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Analysis of onset-to-door time and its influencing factors in Chinese patients with acute ischemic stroke during the 2020 COVID-19 epidemic: a preliminary, prospective, multicenter study

BMC Health Services Research volume 24, Article number: 615 (2024) Cite this article

Abstract

Background

Pre-hospital delay in China is a serious issue with unclear relevant reasons, seriously impeding the adoption of appropriate measures. Herein, we analyzed the onset-to-door time (ODT) in Chinese patients with acute ischemic stroke (AIS) and its influencing factors.

Methods

We prospectively recruited 3,459 patients with AIS from nine representative tertiary general hospitals in China between January and June 2022. Patients were divided into ODT ≤ 3 h and ODT > 3 h groups. Following single-factor analysis, binary logistic regression analysis was performed to evaluate the risk factors leading to pre-hospital delay.

Results

In total, 763 (21.83%) patients arrived at the hospital within 3 h of onset. After adjusting for confounding factors, the risk factors for ODT were residence in rural areas (odds ratio [OR]: 1.478, 95% credibility interval [CI]: 1.024–2.146) and hospital transfer (OR: 7.479, 95% CI: 2.548–32.337). The protective factors for ODT were location of onset ≤ 20 km from the first-visit hospital (OR: 0.355, 95% CI: 0.236–0.530), transportation by emergency medical services (OR: 0.346, 95% CI: 0.216–0.555), history of atrial fibrillation (OR: 0.375, 95% CI: 0.207–0.679), moderate stroke (OR: 0.644, 95% CI: 0.462–0.901), and severe stroke (OR: 0.506, 95% CI: 0.285–0.908).

Conclusions

Most patients with AIS fail to reach a hospital within the critical 3-h window. The following measures are recommended to reduce pre-hospital delays: reasonable distribution of hospitals accessible to nearby residents, minimizing interhospital transfer, paying attention to patients with mild stroke, and encouraging patients to use ambulance services. Pre-hospital delays for patients can be reduced by implementing these measures, ultimately improving the timeliness of treatment and enhancing patient prognosis. This study was carried out amid the COVID-19 pandemic, which presented challenges and constraints.

Peer Review reports

Background

Acute ischemic stroke (AIS) is a common acute cerebrovascular disease with high disability and mortality rate [1]. Restoring vascular recanalization and improving tissue perfusion within the time window is the key to a successful treatment. Intravenous thrombolysis (IVT) within 3 h of AIS onset can effectively improve prognosis without a significantly increased risk of death [2]. Pre-hospital delay in AIS in China is common [3,4,5,6,7,8,9]. However, the impact of the Coronavirus Disease 2019 (COVID-19) pandemic, which began in January 2020 [10], on these delays and their influencing factors remain unclear. This lack of clarity further impedes the formulation of improvement measures.

The global burden of stroke has increased markedly over the past 20 years, especially in developing countries [11]. In China, stroke has become the leading cause of death over the past 30 years [12]. The median onset-to-door time (ODT) for AIS in China is 15 h, and only a quarter of patients reach the hospital within 3 h [13], which is obviously longer than that in developed Western nations. Compared to those in the United States, patients in China are more prone to experiencing pre-hospital delays (1318 min vs. 644 min), resulting in a lower thrombolysis rate (2.5% vs. 8.1%) [14]. In contrast, stroke burden and mortality have declined in many developed countries, largely due to improvements in stroke prevention and acute stroke care.

Many factors are associated with ODT in patients with AIS, including age, sex, residential status, educational level, medical history, transportation to the hospital, and efficiency of emergency medical services (EMS) [5, 15, 16]. Patients’ understanding and recognition of stroke symptoms are critical for shortening the ODT [17]. EMS has been shown to reduce pre-hospital delays [18]. Pre-hospital delays differ greatly between China and developed countries, owing to the differences in education, culture, socioeconomic status, medicine, and health [19].

This study aimed to analyze the current situation of ODT in Chinese patients with AIS and its influencing factors through a large-scale, multicenter study and provide evidence for government health departments to make scientific decisions so that more patients can receive timely and optimal treatment, improving prognosis.

Methods

Study design

This study was a multicenter, large-sample, prospective, and observational study.

Study participants

A total of 3495 patients with AIS were recruited from nine hospitals (including Fushun Central Hospital, Wuzhou Workers’ Hospital, Huaihua First People’s Hospital, Inner Mongolia Autonomous Region People’s Hospital, the First Affiliated Hospital of Shaoyang Medical College, Xiangxi Tujia and Miao Autonomous Prefecture People’s Hospital, Affiliated Hospital of Yan’an University, Yueyang Central Hospital, and Zhuzhou Central Hospital) certified as “stroke centers” [20] by the China National Stroke Prevention Project Committee Commission from January to June 2022.

The data collection and entry personnel in all subcenters had professional knowledge of stroke and were trained by the project manager. AIS was diagnosed according to the guidelines [21], and intracranial hemorrhage was excluded using head computed tomography or magnetic resonance imaging [22]. The stroke subtype was based on the trial of Org 10,172 in acute stroke treatment (TOAST) classification of stroke [23].

Inclusion criteria

  1. 1)

    Age ≥ 18 years;

  2. 2)

    AIS diagnosis;

  3. 3)

    Stroke onset ≤ 7 days on admission;

  4. 4)

    provision of consent to participate in this program.

Exclusion criteria

  1. 1)

    Diagnosis of transient ischemic attack, AIS occurring in hospitals, active malignancy, iatrogenic AIS, or cerebral venous sinus thrombosis.

  2. 2)

    Life expectancy less than 3 months; and

  3. 3)

    Diagnosed with severe mental disorders, cognitive disorders, or other conditions.

