54% of males have stroke [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A Canadian study by Wan et al. reported a sex difference in the rate of hospitalizations and ED visits, with event rates of 292.2 and 281.3 per 100,000 visits for male and female patients, respectively, although they did not observe other significant disparities between the sexes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Another comprehensive province-wide cohort study in Canada revealed no discernible disparities between sexes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In a study from Spain, the incidence rate 55.7% of the patients with stroke were males [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additionally, the average age for the first stroke event was higher in females than in males (79.07\u0026thinsp;\u0026plusmn;\u0026thinsp;11.96 versus 72.47\u0026thinsp;\u0026plusmn;\u0026thinsp;12.48 years). Therefore, the current evidence strongly suggests that the sex disparity in the rate of patients presenting to the ED with stroke varies across countries, highlighting the importance of considering regional factors in understanding healthcare patterns related to stroke incidence.\u003c/p\u003e \u003cp\u003eAge is a critical risk factor for stroke [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In the current cohort, the mean age of the patients with confirm stroke was similar to that reported in a previous study (61\u0026ndash;70 years) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, a study in China highlighted the critical role of age in stroke incidence [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Ekker et al. described an exponential increase in stroke incidence with increasing age in patients older than 35 years. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. another study reported similar findings, indicating highest stroke incidence in individuals aged older than 65 years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, this study observed a rise in stroke incidence in individuals aged 25\u0026ndash;44 years. In contrast to the prior study, however, Alhazani et al., identified an increase in stroke incidence among individuals aged 45\u0026ndash;64 years. We also found older age as a significant factor associated with mortality, in agreement with a study by He et al., who reported that older age was associated with a higher risk of in-hospital mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The observed association of older age with stroke might be attributed to the higher rates of neurologic and non-neurologic complications of stroke in older patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIncreased body mass index (BMI) is associated with a higher all-cause mortality in the general population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the present study, weight was significantly associated with mortality in patients with stroke. This has been specifically attributed to the increase in stroke incidence in the younger population, as reported in a case-control study of stroke incidence and mortality among patients under 45 years of age with central obesity across 32 countries [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Jo et al. also reported obesity as a significant risk factor in this age group, which had an obesity prevalence of 44.8% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Conversely, recent studies have reported improved mortality in patients with a higher BMI, illustrating the \u0026ldquo;obesity paradox.\u0026rdquo; The National Institutes of Health FAST-MAG (Field Administration of Stroke Therapy\u0026ndash;Magnesium) acute stroke trial revealed that a high BMI was associated with a consistent increase in survival rates, showing that the relationship of BMI with disability and stroke-related quality of life followed a U- or J-shaped pattern, indicating decreased survival with low or very high BMIs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Aparicio et al. reported similar findings; they found that 10-year survival rates after stroke were better in patients classified as mildly obese or overweight than in those with normal weight [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, other studies disagree with these conclusions, attributing the results to potential confounders, such as age, sex, smoking, and obesity phenotypes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have reported that the use of ambulance services was associated with earlier arrival for care [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In the present study, only 25.9% of the patients arrived via ambulance, highlighting the underutilization of emergency medical services (EMS) for the transportation of patients with stroke in Saudi Arabia. Indeed, one study reported that only 34.1% of the Saudi population utilized EMS [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] whereas another study found that 18.5% of the patients with stroke used ambulance services after the onset of symptoms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Prenotification by EMS has also been associated with decreased in-hospital mortality in patients with stroke [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It is evident that the majority of patients with stroke remain dependent on private transportation, consistent with the general population practices regarding stroke in North Africa and the Middle East [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Several studies found that the failure of family members in recognizing stroke symptoms led to delays in ED presentation and subsequent diagnosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStroke can lead to serious adverse outcomes; therefore, patients with stroke should be prioritized, particularly in emergency settings where stroke management yields the best outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the present study, mechanical thrombectomy was the prevalent type of management used in 43% of the patients whereas tissue plasminogen activator was used in 12.7% of the patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Due to the retrospective study design and the presence of incomplete data, we could not determine whether stroke management was effective in improving the NIHSS score.