Abstract
Background
Stroke is a sudden focal neurological deficit caused by vascular damage to the central nervous system. Globally, stroke is a leading cause of death and disability, with a particularly significant burden in low- and middle-income countries. In Iran, the incidence of acute stroke is increasing, and the age of onset is lower compared to developed countries. This study examines factors causing pre-hospital delays in acute stroke patients at Shahid Rajaei Hospital, Tonekabon, North of Iran (2022–2023). Timely intervention is critical for improving outcomes, as delays significantly affect treatment effectiveness. This study aims to identify the primary causes of pre-hospital delays in acute stroke patients and provide actionable insights to enhance timely intervention strategies, ultimately improving patient outcomes.
Methods
A retrospective descriptive-analytical study was conducted on 150 acute stroke patients using census sampling. Data included demographics, symptoms, and time intervals from onset to hospital arrival. Analysis was performed with SPSS using chi-square, T-tests, and ANOVA.
Results
Of the 150 patients, 81.3% arrived over 4.5 hours after symptom onset, missing the thrombolytic treatment window. Statistical analysis revealed significant associations between some demographic and clinical factors: shorter distances to the hospital and faster arrival (p = 0.037), presence of a witness (p = 0.041), and stroke onset during the daytime (p = 0.002) were linked to reduced delays. Marital status also influenced arrival times significantly (p = 0.007). Other delay factors included unawareness of stroke symptoms (46%), being alone (14.7%), and symptoms occurring during sleep (8.6%).
Conclusions
The study highlights the need for public education on stroke symptoms and improved emergency systems to reduce delays. Addressing these gaps can enhance patient outcomes, especially in areas with limited awareness and resources.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12883-025-04361-8.
Keywords: Stroke, Time-to Treatment, Thrombolytic therapy, Health education
Introduction
Stroke is a sudden, focal neurological deficit caused by vascular damage to the central nervous system, usually resulting from infarction or hemorrhage [1]. It was the third leading cause of death in the 2021 Global Burden of Disease (GBD) study [2]. The epidemiology of stroke is changing rapidly worldwide, with increases in stroke incident events, survivors, and deaths over the last two decades. The proportional contribution of stroke-related disability-adjusted life years (DALYs) varies between developed and developing countries, with low- and middle-income nations bearing the highest burden [3, 4]. In Iran, the incidence of acute stroke is rising and estimated at 128–149 per 100,000 people in recent studies, a rate significantly higher than neighboring regions [5]. Furthermore, the average age of stroke onset in Iran is lower, correlating with increased mortality compared to developed countries [6]. Stroke prevalence in the northern provinces of Iran, such as Mazandaran and Gilan, shows significant variation, with studies reporting higher incidence rates in urban regions compared to rural areas [7]. In Sari, a city in northern Iran, stroke incidence data indicates that 52% of strokes recorded between 2020 and 2022 occurred in women, highlighting potential sex-based differences in risk factors [8]. Similarly, research from Babol suggests that 88.4% of stroke cases are ischemic, with women accounting for 55% of affected individuals [7]. Stroke severity at admission and discharge has been reported as higher in rural populations, emphasizing disparities in healthcare accessibility [7]. Most strokes (85%) are ischemic, predominantly caused by small vessel disease, cardioembolism, or large artery athero-thromboembolism, while intracerebral hemorrhage accounts for approximately 15% [9]. Non-modifiable risk factors include age, sex, and genetics; modifiable risks include hypertension, diabetes mellitus, smoking, and hyperlipidemia [7]. Clinical symptoms depend on the affected brain region, but sudden onset is a hallmark feature, leading to deficits in movement, speech, or balance. Severe cases may involve loss of consciousness, while transient ischemic attacks (TIA) resolve within 24 h [10]. Advances such as endovascular thrombectomy (EVT) have enabled recovery in select patients; however, stroke prevention remains the cornerstone of care given limited eligibility for recanalization therapies. Admission to specialized stroke units significantly reduces complications and improves outcomes, emphasizing the importance of structured management protocols [11, 12].
Since many stroke patients are transported to hospitals by pre-hospital emergency services, early diagnosis plays a critical role in optimizing treatment outcomes. In Iran, emergency medical services (EMS) utilize the SAMA code system (an acronym derived from the Persian term “سما,” meaning “Emergency Stroke”) to prioritize suspected stroke patients. This protocol enables rapid identification and transport, ensuring timely intervention at designated stroke centers. Pre-hospital stroke triage is primarily based on the FAST criteria (Face drooping, Arm weakness, Speech difficulty, Time to call emergency services), which helps emergency technicians assess symptom severity and determine the need for urgent referral. While pre-hospital stroke identification has demonstrated a high positive predictive value (approximately 86.3%), there remain challenges in diagnostic accuracy, particularly in differentiating ischemic strokes from other neurological conditions [13]. Response times vary, with an average total EMS response time of approximately 37.9 min, including arrival, on-scene assessment, and transport [14]. Reducing pre-hospital delays through enhanced training and standardized triage methods could further improve stroke care efficiency.