The ODT was defined as the time from the onset of stroke symptoms to admission to the hospital emergency department or outpatient clinic. For patients whose onset time was uncertain (e.g., wake-up stroke), the last known asymptomatic time was taken as the onset time.

Study variables and groups

The variables studied included sex, age, educational level, residence status, medical insurance, wake-up stroke, first symptom of AIS, distance between onset location and first-visit hospital, transfer method for patients, whether an inter-hospital transfer was performed, medical history, pre-onset modified Rankin scale (mRS) score, stroke severity (according to the National Institutes of Health Stroke Scales [NHISS] first score after onset, moderate and severe stroke have NHISS score 5–14 and NHISS score 15–42 respectively) [24], patient’s knowledge about AIS, and TOAST classification. The division of patients based on ODT is crucial for stratifying stroke care and predicting outcomes [25]. Therefore, patients with AIS were categorized into the ODT ≤ 3 h group and ODT > 3 h group accordingly.

Definition of AIS’s initial symptoms

In this section, we outline a comprehensive understanding of various symptoms encountered in AIS cases, ranging from common manifestations, such as vomiting or unconsciousness, to more specific indicators, including diplopia or dysarthria. The initial symptoms of AIS are defined as follows [26, 27]: (1) Vomiting: Involuntary expulsion of stomach contents through the mouth or nasal cavity. (2) Unconsciousness: Lack of response to external stimuli, coma, or other non-alert states. (3) Paralysis: Complete loss of voluntary motor function, which may affect specific body parts or one side. (4) Diplopia: Simultaneous perception of two images of the same object. (5) Aphasia: Loss or impairment of the ability to express or understand language, characterized by difficulties in speaking, expressing oneself, or understanding others. (6) Dysarthria: Unclear speech or difficulty in pronouncing words due to impaired neuromuscular control. (7) Drooping of the angle of the mouth: Noticeable drooping of one side of the mouth corner, resulting in an asymmetrical facial expression. (8) Headache: Persistent pain or discomfort experienced in the head. (9) Paresthesia: Abnormal sensations felt on the skin, such as numbness, tingling, or burning, without obvious stimulation. (10) Vertigo: Sensation of spinning or movement of the surrounding environment or oneself, often accompanied by balance disorders. 11) Other symptoms included visual disturbances that are difficulty to classify within the categories mentioned above. Detailed symptom information can be provided upon entry of specific data.

ODT calculation method

In this study, we typically documented the precise time when the patient or a witness first noticed stroke symptoms, such as sudden weakness, speech difficulties, or visual disturbances. Alternatively, when the onset time of symptoms was unclear, the following methods were used: (a) When the patient woke up with symptoms, the time before sleep when the last symptom-free period was confirmed was considered as the onset time of symptoms [28, 29]. (b) When the exact time of symptom onset cannot be determined, the time of the last confirmed symptom-free period was considered as the onset time of symptoms [28, 29]. Subsequently, the time of the patient’s arrival at the outpatient department or emergency room, specifically at the triage entrance [30], was documented. The ODT was calculated by subtracting the recorded onset time of stroke symptoms from the time of arrival at the healthcare facility’s door. For instance: (1) Unconsciousness: If the patient lost consciousness, the time when symptoms started was determined based on witness accounts or when the patient was found. This time was considered as the onset time, and then the time when the patient arrived at the hospital was recorded to calculate ODT. (2) Headaches: For localized headaches, the time when the patient or witnesses noticed the headache starting was considered the onset time. Following this, the time of arrival at the hospital was recorded to calculate ODT.

Statistical analysis

All statistical analyses were performed using IBM SPSS Statistics (version 26.0; IBM Corp., Armonk, N.Y., USA). The measured data with normal distribution are expressed as the mean ± standard deviation, and the independent-sample t-test was used for between-group comparisons. When data does not follow a normal distribution, quartiles are used to describe the data. Categorical variables are presented as counts and percentages, and the differences between the two groups were analyzed using the chi-squared test. Firstly, the differences between the ODT ≤ 3 h group and the ODT > 3 h group were analyzed using single-factor analysis; subsequently, the variables with significant differences were included in the binary logistic multivariate analysis, which typically yields confidence intervals for parameter estimates and conducts multicollinearity tests on the variables within the multivariable model. All statistical tests were two-sided, and the threshold for statistical significance was set at P < 0.05.

Results

Overview of ODT in patients with AIS (Table 1)

Table 1 Baseline characteristics and single factor analysis of ODT in patients with acute ischemic stroke
Full size table

A total of 3495 patients with AIS were recruited, and the average is 1,698.88 min (range, 9–10,062 min), and the median is 883 min (Q1 = 181 min, Q3 = 2555 min). There were 763 patients (21.83%) with ODT ≤ 3 h and 2732 patients (78.17%) with ODT > 3 h. Specifically, the number of patients with ODT 3–6 h, 6–12 h, 12–24 h, 24–72 h, and > 72 h accounted for 12.26%, 13.18%, 19.02%, 23.10%, and 10.61% of all patients with AIS, respectively (Fig. 1). There were 2,317 patients (66.29%) with ODT ≤ 24 h and 1178 patients (33.71%) with ODT >24 h.

Fig. 1
figure 1

Distribution ratio of ODT in patients with acute ischemic stroke

ODT: onset-to-door time

Full size image

Baseline characteristics and single-factor analysis of ODT in patients with acute ischemic stroke (Table 1)

Older patients (65.84 ± 11.52 years), lived in the city, had high educational qualifications, had a distance of > 20 km between onset location and the first-visit hospital, reached the hospital by ambulance, and had no inter-hospital transfer had higher ODT (P < 0.05).

There were significant differences in medical history (including current drinking, diabetes, hypertension duration, hyperlipidemia, and atrial fibrillation), the first symptom of AIS (including unconsciousness, aphasia, dysarthria, headache, and vertigo), TOAST classification of stroke, and stroke severity between ODT ≤ 3 h group and ODT > 3 h group (P < 0.05).