\u003c/p\u003e \u003cp\u003eWe acknowledge the limitations of our study. The retrospective study design introduced the risk of incomplete or missing information. Additionally, the study was conducted in a single center, limiting its generalizability to other healthcare settings. A multicenter study is warranted to more comprehensively and reliably evaluate the impact of the stroke code activation protocol on patient outcomes and to determine factors contributing to delays in its activation. We acknowledge that the data presented in the current study, which covers the period 2021\u0026ndash;2022, may not fully reflect the present-day efficiency and responsiveness of stroke code activation protocols, considering the advancements and system improvements implemented since then. To address this limitation, we plan to conduct a follow-up study covering the 2025\u0026ndash;2026 period. This will allow for a more accurate evaluation of contemporary stroke code activation performance and its alignment with current standards of care.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eImplementing stroke code activation protocols in the ED can accelerate the diagnosis and treatment of patients with stroke. Delays encountered due to the lack of symptom recognition in patients with stroke presenting to the ED highlight areas that can benefit from training of the frontline triage staff. Activation of the stroke code by the ED physicians instead of the neurologists may improve patient outcomes. The underutilization of ambulance services in transporting patients with stroke in Saudi Arabia should be addressed by increasing public awareness.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Abolyazid AY\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Body mass index\u003c/p\u003e\n\u003cp\u003eCT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Computed tomography\u003c/p\u003e\n\u003cp\u003eED\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Emergency department\u003c/p\u003e\n\u003cp\u003eEMS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Emergency medical services\u003c/p\u003e\n\u003cp\u003eHJ\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Himali JJ\u003c/p\u003e\n\u003cp\u003eNIHSS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;National Institutes of Health Stroke Scale\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained and ethically approved from King Faisal hospital and research center #RAC: 2221151\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe paper have been approved by an appropriate King Faisal hospital and research center ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors on request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo financial support or funding was received from private entities or international parties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted without any external funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eAbdulaziz Omar AlSebiheen: Conceptualization, methodology, ethical approval writing-original draft, supervision, manuscript writing.\u003c/li\u003e\n\u003cli\u003eMuhammad Nauman Qureshi: Methodology \u0026amp; discussion writing, writing - reviewing and editing of the manuscript.\u003c/li\u003e\n\u003cli\u003eAsma Waqit AlGhamdi: Software – Data curation, Data review and editing, manuscript writing\u003c/li\u003e\n\u003cli\u003eAhmed Gamal Syed: Data collection, supervision of co-authors progress, manuscript writing \u003c/li\u003e\n\u003cli\u003eRaghad Mohammed Hijazi: Data collection, manuscript writing\u003c/li\u003e\n\u003cli\u003eJibran Ahmed Khan: Data collection, manuscript writing\u003c/li\u003e\n\u003cli\u003eOhoud Turki Alsudairi: Data collection\u003c/li\u003e\n\u003cli\u003eAya Arwadi: Data collection\u003c/li\u003e\n\u003cli\u003eMohammed Bassel AlSarraj: Co- Conceptualization, manuscript reviewing.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsirvatham AR, Marwan MZ. Stroke in Saudi Arabia: a review of the recent literature. Pan Afr Med J. 2014;17:14.\u003c/li\u003e\n\u003cli\u003eJowi JO, Mativo PM. Pathological sub-types, risk factors and outcome of stroke at the Nairobi Hospital, Kenya. East Afr Med J. 2008;85:572-81.\u003c/li\u003e\n\u003cli\u003eBenamer HT, Grosset D. Stroke in Arab countries: a systematic literature review. J Neurol Sci. 2009;284:18-23.\u003c/li\u003e\n\u003cli\u003eAlahmari K, Paul SS. Prevalence of stroke in Kingdom of Saudi Arabia-through a physiotherapist diary. Mediterr J Soc Sci. 2016;7:228.\u003c/li\u003e\n\u003cli\u003eAlkeaid M, Alorainy S, Alhussainan F, Dabil T, Alkhazi A, Alsulaymi O, et al. Characteristics of stroke in prehospital settings in Saudi Arabia: a descriptive analysis. J Med Law Public Health. 2023;3:212-8.\u003c/li\u003e\n\u003cli\u003eAlabdali A, Yousif S, Alsaleem A, Aldhubayb M, Aljerian N. Can emergency medical services (EMS) shorten the time to stroke team activation, computed tomography (CT), and the time to receiving antithrombotic therapy? A prospective cohort study. Prehosp Disaster Med. 2020;35:148-51.