The optimal approach to treating stroke involves restoring blood flow to affected areas of the ischemic brain before permanent tissue damage occurs. Intravenous thrombolysis (IVT) with fibrinolytic drugs is a widely used treatment for acute ischemic stroke [11]. Despite advancements in stroke management, delays in initiating thrombolytic therapy remain a major challenge. Studies conducted at Imam Reza Hospital in Tabriz have examined factors influencing patient workflow and outcomes in the emergency department, highlighting the need for hospital system optimization to reduce treatment delays [15]. Another study in this center highlights improvements in Door-to-Needle time for stroke patients using thrombolytic activation codes, emphasizing the importance of optimizing treatment protocols at the local level [16]. The National Institute of Neurologic Disorders and Stroke (NINDS) trial demonstrated the efficacy of intravenous tissue-type plasminogen activator (IV tPA) administered within 3 h of symptom onset, making thrombolytic therapy the standard of care [17]. The European Cooperative Acute Stroke Study (ECASS) III later extended the treatment window to 4.5 h under stricter criteria, further improving patient outcomes [18]. Imaging modalities such as computed tomography (CT) remain critical for identifying candidates for thrombolysis, while advanced techniques like MRI offer benefits in cases with unclear symptom onset time [19–21]. The phrase “Time is Brain” underscores the urgent need for rapid diagnosis and treatment, as the ischemic brain experiences significant neuronal loss with every minute of delay [22]. Despite advancements in stroke management, delays in initiating thrombolytic therapy remain common due to challenges at multiple levels:
Community level: Limited awareness of stroke symptoms and delayed care-seeking behavior result in late hospital arrivals. Pre-hospital emergency services are underutilized [23].
Pre-hospital emergency level: Factors such as a lack of ambulances, inadequate triage systems, and insufficient personnel contribute to delays in patient transport [24].
Hospital level: Slow imaging procedures and a lack of trained professionals capable of administering thrombolytic therapy exacerbate delays [25].
By addressing these barriers, this research aims to improve stroke care protocols and reduce treatment delays, particularly in resource-limited settings.
Key time intervals in stroke management include the following: (1) Symptom to Door Time: The time from the onset of symptoms to hospital arrival, ideally within 1 h; (2) Door Time: The time when the patient arrives at the hospital providing stroke services; (3) Door to Needle Time: The interval between hospital arrival and initiation of thrombolytic treatment, ideally less than one hour; and (4) Door to Device Time: The time to start mechanical thrombectomy (PPCI), also ideally less than one hour [12, 23–27]. Despite advances in therapies like thrombolysis, significant delays remain prevalent at the community, pre-hospital, and hospital levels due to lack of awareness, limited emergency services, and delays in diagnostic procedures. Such delays—especially from symptom onset to hospital arrival—are crucial factors influencing recovery and survival rates. This study aims to identify and assess factors contributing to treatment delays in acute stroke patients at Shahid Rajaei Hospital’s emergency department. By evaluating time intervals and barriers to timely care, the research seeks to provide actionable insights for improving stroke management protocols. Its findings will inform strategies for enhancing awareness campaigns, optimizing hospital systems, and ultimately improving patient outcomes—particularly in regions where stroke incidence is rising and delays in treatment may mean the difference between recovery and long-term disability.
Materials and methods
Study design
This retrospective descriptive-analytical study employed a census sampling method to analyze patients diagnosed with acute cerebral stroke. Since retrospective data collection carries a risk of information bias, standardized data extraction protocols were implemented to minimize discrepancies. Whenever feasible, records were cross-referenced with electronic health records and pre-hospital emergency system reports to enhance data accuracy.
Setting
The study was conducted in the emergency department of Shahid Rajaei Hospital. Patient data from 2022 to 2023 was reviewed.
Participants
The study included all patients diagnosed with acute stroke during the specified period.
Sample size considerations
The sample size of 150 patients was determined based on feasibility and prior research indicating that retrospective studies evaluating stroke outcomes with similar methodologies have effectively derived statistically significant results using comparable sample sizes. Studies such as those published in the Caspian Journal of Neurological Sciences have utilized sample sizes ranging between 120 and 180 for stroke-related research, supporting the adequacy of this sample size for identifying meaningful associations [28].
Inclusion criteria
Confirmed diagnosis of acute stroke.
Availability of the exact time of symptom onset until arrival at the treatment center (as reported by the patient’s companion).
Informed consent provided by the patient or their companion.
Exclusion criteria
Missing or inaccessible medical records.
Incomplete or inadequate medical file information.
Inability to contact the patient or their companion.
Variables
The primary variables examined were:
Demographic information: Age, gender, marital status, educational status.