Analysis of delayed ODT (> 3 h) with binary logistic regression analysis

Living in rural areas (OR: 1.478, 95% CI: 1.024–2.146) and existing interhospital transfer (OR: 7.479, 95% CI: 2.548–32.337) were risk factors for ODT (Table 2).

Table 2 Analysis of delayed onset-to-door time (> 3 h) with binary logistic regression analysis
Full size table

Distance between the onset location and first-visit hospital ≤ 20 km (OR: 0.355, 95% CI: 0.236–0.530), transportation of patients by EMSs (OR: 0.346, 95% CI: 0.216–0.555), history of atrial fibrillation (OR: 0.375, 95% CI: 0.207–0.679), moderate stroke (OR: 0.644, 95% CI: 0.462–0.901), and severe stroke (OR: 0.506, 95% CI: 0.285–0.908) were protective factors for ODT.

Discussion

Our study demonstrated that only about one-fifth of patients with AIS could reach the hospital within 3 h of symptom onset, and the pre-hospital delay was significant. Some characteristics of pre-hospital delay are risk factors for ODT, such as living in rural areas and existing inter-hospital transfer; meanwhile, distance of ≤ 20 km between onset location and the first-visit hospital, transportation of patients by EMSs, and history of atrial fibrillation and moderate and severe stroke were protective factors for ODT.

Comparison of ODT in China and developed countries

This study showed that the median of ODT was 852 min (range, 215–2459 min), and 21.83% of patients had ODT ≤ 3 h. A multicenter study in the United States showed that 21–40% of patients with AIS reach the hospital within 3 h of symptom onset [31]. In a 2006 study that included 62 subcenters in China that showed similar results, the median ODT was 15 h [13]. Our findings reveal that ODT has not shown much reduction after more than 10 years and is still 3–6 h longer than that in developed countries [30]. Furthermore, a study in 2012–2013 indicated that patients in China experienced more pre-hospital delays compared to those in the United States (1318 min vs. 644 min) [14]. There is a significant difference between ODTs in China and those in developed countries [9, 15, 32, 33].

The World Health Organization’s MONICA manual provides standardized guidelines for registering stroke events [26]. These guidelines ensure that stroke cases are consistently defined and registered, facilitating accurate comparisons across different populations and regions. The MONICA project has played a crucial role in standardizing the registration of acute stroke events, enabling uniform data collection and analysis [26]. Research has demonstrated that adherence to the MONICA criteria for stroke registration is essential for quality control and accurate event validation [34]. The protocols established by the MONICA project have been widely adopted in various studies for registering stroke events, highlighting the broad acceptance and utility of these guidelines [35]. The standardized approach to stroke event registration outlined in the MONICA manual is critical for ensuring the accuracy and consistency of data collected across diverse populations and periods.

Over time, the accuracy of patients’ and witnesses’ recollection of the onset time may diminish, posing a challenge in determining the ODT accurately. We have therefore implemented the following measures to address recall bias in our study design: utilizing standardized questionnaires and interview methods. Additionally, we observed that most patients experience an ODT of less than 1 day. Therefore, we argue that including all patients in the primary analysis, even those with an onset of illness exceeding 24 h, can offer a more comprehensive depiction of the actual situation.

Residential area type and ODT

This study showed that living in rural areas was a risk factor for ODT. Only 8.18% of patients in rural areas in China reached the hospital within 3 h [36], while 45.8% of patients in urban areas reached the hospital within 3 h [37]. Compared with patients in urban areas, those in rural areas are typically older and have lower levels of education, poor housing conditions, and high poverty rates.

China’s economic and healthcare service development has been uneven. Medical and health services supply in China has obvious differences in spatial distribution [19], and the eastern region has the highest medical and health services supply level, followed by the western and central regions. Patients in some parts of China experienced pre-hospital delays owing to poor economic and sanitary conditions [3]. In rural areas, insufficient medical resources, low levels of medical care, and fewer medical staff members make it extremely difficult to meet the needs of patients with stroke. In addition, rural residents have limited access to medical knowledge about first aid; this often results in patients missing the optimal stroke treatment time [38]. The coverage and reimbursement rates of medical insurance in rural areas are lower than those in urban areas, and the frequency of rural patients visiting hospitals is also low [39, 40], which may also result in longer ODTs in rural areas compared with in urban areas.

Distance between onset location and the first-visit hospital and ODT

Our study showed that the distance between the onset location and the initial hospital ≤ 20 km was associated with shorter ODT. Long distances are an important factor delaying patient transport. Improving transport efficiency is a solution that the EMS plays a crucial role in achieving. EMS most closely affects ODT [6]. When there is an optimal EMS, the median ODT can be reduced to 151 min, and the proportion of patients reaching the hospital within 3 h can be increased to 54% [6]. However, EMS usage adds to medical costs; therefore, EMS construction is not feasible in some areas. The awareness of patients regarding EMS usage is also relatively low, and the proportion of patients with AIS using it in China is extremely low, as shown in this study; our results are also consistent with the findings of Wang et al. [14].

Stroke severity and ODT

Our findings showed that patients with moderate or severe stroke were more likely to reach the stroke center within 3 h after onset. Similar results have been reported by Iversen et al. [41]. Patients with moderate or severe stroke were more likely to arrive at the hospital promptly and receive reperfusion therapy. The more serious the stroke, the more it is likely to attract patients’ and bystanders’ attention; this was associated with a higher probability of using EMS. Our research suggests that patients with mild strokes often experience more delays, which can be attributed to several factors: (1) Atypical Symptoms: Mild strokes may manifest with subtle or non-specific symptoms that patients may not immediately recognize as indicative of a stroke. (2) Minimization of symptoms: Patients with mild strokes may diminish the severity of their symptoms or attribute them to other less serious conditions, delaying their decision to seek medical attention. (3) Fear or denial: Some patients may experience fear or denial about the possibility of experiencing a stroke. This psychological barrier can prevent them from promptly seeking medical care. (4) Neglect: Patients with mild strokes may perceive their symptoms as less urgent and may prioritize other obligations over seeking immediate medical attention. Thus, we emphasize the importance of concentrating on patients with mild stroke and the significance of timely referrals.