\u003c/li\u003e\n\u003cli\u003eParamasivam S. Current trends in the management of acute ischemic stroke. Neurology India. 2015;63:665.\u003c/li\u003e\n\u003cli\u003eGurav SK, Zirpe KG, Wadia RS, Naniwadekar A, Pote PU, Tungenwar A, et al. Impact of \u0026ldquo;stroke code\u0026rdquo;-rapid response Team: an attempt to improve intravenous thrombolysis rate and to shorten door-to-needle time in acute ischemic stroke. Indian J Crit Care Med. 2018;22:243.\u003c/li\u003e\n\u003cli\u003eKamal N, Smith EE, Jeerakathil T, Hill MD. Thrombolysis: improving door-to-needle times for ischemic stroke treatment\u0026ndash;a narrative review. Int J Stroke. 2018;13:268-76.\u003c/li\u003e\n\u003cli\u003eAlhazzani AA, Mahfouz AA, Abolyazid AY, Awadalla NJ, Aftab R, Faraheen A, et al. Study of stroke incidence in the aseer region, Southwestern Saudi Arabia. Int J Env Res Public Health. 2018;15:215.\u003c/li\u003e\n\u003cli\u003eHe Q, Wu C, Luo H, Wang ZY, Ma XQ, Zhao YF, et al. Trends in in-hospital mortality among patients with stroke in China. PLoS One. 2014;9:e92763.\u003c/li\u003e\n\u003cli\u003eXia X, Yue W, Chao B, Li M, Cao L, Wang L, et al. Prevalence and risk factors of stroke in the elderly in Northern China: data from the national stroke screening survey. J Neurol. 2019;266:1449-58.\u003c/li\u003e\n\u003cli\u003eAl-Senani F, Al-Johani M, Salawati M, Alhazzani A, Morgenstern LB, Ravest VS, et al. An epidemiological model for first stroke in Saudi Arabia. J Stroke Cerebrovasc Dis. 2020;29:104465.\u003c/li\u003e\n\u003cli\u003eC Zurru M, Orzuza G. Epidemiological aspects of stroke in very old patients. Cardiovasc Haematol Disord Drug Targets. 2011;11:2-5.\u003c/li\u003e\n\u003cli\u003eAparicio HJ, Himali JJ, Beiser AS, Davis‐Plourde KL, Vasan RS, Kase CS, et al. Overweight, obesity, and survival after stroke in the Framingham heart study. J Am Heart Assoc. 2017;6:e004721.\u003c/li\u003e\n\u003cli\u003eNedeltchev K, Arnold M, Brekenfeld C, Isenegger J, Remonda L, Schroth G, et al. Pre- and in-hospital delays from stroke onset to intra-arterial thrombolysis. Stroke. 2003;34:1230-4.\u003c/li\u003e\n\u003cli\u003eWilliams LS, Bruno A, Rouch D, Marriott DJ, MAS. Stroke patients\u0026rsquo; knowledge of stroke: influence on time to presentation. Stroke. 1997;28:912-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ambulances, Cause of Death, Neurologists, Retrospective Studies, Tertiary Care Centers, Thrombectomy, Thrombolytic Therapy, Triage","lastPublishedDoi":"10.21203/rs.3.rs-6697218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6697218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStroke, a major cerebrovascular disorder with a high mortality that can lead to permanent disability, is the third leading cause of death in Saudi Arabia. Quick recognition of stroke symptoms and initiation of time-sensitive treatment can significantly change the course of stroke, and stroke code activation in the emergency department (ED) can expedite patient management. This study aimed to analyze the stroke code activation protocol against the set hospital standards in the ED of a tertiary care center in Saudi Arabia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe data of patients aged\u0026thinsp;\u0026ge;\u0026thinsp;14 years who were admitted to the ED between January 2021 and January 2022, for whom the stroke code was activated in the ED, were retrospectively analyzed, and the time intervals from ED triage to stroke code activation, neurologist review, computed tomography (CT) imaging/reporting, and thrombolysis were determined.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 409 patients with a mean age of 60.12\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1 years and a mean weight of 73.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17 kg. Additionally, 61% of the patients were male, 26% of the patients were transported to the ED by ambulance, 63% of the patients were diagnosed with stroke based on CT imaging, and 43% of the patients were managed by mechanical thrombectomy. Furthermore, 91.12% of the patients with stroke had neurologic symptoms whereas 8.89% of the patients with stroke had atypical presentations. The mean time from ED triage to stroke code activation was 44.7\u0026thinsp;\u0026plusmn;\u0026thinsp;49.6 min, the mean time from code activation to neurologist review was 12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;28.1 min, and the mean time from code activation to CT imaging was 51.9\u0026thinsp;\u0026plusmn;\u0026thinsp;38.2 min, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eImplementing the stroke code protocol in the ED can accelerate the diagnosis and treatment of patients with stroke. Delays in various stages in managing patients with stroke can be resolved with training and robust teamwork. Utilizing ambulance services to transport patients with stroke to appropriate centers can play a key role in expediting care.\u003c/p\u003e","manuscriptTitle":"Retrospective analysis of stroke code activation in the emergency department of a large tertiary care center in Saudi Arabia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 09:54:52","doi":"10.21203/rs.3.rs-6697218/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ea96207-f82c-450f-8993-9a7d9ccc2f92","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T04:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 09:54:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6697218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6697218","identity":"rs-6697218","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}