Medical data: Stroke type, accompanying symptoms, vascular risk factors (e.g., history of heart attack or stroke).
Other factors: Presence of a witness during the stroke, distance from the treatment center, and time elapsed between symptom onset and arrival at the treatment center.
Data sources and measurement
Data was extracted from patient medical files. When additional information was required, the patient’s companion was contacted to provide informed consent and complete the data collection process. Collected data was recorded in a researcher-designed checklist and transferred to an Excel sheet for analysis.
Checklist validity and reliability
The validity and reliability of the checklist were evaluated before data collection. Content validity was confirmed through expert review, ensuring the checklist addressed key stroke-related variables. Internal consistency was assessed using Cronbach’s alpha, with an acceptable threshold of 0.82. The checklist comprised three main domains: (1) Patient demographics (6 items), (2) Stroke characteristics (8 items), and (3) Pre-hospital and hospital-related time intervals (5 items).
Ethical considerations
The study received approval from the Ethics Committee of Mazandaran University of Medical Sciences in Ramsar Campus (IR.MAZUMS.RIB.REC.1401.091)and certify that the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
Statistical analysis
Statistical analyses were performed using SPSS version 23. The chi-square test, T-test, and ANOVA were applied as appropriate. A p-value of less than 0.05 was considered statistically significant.
Results
This study involved 150 patients with acute stroke. Of these, 26 patients received standard intravenous thrombolytic treatment with alteplase, while 121 patients were not eligible for this treatment for various reasons. The sample included 48.7% men and 51.3% women, with an average age of 70.51 ± 13.3 years. Table 1 presents the demographic characteristics of the patients, including details on education, occupation, dominant hand, smoking habits, and sleep status, while Table 2 displays the age, weight, height, and body mass index (BMI) of the patients with acute stroke.
Table 1.
Demographic characteristics of the study population
| Variable | Count (%) |
|---|---|
| Gender | |
| Male | 73 (48.7%) |
| Female | 77 (51.3%) |
| Education | |
| Illiterate | 71 (47.3%) |
| Primary | 43 (28.7%) |
| Diploma | 28 (18.7%) |
| University | 8 (5.3%) |
| Occupation | |
| Self-employed | 21 (14%) |
| Farmer | 8 (5.3%) |
| Civil servant | 6 (4%) |
| Retired | 26 (17.3%) |
| Disabled | 40 (26.7%) |
| Housewife | 49 (32.7%) |
| Marital status | |
| Married | 105 (70%) |
| Single | 45 (30%) |
| Dominant hand | |
| Right-handed | 128 (85.3%) |
| Left-handed | 22 (14.7%) |
| Smoking | |
| No | 112 (74.7%) |
| Yes | 38 (25.3%) |
| Sleep status | |
| Abnormal | 71 (47.3%) |
| Normal | 79 (52.7%) |
| Type of stroke | |
| Ischemic | 143 (95.3%) |
| Hemorrhagic | 4 (2.7%) |
| Transient | 3 (2%) |
Table 2.
Mean, standard deviation of age, weight, height, and BMI in patients with acute stroke
| Variable | Count | Min | Max | Mean | Std. Dev. |
|---|---|---|---|---|---|
| Age | 150 | 36 | 91 | 70.51 | 13.33 |
| Weight (kg) | 150 | 40 | 125 | 73.89 | 13.90 |
| Height (cm) | 150 | 150 | 190 | 166.4 | 8.45 |
| BMI | 150 | 15 | 48 | 25.80 | 4.70 |
Descriptive statistics
The mean age of the patients was 70.51 ± 13.33 years, while the mean BMI was 25.80 ± 4.70. Other continuous variables, including weight (73.89 ± 13.90 kg) and height (166.40 ± 8.45 cm), were also analyzed (Table 2) using mean and standard deviation to summarize the central tendency and dispersion within the sample.
Clinical and risk factors
Hypertension was the most prevalent risk factor, affecting 122 patients (81.3%). Among clinical symptoms, one-sided weakness (52%) and reduced consciousness (19.3%) were the most frequently observed following an acute stroke (Table 3).
Table 3.
Frequency of risk factors and clinical symptoms in patients with acute stroke
| Variable | Count (%) |
|---|---|
| Risk factors | |
| Diabetes | 70 (46.7%) |
| Hypertension | 122 (81.3%) |
| Hyperlipidemia | 53 (35.3%) |
| History of stroke | 15 (10%) |
| History of heart attack | 57 (38%) |
| Clinical symptoms | |
| Generalized weakness | 4(2.7%) |
| Speech disorder/aphasia | 26 (17.3%) |
| Balance disorder | 9 (6%) |
| Reduced consciousness | 29 (19.3%) |
| One- sided weakness | 78 (52%) |
| Nausea/vomitting | 2 (1.3%) |
| Diplopia (double vision) | 2 (1.3%) |
Factors influencing time to hospital arrival
Out of 150 patients, 28 (18.7%) arrived at the hospital within 4.5 h of symptom onset, while 122 (81.3%) experienced delayed arrival (> 4.5 h). The relationship between various demographic and clinical variables with hospital arrival time was assessed using appropriate statistical tests (Table 4).