Bystanders are more likely to notice typical stroke symptoms such as limb weakness, speech disturbance, and walking difficulties [42]. It has been reported that living alone increases admission delay, and the recognition of symptoms by bystanders may shorten it [43]. The onset of symptoms can influence a patient’s decision-making. When dysarthria or decreased muscle strength were the first symptoms, the rate of hospital visits increased significantly within 4.5 h (P < 0.01) [6, 44, 45]. The more prominent the impact of the first symptom on daily living, the easier it is to attract the attention of patients and their families, the stronger the desire to seek medical attention, and the shorter the ODT. Only 53.8% of patients with posterior circulation stroke reach the hospital within 3 h, compared to 68.4% of patients with anterior circulation stroke [7]. Compared with dysphagia and limb weakness caused by posterior circulation stroke, posterior circulation stroke often presents with non-specific symptoms such as dizziness, vertigo, and nausea, which are easily attributed to poor rest, anxiety, and failure. Cryptogenic stroke is common in young people [46]. However, young patients often ignore the possibility of stroke onset, which leads to a pre-hospital delay. Patients often choose self-observation when stroke occurs and only visit the hospital if the symptoms persist or worsen because of the inability to identify stroke in an accurate and timely manner [13]. Recognizing symptoms of stroke is an independent factor associated with early arrival [47].

Transportation of patients to hospital and ODT

Most patients in our study chose to go to the hospital by themselves, which increased the probability of inter-hospital transfer and caused pre-hospital delays. There are two main specific situations of interhospital transfer: (1) Patients who independently seek medical attention may arrive at a hospital without a stroke center, necessitating their transfer to one. (2) In our study, patients who utilized an ambulance were directly transported to a stroke center. However, interhospital transfers may occur for these patients if the initial hospital cannot administer mechanical thrombectomy treatment. Only one in eight patients with stroke in China arrived at the hospital via EMS [48], compared to 59.6% in the DASH II study [49]. Our study showed that patients who visited the hospital via EMSs (ambulances) had shorter ODTs. Moreover, many studies have demonstrated a reduction in pre-hospital delays via EMS [50, 51]. The 2019 AHA/ASA guidelines indicate that patients with stroke who use EMS arrive at the emergency department earlier, and more eligible patients receive IVT [27]. A stroke emergency map (an intelligent EMS that can guide ambulances to transport patients more effectively) in China has effectively shortened the ODT and improved the thrombolysis rate [52].

Atrial fibrillation (AF) and ODT

Our study demonstrated that patients with AF are not prone to pre-hospital delays, given that strokes resulting from atrial fibrillation tend to be more severe [53, 54]. Cardiac stroke typically occurs abruptly with evident symptoms. Patients often experience obvious discomfort, which helps in raising the alert faster, causing them to seek medical attention in time. The multivariate regression analysis revealed that AF was an independent factor associated with early arrival [47]. AF and a history of coronary artery disease accelerated the presentation to the hospital [13]; sudden onset of symptoms, loss of consciousness, recognition of symptoms as stroke, and feelings of fear and panic were associated with hospital arrival within 3 h.

The impact of the COVID-19 pandemic on patients with stroke

The COVID-19 pandemic has markedly impacted patients with stroke, affecting different aspects of stroke care. Studies have demonstrated a decrease in hospital admissions for transient ischemic attacks and mild to moderate stroke during the COVID-19 era [55, 56]. Additionally, the pandemic has disrupted the chain of acute stroke care, resulting in potential risks such as decreased thrombectomy rates [57, 58] and modifications in the acute stroke care pathway [59]. Furthermore, the pandemic has caused a delay in patients with AIS seeking treatment at stroke centers [4]. Both pre- and post-hospital delays have been considerably prolonged, and the number of patients receiving intravenous thrombolysis treatment has decreased [60].

Moreover, a higher occurrence of severe strokes and an increased in-hospital mortality rate have been observed during the COVID-19 pandemic [61]. The pandemic has also raised concerns about the collateral damage on stroke emergency services, as well as the necessity to reorganize stroke networks in order to provide optimal care while mitigating the risk of transmission [55]. In conclusion, the COVID-19 pandemic has had a multifaceted impact on patients with stroke, affecting various aspects of stroke care, including hospital admissions, acute stroke care pathways, delayed presentation, and treatment.

Limitations

The stroke population recruited in each subcenter of this study had certain regional characteristics; therefore, generalization of the research conclusions was affected to some extent. This study lacks detailed information on imaging, timing of EMS notification, and adjustment for socioeconomic factors. These limitations may have specific implications for interpreting and inferring research results: (1) Lack of imaging information: Inability to accurately assess disease severity and progression. (2) Lack of information on EMS notification time: Inability to determine the timeliness of patient medical assistance and the absence of a reference for optimizing emergency response systems. (3) Failure to adjust for socioeconomic factors: Socioeconomic status may influence patients’ healthcare-seeking behavior. Neglecting socioeconomic factors may introduce bias, potentially leading to an overestimation or underestimation of the impact of certain factors. Lastly, the COVID-19 pandemic presented significant challenges for this study. Lockdown restrictions and safety concerns led to limited data collection, resulting in a smaller sample size.

Conclusions

Pre-hospital delays for patients with AIS are a serious medical and social issue that needs immediate attention. The majority (approximately four in five) of patients with AIS fail to reach the hospital within a 3-h prime time for stroke treatment, leaving much room for improvement in this regard. Reasonable distribution of hospitals that provide treatments to residents staying nearby, minimizing interhospital transfers, paying special attention to patients with moderate or severe stroke, and encouraging patients to reach the hospital by ambulance are recommended measures that can help reduce pre-hospital delays. The findings should be interpreted considering the constraints imposed by the COVID-19 pandemic. Future longitudinal studies could investigate the lasting effects of the pandemic on the research topic.

Data availability

The datasets used and/or analyzed during this study are available from the corresponding author upon reasonable request.

Abbreviations

AF:

Atrial fibrillation

AIS:

Acute ischemic stroke

EMS:

Emergency medical services

IVT:

Intravenous thrombolysis

mRS:

Modified Rankin scale

NHISS:

National Institutes of Health Stroke Scales

ODT:

Onset-to-door time

TOAST:

Trial of Org 10,172 in acute stroke treatment

References

  1. Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2019;394:1145–58. https://doi.org/10.1016/S0140-6736(19)30427-1

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hacke W, Kaste M, Fieschi C, Toni D, Lesaffre E, von Kummer R, et al. Intravenous thrombolysis with recombinant tissue plasminogen activator for acute hemispheric stroke. The European Cooperative Acute Stroke Study (ECASS). JAMA. 1995;274:1017–25.

    Article  CAS  PubMed  Google Scholar 

  3. Wang Y, Wu D, Zhou Y, Zhao X, Wang C, Wang Y. Thrombolysis in the emergency department in China: results from an emergency department registry in 7 urban hospitals. Chin J Stroke. 2009;4:23–8. https://doi.org/10.3969/j.issn.1673-5765.2009.01.005. [Chinese].

    Article  CAS  Google Scholar 

  4. Schirmer CM, Ringer AJ, Arthur AS, Binning MJ, Fox WC, James RF, et al. Delayed presentation of acute ischemic strokes during the COVID-19 crisis. J Neurointerv Surg. 2020;12:639–42. https://doi.org/10.1136/neurintsurg-2020-016299

    Article  PubMed  Google Scholar 

  5. Teo KC, Leung WCY, Wong YK, Liu RKC, Chan AHY, Choi OMY, et al. Delays in stroke onset to hospital arrival time during COVID-19. Stroke. 2020;51:2228–31. https://doi.org/10.1161/STROKEAHA.120.030105

    Article  CAS  PubMed  Google Scholar 

  6. Rossnagel K, Jungehülsing GJ, Nolte CH, Müller-Nordhorn J, Roll S, Wegscheider K, et al. Out-of-hospital delays in patients with acute stroke. Ann Emerg Med. 2004;44:476–83. https://doi.org/10.1016/j.annemergmed.2004.06.019

    Article  PubMed  Google Scholar 

  7. Sommer P, Seyfang L, Posekany A, Ferrari J, Lang W, Fertl E, et al. Prehospital and intra-hospital time delays in posterior circulation stroke: results from the Austrian stroke unit registry. J Neurol. 2017;264:131–8. https://doi.org/10.1007/s00415-016-8330-x

    Article  PubMed  Google Scholar 

  8. Khatri P, Yeatts SD, Mazighi M, Broderick JP, Liebeskind DS, Demchuk AM, et al. Time to angiographic reperfusion and clinical outcome after acute ischaemic stroke: an analysis of data from the interventional management of stroke (IMS III) phase 3 trial. Lancet Neurol. 2014;13:567–74. https://doi.org/10.1016/S1474-4422(14)70066-3

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tong D, Reeves MJ, Hernandez AF, Zhao X, Olson DM, Fonarow GC, et al. Times from symptom onset to hospital arrival in the get with the guidelines–stroke program 2002 to 2009: temporal trends and implications. Stroke. 2012;43:1912–17. https://doi.org/10.1161/STROKEAHA.111.644963

    Article  PubMed  Google Scholar 

  10. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med. 2020;382:1199–207. https://doi.org/10.1056/NEJMoa2001316

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Katan M, Luft A. Global burden of stroke. Semin Neurol. 2018;38:208–11. https://doi.org/10.1055/s-0038-1649503

    Article  PubMed  Google Scholar 

  12. Wang W, Jiang B, Sun H, Ru X, Sun D, Wang L, et al. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults. Circulation. 2017;135:759–71. https://doi.org/10.1161/CIRCULATIONAHA.116.025250

    Article  PubMed  Google Scholar 

  13. Jin H, Zhu S, Wei JW, Wang J, Liu M, Wu Y, et al. Factors associated with prehospital delays in the presentation of acute stroke in urban China. Stroke. 2012;43:362–70. https://doi.org/10.1161/STROKEAHA.111.623512

    Article  PubMed  Google Scholar 

  14. Wangqin R, Laskowitz DT, Wang Y, Li Z, Wang Y, Liu L, et al. International comparison of patient characteristics and quality of care for ischemic stroke: analysis of the China national stroke registry and the American Heart Association get with the guidelines–stroke program. J Am Heart Assoc. 2018;7:e010623. https://doi.org/10.1161/JAHA.118.010623

    Article  PubMed  PubMed Central  Google Scholar 

  15. Tanaka Y, Nakajima M, Hirano T, Uchino M. Factors influencing pre-hospital delay after ischemic stroke and transient ischemic attack. Intern Med. 2009;48:1739–44. https://doi.org/10.2169/internalmedicine.48.2378