Table 4.
Factors affecting time to hospital arrival in acute stroke patients
| Variable | Early Arrival (< 4.5 h) | Late Arrival (> 4.5 h) | Total | P-value |
|---|---|---|---|---|
| Gender | p = 0.540 | |||
| Male | 15 (20.54%) | 58 (79.45%) | 73 (100%) | |
| Female | 13 (16.9%) | 64 (83.1%) | 77 (100%) | |
| Age | 70.50 ± 12.45 | 70.51 ± 13.53 | - | p = 0.995 |
| Education Level | p = 0.951 | |||
| Illiterate | 13 (18.3%) | 58 (81.7%) | 71 (100%) | |
| Primary | 9 (20.9%) | 34 (79.1%) | 43 (100%) | |
| Diploma | 5 (17.9%) | 23 (82.1%) | 28 (100%) | |
| University | 1 (12.5%) | 7 (87.5%) | 8 (100%) | |
| Occupation | p = 0.933 | |||
| Self-Employed | 4 (19%) | 17 (81%) | 21 (100%) | |
| Disabled | 6 (15%) | 34 (85%) | 40 (100%) | |
| Retired | 4 (15.4) | 22 (84.6%) | 26 (100%) | |
| Housewife | 10 (20.4%) | 39 (79.6) | 49 (100%) | |
| Civil Servant | 1 (16.7%) | 5 (83.3%) | 6 (100%) | |
| Farmer | 2 (25%) | 6 (75%) | 8 (100%) | |
| Medical conditions | ||||
| Diabetes | 10 (14.3%) | 60 (85.7%) | 70 (100%) | p = 0.140 |
| Hypertension | 24 (19.7%) | 98 (80.3%) | 122 (100%) | p = 0.345 |
| Hyperlipidemia | 10 (18.9%) | 43 (81.1%) | 53 (100%) | p = 0.483 |
| History of stroke | p = 0.529 | |||
| No | 26 (19.3%) | 109 (80.7%) | 135(100%) | |
| Yes | 2 (13.3%) | 13 (86.4%) | 15 (100%) | |
| History of myocardial infarction (MI) | p = 0.154 | |||
| No | 27 (20.1%) | 107 (79.9%) | 134 (100%) | |
| Yes | 1 (6.25%) | 15 (93.75%) | 16 (100%) | |
| Clinical symptoms | p = 0.672 | |||
| Generalized weakness | 1(25%) | 3(75%) | 4(100%) | |
| Speech disorder/aphasia | 7 (26.9%) | 19 (73.1%) | 26 (100%) | |
| Balance disorder | 0 | 9 (100%) | 9 (100%) | |
| Reduced consciousness | 4 (13.8%) | 25 (86.2%) | 29 (100%) | |
| One-sided weakness | 16 (20.5%) | 62 (79.5%) | 78 (100%) | |
| Nausea/vomitting | 0 | 2 (100%) | 2 (100%) | |
| Double vision | 1 (50%) | 1 (50%) | 2 (100%) | |
| Type of arrival | p = 0.831 | |||
| Self-referral | 4 (17.4%) | 19 (82.6%) | 23 (100%) | |
| By EMS | 24 (18.9%) | 103 (81.1%) | 127 (100%) | |
| Distance to hospital (km) | 19.82 ± 4.45 | 25.91 ± 17.94 | - | p = 0.037 |
| Witness Present | p = 0.041 | |||
| Accompanied | 28 (21.9%) | 100 (78.1%) | 128 (100%) | |
| Alone | 0 | 22 (100%) | 22 (100%) | |
| Marital status | p = 0.007 | |||
| Married | 25 (24%) | 79 (76%) | 104 (100%) | |
| Single | 3 (2.9%) | 43 (97.1%) | 47 (100%) | |
| Stroke onset time | p = 0.002 | |||
| Morning | 0 | 23 (100%) | 23 (100%) | |
| Noon | 18 (48.6%) | 19 (51.4%) | 37 (100%) | |
| Night/midnight | 10 (12.5%) | 70 (87.5%) | 80 (100%) | |
| Unknown | 0 | 10 (100%) | 10 (100%) | |
| Stroke Type | p = 0.571 | |||
| Ischemic | 27 (18.9%) | 114 (81.1%) | 143 (100%) | |
| Hemorrhagic | 1 (75%) | 3 (25%) | 4 (100%) | |
| Transient | 0 | 3 (100%) | 3 (100%) | |
Chi-square test for categorical variables
The Chi-square test was performed to evaluate associations between categorical variables and arrival time. Results showed that marital status (p = 0.007), presence of a witness at symptom onset (p = 0.041), and stroke onset time (p = 0.002) were significantly associated with early hospital arrival (p < 0.05). Patients who were married (25 early arrivals, 24%) and those accompanied at stroke onset (28 early arrivals, 21.9%) were more likely to reach the hospital within 4.5 h. Conversely, factors such as gender (p = 0.540), education level (p = 0.951), occupation (p = 0.933), and history of stroke (p = 0.529) did not show statistically significant associations with hospital arrival time (p > 0.05). Similarly, patients with speech disorders (p = 0.672), one-sided weakness, and visual symptoms had a higher percentage of early arrivals, though these differences were not statistically significant.