    Article  PubMed  Google Scholar 

  16. Lee EJ, Kim SJ, Bae J, Lee EJ, Kwon OD, Jeong HY, et al. Impact of onset-to-door time on outcomes and factors associated with late hospital arrival in patients with acute ischemic stroke. PLoS ONE. 2021;16:e0247829. https://doi.org/10.1371/journal.pone.0247829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Fang J, Yan W, Jiang GX, Li W, Cheng Q. Time interval between stroke onset and hospital arrival in acute ischemic stroke patients in Shanghai, China. Clin Neurol Neurosurg. 2011;113:85–8. https://doi.org/10.1016/j.clineuro.2010.09.004

    Article  PubMed  Google Scholar 

  18. Keskin O, Kalemoğlu M, Ulusoy RE. A clinic investigation into prehospital and emergency department delays in acute stroke care. Med Princ Pract. 2005;14:408–12. https://doi.org/10.1159/000088114

    Article  PubMed  Google Scholar 

  19. Chen B, Jin F. Spatial distribution, regional differences, and dynamic evolution of the medical and health services supply in China. Front Public Health. 2022;10:1020402. https://doi.org/10.3389/fpubh.2022.1020402

    Article  PubMed  PubMed Central  Google Scholar 

  20. Chao B, Cao L, Tu W, Wang L. Construction of national stroke center network system. Int J Biomed Eng. 2019;42:363–66. https://doi.org/10.3760/cma.j.issn.1673-4181.2019.05.001. [Chinese].

    Article  Google Scholar 

  21. Jauch EC, Saver JL, Adams HP Jr, Bruno A, Connors JJ, Demaerschalk BM, 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. https://doi.org/10.1161/STR.0b013e318284056a

    Article  PubMed  Google Scholar 

  22. The World Health Organization MONICA Project. (monitoring trends and determinants in cardiovascular disease): a major international collaboration. WHO MONICA project principal investigators. J Clin Epidemiol. 1988;41:105–14. https://doi.org/10.1016/0895-4356(88)90084-4

    Article  Google Scholar 

  23. Adams HP Jr, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993;24:35–41. https://doi.org/10.1161/01.str.24.1.35

    Article  PubMed  Google Scholar 

  24. Chang KC, Tseng MC, Weng HH, Lin YH, Liou CW, Tan TY. Prediction of length of stay of first-ever ischemic stroke. Stroke. 2002;33:2670–4. https://doi.org/10.1161/01.str.0000034396.68980.39

    Article  PubMed  Google Scholar 

  25. Kamal H, Assaf S, Kabalan M, El Maissi R, Salhab D, Rahme D, et al. Evaluation of stroke pre-hospital management in Lebanon from symptoms onset to hospital arrival and impact on patients’ status at discharge: a pilot study. BMC Neurol. 2022;22:494. https://doi.org/10.1186/s12883-022-03018-0

    Article  PubMed  PubMed Central  Google Scholar 

  26. Stegmayr B, Vinogradova T, Malyutina S, Peltonen M, Nikitin Y, Asplund K. Widening gap of stroke between east and west. Eight-year trends in occurrence and risk factors in Russia and Sweden. Stroke. 2000;31:2–8. https://doi.org/10.1161/01.str.31.1.2

    Article  CAS  PubMed  Google Scholar 

  27. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, et al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2019;50:e344–418. https://doi.org/10.1161/STR.0000000000000211

    Article  PubMed  Google Scholar 

  28. Saver JL, Fonarow GC, Smith EE, Reeves MJ, Grau-Sepulveda MV, Pan W, et al. Time to treatment with intravenous tissue plasminogen activator and outcome from acute ischemic stroke. JAMA. 2013;309:2480–8. https://doi.org/10.1001/jama.2013.6959

    Article  CAS  PubMed  Google Scholar 

  29. Strbian D, Ahmed N, Wahlgren N, Lees KR, Toni D, Roffe C, et al. Trends in door-to-thrombolysis time in the safe implementation of stroke thrombolysis registry: effect of center volume and duration of registry membership. Stroke. 2015;46:1275–80. https://doi.org/10.1161/STROKEAHA.114.007170

    Article  PubMed  Google Scholar 

  30. Evenson KR, Foraker RE, Morris DL, Rosamond WD. A comprehensive review of prehospital and in-hospital delay times in acute stroke care. Int J Stroke. 2009;4:187–99. https://doi.org/10.1111/j.1747-4949.2009.00276.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Majersik JJ, Smith MA, Zahuranec DB, Sánchez BN, Morgenstern LB. Population-based analysis of the impact of expanding the time window for acute stroke treatment. Stroke. 2007;38:3213–7. https://doi.org/10.1161/STROKEAHA.107.491852

    Article  PubMed  Google Scholar 

  32. Faiz KW, Sundseth A, Thommessen B, Rønning OM. Prehospital delay in acute stroke and TIA. Emerg Med J. 2013;30:669–74. https://doi.org/10.1136/34emermed-2012-201543

    Article  PubMed  Google Scholar 

  33. Matsuo R, Yamaguchi Y, Matsushita T, Hata J, Kiyuna F, Fukuda K, et al. Association between onset-to-door time and clinical outcomes after ischemic stroke. Stroke. 2017;48:3049–56. https://doi.org/10.1161/STROKEAHA.117.018132

    Article  PubMed  Google Scholar 

  34. Angerud KH, Brulin C, Näslund U, Eliasson M. Patients with diabetes are not more likely to have atypical symptoms when seeking care of a first myocardial infarction. An analysis of 4028 patients in the Northern Sweden MONICA Study. Diabet Med. 2012;29:e82–7. https://doi.org/10.1111/j.1464-5491.2011.03561.x

    Article  CAS  PubMed  Google Scholar 

  35. Truelsen T, Bonita R. Epidemiological transition of stroke in China? Stroke. 2008;39:1653–4. https://doi.org/10.1161/strokeaha.107.510552

    Article  PubMed  Google Scholar 

  36. Zeng R, He X, Zhang J. Analysis of influencing factors of delayed thrombolytic therapy for acute cerebral infarction in rural areas of a hospital. Chin Rural Health Service Adm. 2016;36:394–7. [Chinese].