Independent t-test for continuous variables
An independent t-test was used to compare continuous variables between patients with early and late arrival. The distance to the hospital was significantly different between these groups (p = 0.037), with earlier arrivals having an average distance of 19.82 ± 4.52 km compared to 25.91 ± 17.94 km in late arrivals. No significant differences were observed for age (p = 0.995) between the two groups.
ANOVA for multi-group comparisons
A one-way ANOVA was conducted to compare the effect of stroke onset time across different time periods (morning, noon, night/midnight). The analysis revealed a statistically significant effect (p = 0.002), indicating that patients experiencing a stroke at noon (48.6% early arrival) were more likely to arrive promptly compared to those with nighttime strokes (12.5% early arrival).
Reasons for delayed arrival
The primary reason for delayed arrival was lack of awareness of stroke symptoms, reported by 69 patients (46%). Other contributing factors included being alone at symptom onset (22 patients, 14.66%), experiencing symptoms while sleeping (13 patients, 8.6%), not initially visiting a specialized stroke center (8 patients, 5.33%), and long distance from the treatment facility (5 patients, 3.33%).
Discussion
This study examined pre-hospital delays in acute stroke patients and identified several key findings. A substantial 81.3% of patients arrived at the hospital later than 4.5 h after symptom onset, missing the optimal thrombolysis window. This trend is consistent with studies conducted in low- and middle-income countries (LMICs), including Kabanda et al. and Kakame et al. [29, 30], which highlight similar delays influenced by systemic and behavioral factors. Demographic variables such as gender, age, and education level did not show a statistically significant association with hospital arrival times. These findings align with Kabanda et al. and Zhou et al. [29, 32], suggesting that structural and behavioral factors may exert a greater influence than individual characteristics on pre-hospital delays. Additionally, while hypertension (81.3%) and diabetes (46.6%) were the most prevalent comorbidities among stroke patients, neither condition showed a significant relationship with hospital arrival time. This observation is consistent with Kabanda et al. and Zhou et al. [29, 31], indicating that vascular risk factors alone may not play a decisive role in pre-hospital decision-making or emergency response efficiency.
Significant predictors of early arrival
In our study several factors were found to significantly impact hospital arrival time:
Marital status (p = 0.007): Married patients (24% early arrivals) were more likely to seek timely care, supporting research by Kakame et al. and Ashraf et al. [29, 32], so that Married individuals experienced shorter delays in reaching the hospital primarily due to greater social support and immediate assistance from family members.
Witness presence (p = 0.041): In this study, patients who experienced a stroke in the presence of others had higher rates of early hospital arrival, reinforcing the role of social awareness in emergency response.
Distance to the hospital (p = 0.037): Patients residing closer to healthcare facilities had significantly shorter delays, highlighting the importance of accessible emergency services.
Stroke onset time (p = 0.002): Those who experienced strokes at night had prolonged delays, likely due to reduced healthcare availability, patient hesitation, and lower likelihood of immediate symptom recognition.
These findings align with studies from LMICs, including the Zweditu Memorial Hospital study in Ethiopia, which reported that nighttime stroke onset significantly increased pre-hospital delays due to limited EMS availability and delayed symptom recognition [33].
Regional disparities in stroke care
In Iran, EMS bases and hospitals are unevenly distributed, with Tehran and Mashhad having a higher density of EMS stations, whereas Sistan and Baluchestan face severe shortages leading to longer emergency response times and poorer stroke outcomes. Findings from this study confirm that geographical accessibility plays a significant role in pre-hospital delays. Patients in urban areas arrived earlier, while those in rural regions experienced prolonged delays, reflecting challenges such as long travel distances and inadequate transportation infrastructure. Research conducted in Mazandaran and Gilan indicates that while healthcare resources are relatively accessible, disparities persist—especially in isolated rural areas [34]. Similar findings have been reported at Imam Reza Hospital in Tabriz, where researchers emphasized the need for optimized EMS dispatch protocols and better hospital workflow efficiency to reduce treatment delays [15].
Comparison with global findings
The challenges observed in this study are not unique to Iran. Comparable pre-hospital delays are reported in other LMICs, including: - India and Brazil, where rural patients encounter longer transport times and limited access to specialized stroke centers, resulting in worse outcomes [35]. - Nigeria, where studies show significant EMS delays due to ambulance shortages, often forcing patients to rely on private transportation, further exacerbating hospital arrival delays [36]. Bangladesh, where healthcare accessibility barriers disproportionately affect low-income communities, similar to patterns seen in Iran’s underserved regions [37].
Strengths and limitations
Strengths:
Comprehensive analysis of both demographic and clinical variables affecting pre-hospital delay.
Statistical rigor, employing Chi-square, independent t-tests, and ANOVA for robust evaluation.
Findings provide critical public health insights, emphasizing the need for stroke care improvements, particularly in LMICs [38].
Limitations:
Single-center study: Results may not be generalizable.
Potential recall bias: Patients’ reported reasons for delayed arrival may be subjective or inaccurate.
Lack of qualitative insights: While statistical correlations were examined, patient awareness and healthcare accessibility barriers were not explored in depth.
Implications and future research
Given the high incidence of pre-hospital delays, urgent improvements in stroke awareness, emergency response systems, and healthcare infrastructure are needed. Future research should prioritize:
Community-based stroke education programs for symptom recognition [28, 34].
Expanding ambulance services and referral networks to improve patient transport efficiency.
Evaluating targeted stroke awareness campaigns in rural populations where delays are more pronounced [39, 40].
Investigating socioeconomic influences on stroke care-seeking behaviors to develop interventions for high-risk groups [24, 25, 39, 41].
Extending the study period in future research to cover a longer timeframe, ensuring a more comprehensive analysis of trends and improving the accuracy of statistical findings.
Conclusion
In conclusion, this study highlights the urgent need for systematic improvements in stroke awareness and emergency response, particularly in low- and middle-income countries, to ensure timely treatment and better patient outcomes. Addressing these barriers can significantly reduce pre-hospital delays, improving survival rates and neurological recovery for stroke patients. The findings highlighted that pre-hospital delays after acute stroke onset was 81.3% and significantly influenced by factors such as hospital distance, time of stroke onset, marital status, and the presence of a companion. Despite the absence of influence from common vascular risk factors, patients with delays tend to experience more severe symptoms, such as weakness and hemiplegia. These results underscore the importance of targeted interventions, such as improving stroke awareness and addressing the logistical barriers that contribute to delays, especially in rural and isolated communities. Future studies should focus on strategies to reduce delays, particularly through community-based education and enhancing access to emergency services. By addressing these factors, we can potentially improve timely access to treatment and reduce the long-term effects of stroke.
Supplementary Information
Acknowledgements
The authors would like to express their gratitude to the patients and their families for their participation in this study. We also thank the staff at Shahid Rajaei Hospital, Tonekabon, for their support and collaboration in collecting data.
Authors’ contributions
MF: Substantial contributions to the conception or design of the work, and to the acquisition, analysis, or interpretation of data – Drafting the work and critically reviewing it for important intellectual content – Final approval of the version to be published – Agreement to be accountable for all aspects of the work, ensuring integrity and accuracy are properly investigated and resolved. MSPA: Substantial contributions to the conception or design of the work, and to the acquisition, analysis, or interpretation of data – Drafting the work and critically reviewing it for important intellectual content – Final approval of the version to be published – Agreement to be accountable for all aspects of the work, ensuring integrity and accuracy are properly investigated and resolved.
Funding
The authors declare that no funding was received to support this study.
Data availability
Data is provided within the manuscript or supplementary information files.
Declarations
Ethics approval and consent to participate
The present study has been approved by Ethics committee of Mazandaran University of Medical Sciences in Ramsar Campus (IR.MAZUMS.RIB.REC.1401.091).and certify that the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
As part of hospital protocol, an informed consent form is routinely added to the medical file of all patients upon admission at Shahid Rajaee Hospital. This document outlines the patient’s voluntary cooperation with clinical evaluations and permits the ethical use of medical data in future scientific research. In this study, only the records of patients who signed the written consent form (attached) were included. In addition, each patient or their legal guardian was individually contacted, and the objectives of the study were thoroughly explained. Following these discussions, verbal consent was obtained once again. Patients who, after full clarification, declined participation for any reason were not enrolled in the study.
Consent for publication
All participants and their witnesses were verbally informed about the nature and purpose of the study, including the potential publication of anonymized clinical and demographic data. Participation was voluntary, and individuals who provided oral consent were enrolled in the study.