    Google Scholar 

  37. Bi Q, Zhang Z, Zhang WW, Li Q. Study on prehospital time and influencing factors of stroke patients in 15 Chinese cities. Chin J Epidemiol. 2006;27:996–9. https://doi.org/10.3760/j.issn:0254-6450.2006.11.019. [Chinese].

    Article  Google Scholar 

  38. Yi J, Zhuo C, Yan X. Status quo and countermeasures of the popularity of emergency medical knowledge in China rural areas. Chin Rural Health Service Adm. 2014;34:366–8. [Chinese].

    Google Scholar 

  39. Kozera G, Chwojnicki K, Gójska-Grymajło A, Gąsecki D, Schminke U, Nyka WM, et al. Pre-hospital delays and intravenous thrombolysis in urban and rural areas. Acta Neurol Scand. 2012;126:171–7. https://doi.org/10.1111/j.1600-0404.2011.01616.x

    Article  CAS  PubMed  Google Scholar 

  40. Lu Z, Fang Y, Wang Z, Wang X, Yang H, Wang L. Social determinants of pre-hospital delays after onset of stroke. Chin J Health Manage. 2014;8:40–3. https://doi.org/10.3760/cma.j.issn.1674-0815.2014.01.012. [Chinese].

    Article  Google Scholar 

  41. Iversen AB, Blauenfeldt RA, Johnsen SP, Sandal BF, Christensen B, Andersen G, et al. Understanding the seriousness of a stroke is essential for appropriate help-seeking and early arrival at a stroke centre: a cross-sectional study of stroke patients and their bystanders. Eur Stroke J. 2020;5:351–61. https://doi.org/10.1177/2396987320945834

    Article  PubMed  PubMed Central  Google Scholar 

  42. Nowacki P, Nowik M, Bajer-Czajkowska A, Porebska A, Zywica A, Nocoń D, et al. Patients’ and bystanders’ awareness of stroke and pre-hospital delay after stroke onset: perspectives for thrombolysis in West Pomerania Province, Poland. Eur Neurol. 2007;58:159–65. https://doi.org/10.1159/000104717

    Article  PubMed  Google Scholar 

  43. Derex L, Adeleine P, Nighoghossian N, Honnorat J, Trouillas P. Factors influencing early admission in a French stroke unit. Stroke. 2002;33:153–9. https://doi.org/10.1161/hs0102.100533

    Article  PubMed  Google Scholar 

  44. Gonzalez-Aquines A, Cordero-Pérez AC, Cristobal-Niño M, Pérez-Vázquez G, Góngora- Rivera F, GECEN Investigators. Contribution of onset-to-alarm time to prehospital delay in patients with ischemic stroke. J Stroke Cerebrovasc Dis. 2019;28:104331. https://doi.org/10.1016/j.jstrokecerebrovasdis.2019.104331

    Article  PubMed  Google Scholar 

  45. Terecoasă EO, Radu RA, Negrilă A, Enache I, Cășaru B, Tiu C. Pre-hospital delay in acute ischemic stroke care: current findings and future perspectives in a tertiary stroke center from romania-a cross-sectional study. Med (Kaunas). 2022;58:1003. https://doi.org/10.3390/medicina58081003

    Article  Google Scholar 

  46. Fonseca AC, Ferro JM. Cryptogenic stroke. Eur J Neurol. 2015;22:618–23. https://doi.org/10.1111/ene.12673

    Article  CAS  PubMed  Google Scholar 

  47. Song D, Tanaka E, Lee K, Sato S, Koga M, Kim YD, et al. Factors associated with early hospital arrival in patients with acute ischemic stroke. J Stroke. 2015;17:159–67. https://doi.org/10.5853/jos.2015.17.2.159

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Gu HQ, Rao ZZ, Yang X, Wang CJ, Zhao XQ, Wang YL, et al. Use of emergency medical services and timely treatment among ischemic stroke. Stroke. 2019;50:1013–6. https://doi.org/10.1161/STROKEAHA.118.024232

    Article  PubMed  Google Scholar 

  49. Mathews R, Peterson ED, Li S, Roe MT, Glickman SW, Wiviott SD, et al. Use of emergency medical service transport among patients with ST-segment-elevation myocardial infarction: findings from the national cardiovascular data registry acute coronary treatment intervention outcomes network registry-get with the guidelines. Circulation. 2011;124:154–63. https://doi.org/10.1161/CIRCULATIONAHA.110.002345

    Article  PubMed  Google Scholar 

  50. Tong X, Wiltz JL, George MG, Odom EC, Coleman King SM, Chang T, et al. A decade of improvement in door-to-needle time among acute ischemic stroke patients, 2008 to 2017. Circ Cardiovasc Qual Outcomes. 2018;11:e004981. https://doi.org/10.1161/CIRCOUTCOMES.118.004981

    Article  PubMed  PubMed Central  Google Scholar 

  51. Davis NW, Bailey M, Buchwald N, Farooqui A, Khanna A. Factors that influence door-to-needle administration for acute stroke patients in the emergency department. J Neurosci Nurs. 2021;53:134–9. https://doi.org/10.1097/JNN.0000000000000590

    Article  PubMed  Google Scholar 

  52. Ye S, Hu S, Lei Z, Li Z, Li W, Sui Y, et al. Shenzhen stroke emergency map improves access to rt-PA for patients with acute ischaemic stroke. Stroke Vasc Neurol. 2019;4:115–22. https://doi.org/10.1136/svn-2018-000212