In addition, upon admission, all patients or their legal representatives signed a written consent form acknowledging that Shahid Rajaei Hospital is a teaching and research-oriented medical center. The consent form clarified that information from medical records may be used for research purposes and academic publication, without compromising confidentiality. Only records with signed consent forms (attached) were reviewed and included in this analysis.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Murphy SJ, Werring DJ. Stroke: causes and clinical features. Medicine. 2020;48:561. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Feigin VL, Branin M, Norving B, et al. World stroke organization (WSO): global stroke fact sheet 2022. Int J Stroke. 2022;17:18–29. [DOI] [PubMed] [Google Scholar]
- 3.Feigin VL, Mensah GA, Norrving B, et al. Atlas of the global burden of stroke (1990–2013): the GBD 2013 study. Neuroepidemiology. 2015;45:230–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Hosseini A, Ghandehari D, Benamer K. Frequency and clinical patterns of stroke in Iran - systematic and critical review. BMC Neurol. 2010;10:72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Azarpazhooh M, et al. Excessive incidence of stroke in iran, evidence from Mashhad stroke incidence study (MSIS), A population based study of stroke in the middle East. Stroke. 2010;41:e3–10. [DOI] [PubMed] [Google Scholar]
- 6.Borhani Haghighi A, et al. Hospital mortality associated with stroke in Southern Iran. Iran J Med Sci. 2013;38(4):314–20. [PMC free article] [PubMed] [Google Scholar]
- 7.Saadat P, Ahmadi Ahangar A, Khafri S, et al. Rural-Urban differences in stroke types, risk factors, severity, and prognosis in babol, Northern Iran. Elder Health J. 2018;4(2):68–74. [Google Scholar]
- 8.Sharifi-Razavi A. Women and Stroke in Iran. Think Global Health November. 2022;17.
- 9.Stephen JX, Murphy DJ. Werring. Stroke: causes and clinical features. Medicine. 2020;48(9):561–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kelly AG. Attacking quality-of-care gaps in transient ischemic attack. Stroke. 2021;52(7):2379–80. [DOI] [PubMed] [Google Scholar]
- 11.Davies L, delcourt Candice. Current approach to acute stroke management. Intern Med J. 2021;51(4):481–7. [DOI] [PubMed] [Google Scholar]
- 12.Stroke Unit Trialists’ Collaboration. Organised inpatient (stroke unit) care for stroke. Cochrane Database Syst Rev. 2013;9:CD000197. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Asna Ashari Amiri M, Rezai M, Mohammadi F, Vaziri S, Tabibzadeh Dezfooli SA. Accuracy of prehospital emergency service in activating acute stroke code. J Mazandaran Univ Med Sci. 2023;33(224):163–9. [Google Scholar]
- 14.Talebi Anaraki K, Ahmadi O, Heidari F, Azimi A, Ahmadi Marzaleh M. Prehospital emergency response times for stroke patients in Iran: a cross-sectional study. Iran J Emerg Med. 2024;11(1):e6. [Google Scholar]
- 15.Poureskandari M, Sadeghi-Hokmabadi E, Ala A, Tayyari K, Vahdati SS. Studying the affecting factors in patient workflow and outcome of patients with stroke diagnosis in the emergency department. Int J Aging. 2023;1(1):e13. [Google Scholar]
- 16.Vahdati SS, Ala A, Sadeghi-Hokmabadi E, Ghasemi H. Evaluation of Door-to-Needle stroke patients with thrombolytic activation code in Imam Reza educational center in Tabriz. Int J Aging. 2024;2(1):e16. [Google Scholar]
- 17.Hollist M, Morgan L, Cabatbat R, Au K, Kirmani MF, Kirmani BF. Acute stroke management: overview and recent updates. Aging Dis. 2021;12(4):1000–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Alper BS, Foster G, Thabane L, Rae-Grant A, Malone-Moses M, Manheimer E. Thrombolysis with alteplase 3-4.5 hours after acute ischaemic stroke: trial reanalysis adjusted for baseline imbalances. BMJ Evidence-Based Medicine. 2020;25:168–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Thomalla G, Simonsen CZ, Boutitie F, Andersen G, Berthezene Y, Cheng B, WAKE-UP investigators, et al. MRI-guided thrombolysis for stroke with unknown time of onset. N Engl J Med. 2018;379:611–22. [DOI] [PubMed] [Google Scholar]
- 20.Schwamm LH, Wu O, Song SS, Latour LL, Ford AL, Hsia AW. Intravenous thrombolysis in unwitnessed stroke onset: MR witness trial results. Ann Neurol. 2018;83:980–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Amiri H, Bluhmki E, Bendszus M, Eschenfelder CC, Donnan GA, Leys D, et al. European cooperative acute stroke Study-4: extending the time for thrombolysis in emergency neurological deficits ECASS-4: extend. Int J Stroke. 2016;11:260–7. [DOI] [PubMed] [Google Scholar]
- 22.Saver JL. Time is brain quantified. AHA/ASA Journals (stroke). 2005;37(1):263–6. [DOI] [PubMed] [Google Scholar]
- 23.Rodgers ML, Fox E, Abdelhak T, Franker LM, Johnson BJ, Kirchner-Sullivan C, American heart association Council on cardiovascular and stroke nursing and the stroke council, care of the patient with acute ischemic stroke (Endovascular/Intensive care Unit-Postinterventional Therapy): Update to 2009 Comprehensive nursing care Scientific Statement: A Scientific Statement from the, et al. Care of the Patient With Acute Ischemic Stroke (Endovascular/Intensive Care Unit-Postinterventional Therapy): Update to 2009 Comprehensive Nursing Care Scientific Statement: A Scientific Statement From the American Heart Association. Stroke. 2021;52(5):e198-210. [DOI] [PubMed] [Google Scholar]
- 24.Mead GE, Sposato LA, Sampaio Silva G, Yperzeele L, Wu S, Kutlubaev M, et al. A systematic review and synthesis of global stroke guidelines on behalf of the World Stroke Organization. Int J Stroke. 2023;18(5):499–531. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Fladt J, Engelter S, De Marchis GM, Psychogios M, Lyrer P, Bonati L, et al. Stroke Unit- / Stroke center care. Ther Umsch. 2021;78(6):328–38. [DOI] [PubMed] [Google Scholar]
- 26.Edward CJ, Jeffrey LS, Harold PA, et al. Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2013;44:870–947. [DOI] [PubMed] [Google Scholar]
- 27.Edakkattil S, Abraham SV, Panattil NJ, Gafoor FA, Jacob L, Liu R. Prehospital factors associated with delayed hospital arrival of stroke patients: a regional single-center study from India. Ann Indian Acad Neurol. 2024;27(2):165–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Majidi Shad M, Saberi A, Shakiba M, Rezamasouleh S. Evaluating the duration of hospitalization and its related factors among stroke patients. Casp J Neurol Sci. 2018;4(4):169–77. [Google Scholar]
- 29.Kabanda IK, Kiangebeni Ngonzo C, Emeka Bowamou CK, Divengi Nzambi JP, Kiatoko Ponte N, Tuyinama Madoda O, et al. Stroke signs knowledge and factors associated with a delayed hospital arrival of patients with acute stroke in Kinshasa. Heliyon. 2024;10(7):e28311. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Kakame KT, Nakibuuka J, Mukiza N, Andia-Biraro I, Kaddumukasa M, Burant C, et al. Prevalence and factors associated with pre-hospital delay among acute stroke patients at Mulago and Kiruddu National referral hospitals, Kampala: a cross-sectional study. BMC Neurol. 2023;23(1):381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Zhou Y, Yang T, Gong Y, Li W, Chen Y, Li J, et al. Pre-hospital delay after acute ischemic stroke in central urban China: prevalence and risk factors. Mol Neurobiol. 2017;54(4):3007–16. [DOI] [PubMed] [Google Scholar]
- 32.Ashraf V, Maneesh M, Praveenkumar R, Saifudheen K, Girija A. Factors delaying hospital arrival of patients with acute stroke. Ann Indian Acad Neurol. 2015;18(2):162. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Sintayehu R, Tinsae T, Kefyalew M. Missed golden hours of stroke patients at Zweditu Memorial Hospital in Addis Ababa, Ethiopia. BMC Neurol. 2025;25(1):194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Ahangari A, Dehghani Ahmadabad A. Spatial distribution of health services in Iranian provinces at the end of the fifth development plan. Jundishapur J Health Sci. 2018;10(3):e78862. [Google Scholar]
- 35.Pandian JD, Singh G, Kaur P, et al. Stroke care in india: challenges and opportunities. Lancet Neurol. 2020;19(2):136–44. [Google Scholar]
- 36.Owolabi MO, Thrift AG, Martins S, et al. The state of stroke services in africa: A systematic review. Int J Stroke. 2022;17(1):27–36. [Google Scholar]
- 37.Sadeghi-Hokmabadi E, Vahdati SS, Rikhtegar R, Ghasempour K, Rezabakhsh A. Public knowledge of people visiting Imam Reza hospital regarding stroke symptoms and risk factors. BMC Emerg Med. 2019;19:1–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Feigin VL, Norrving B, Mensah GA. Global burden of stroke. Lancet Neurol. 2021;20(10):791–806.31279745 [Google Scholar]
- 39.Nagao Y, Nakajima M, Inatomi Y, Ito Y, Kouzaki Y, Wada K, Yonehara T, et al. Pre-hospital delay in patients with acute ischemic stroke in a multicenter stroke registry: K-PLUS. J Stroke Cerebrovasc Dis. 2020;29(11): 105284. [DOI] [PubMed] [Google Scholar]
- 40.Miri K, Sabbaghi M, Mazlum SR, Namazinia M. The trend of change in the role of Pre-Hospital emergency medical services in iran’s healthcare system. BMC Emerg Med. 2023;23:99. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.O’Donnell MJ, Chin SL, Rangarajan S, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke. Lancet. 2016;388(10046):761–75. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data is provided within the manuscript or supplementary information files.