    Article  PubMed  PubMed Central  Google Scholar 

  53. Dahl OE. New oral antithrombotics: focus on dabigatran, an oral, reversible direct thrombin inhibitor for the prevention and treatment of venous and arterial thromboembolic disorders. Vasc Health Risk Manag. 2012;8:45–57. https://doi.org/10.2147/VHRM.S26482

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Fang MC, Go AS, Chang Y, Borowsky LH, Pomernacki NK, Udaltsova N, et al. Long-term survival after ischemic stroke in patients with atrial fibrillation. Neurology. 2014;82:1033–7. https://doi.org/10.1212/WNL.0000000000000248

    Article  PubMed  PubMed Central  Google Scholar 

  55. Diegoli H, Magalhães PSC, Martins SCO, Moro CHC, França PHC, Safanelli J, et al. Decrease in hospital admissions for transient ischemic attack, mild, and moderate stroke during the COVID-19 era. Stroke. 2020;51:2315–21. https://doi.org/10.1161/STROKEAHA.120.030481

    Article  CAS  PubMed  Google Scholar 

  56. Luo W, Li J, Li Z, Luo X, Chen M, Cai C. Effects of the COVID-19 pandemic on reperfusion therapy for acute ischemic stroke patients in Huizhou City, China. Neurol Sci. 2021;42:467–73. https://doi.org/10.1007/s10072-020-04938-w

    Article  PubMed  PubMed Central  Google Scholar 

  57. Rudilosso S, Laredo C, Vera V, Vargas M, Renú A, Llull L, et al. Acute stroke care is at risk in the era of COVID-19: experience at a comprehensive stroke center in Barcelona. Stroke. 2020;51:1991–5. https://doi.org/10.1161/STROKEAHA.120.030329

    Article  CAS  PubMed  Google Scholar 

  58. Al Kasab S, Almallouhi E, Alawieh A, Jabbour P, Sweid A, Starke RM, et al. Alarming downtrend in mechanical thrombectomy rates in African American patients during the COVID-19 pandemic-insights from STAR. J Neurointerv Surg. 2021;13:304–7. https://doi.org/10.1136/neurintsurg-2020-016946

    Article  PubMed  Google Scholar 

  59. Kim TJ, Kim BJ, Gwak DS, Lee JS, Kim JY, Lee KJ, et al. Modification of acute stroke pathway in Korea after the coronavirus disease 2019 outbreak. Front Neurol. 2020;11:597785. https://doi.org/10.3389/fneur.2020.597785

    Article  PubMed  PubMed Central  Google Scholar 

  60. Gu S, Dai Z, Shen H, Bai Y, Zhang X, Liu X, et al. Delayed stroke treatment during COVID-19 pandemic in China. Cerebrovasc Dis. 2021;50(6):715–21. https://doi.org/10.1159/000517075

    Article  CAS  PubMed  Google Scholar 

  61. Katsanos AH, Palaiodimou L, Zand R, Yaghi S, Kamel H, Navi BB, et al. Changes in stroke hospital care during the COVID-19 pandemic: a systematic review and meta-analysis. Stroke. 2021;52:3651–60. https://doi.org/10.1161/STROKEAHA.121.034601

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Fushun Central Hospital, Wuzhou Workers’ Hospital, Huaihua First People’s Hospital, Inner Mongolia Autonomous Region People’s Hospital, the First Affiliated Hospital of Shaoyang Medical College, Xiangxi Tujia and Miao Autonomous Prefecture People’s Hospital, Affiliated Hospital of Yan’an University, Yueyang Central Hospital, and Zhuzhou Central Hospital for their support towards this study.

Funding

This study was supported by the Research and Promotion Project on Appropriate Intervention Techniques for High-Risk Stroke Population in China (No. GN-2020R0002), Project of Teaching Reform in Shenzhen Second People’s Hospital (No. 202209), Project of Teaching Reform in School of Medicine of Shenzhen University (No. XBJG202205), and Integration Project of Prevention and Cure of Nervous System Disease in Shenzhen.

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Author notes

  1. Yuqi Liao, Wenwei Qi and Shuting Li contributed equally to this work and share the first authorship.

  2. Liming Cao and Lijie Ren contributed equally to this work.

Authors and Affiliations

  1. School of Medicine, Shenzhen University, Shenzhen, China

    Yuqi Liao & Runyu Xia

  2. National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China

    Wenwei Qi

  3. Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Wenwei Qi

  4. School of Statistics, Shandong Technology and Business University, Yantai, China

    Shuting Li & Xin Shi

  5. Department of Neurology, The First Affiliated Hospital of Shenzhen University, 3002 Sungang West Road, Futian District, Shenzhen City, 518000, China

    Xiaohong Wu, Feng Chi, Limin Qin, Liming Cao & Lijie Ren

  6. Department of Neurology, Shenzhen Second People’s Hospital, Shenzhen, China

    Xiaohong Wu, Feng Chi, Limin Qin & Lijie Ren

  7. Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China

    Liming Cao

  8. School of Health Management, China Medical University, Shenyang, China

    Xin Shi

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Contributions

LC, LR, and XW designed the study. LC, FC, LQ and RX performed the research. XS and LR provided advice on the study. YL, WQ and SL analyzed the data. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Liming Cao.

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Ethics approval and consent to participate

The study was approved by the ethics review board of the First Affiliated Hospital of Shenzhen University (No. 20200727003-FS01-XZ2022). Informed written/verbal consent was obtained from all participants. It was performed per the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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The authors declare no competing interests.

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Liao, Y., Qi, W., Li, S. et al. Analysis of onset-to-door time and its influencing factors in Chinese patients with acute ischemic stroke during the 2020 COVID-19 epidemic: a preliminary, prospective, multicenter study. BMC Health Serv Res 24, 615 (2024). https://doi.org/10.1186/s12913-024-11088-8

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BMC Health Services Research

ISSN: 1472-6963

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