Factors influencing pre-hospital delay among acute ischemic stroke patients in the midlands of China

In-Hospital Emergency Treatment Delay Among Chinese Patients with Acute Ischaemic Stroke: Relation to Hospital Arrivals and Implications for Triage Pathways

Introduction

Timely access to emergency treatment during in-hospital care phase is critical for managing the onset of acute ischaemic stroke (AIS), particularly in developing countries. We aimed to explore in-hospital emergency treatment delay and the relation of door-to-needle (DTN) time to ambulance arrivals vs walk-in arrivals.

Methods

Data were collected from 1276 Chinese AIS patients admitted to a general, tertiary-level hospital for intravenous thrombolysis. Information on patients' characteristics and time taken during in-hospital emergency treatment was retrieved from the hospital registry data and medical records. Ambulance arrival was defined as being transported by emergency ambulance services, while walk-in arrival was defined as arriving at hospital by regular vehicle. In-hospital emergency treatment delay occurred when the DTN time exceeded 60 minutes. We performed multivariable logistic regression analysis to explore the association between hospital arrivals (by ambulance vs by walk-in) and treatment delay after adjustment for age, sex, education, marital status, residence, medical insurance, number of symptoms, clinical severity and survival outcome.

Results

Over half (53.76%) of patients aged over 60 years. Around one-fifth (20.61%) of patients admitted to hospital through emergency ambulance services, while their counterparts arrived by regular vehicle. Overall, the median time taken from the hospital door to treatment initiation was 86.0 minutes. Patients arrived by ambulance (adjusted odds ratio [aOR] = 1.744, 95% confidence interval [CI] = 1.185-2.566, p = 0.005), had higher socio-economic status (aOR = 1.821, 95% CI = 1.251-2.650; p = 0.002), or paid out-of-pocket (aOR = 2.323, 95% CI = 1.764-3.060; p < 0.001) had an increased likelihood of in-hospital emergency treatment delays.

Conclusion

In-hospital emergency treatment delay is common in China, and occurs throughout the entire emergency treatment journey. Having a triage pathway involving hospital arrival by ambulance seems to be more likely to experience in-hospital emergency treatment delay. Further efforts to improve triage pathways may require qualitative evidence on provider- and institutional-level factors associated with in-hospital emergency treatment delay.

Emergency medical service utilization among acute ischemic stroke patients in Beijing: An observational study

Objective

To investigate emergency medical service (EMS) utilization and its associated factors in patients with acute ischemic stroke (AIS), and further explore the urban-rural differences.

Methods

Medical records for AIS in all emergency departments in Beijing were obtained from the Beijing Emergency Care Database from January 2018 to December 2021. EMS utilization was described and factors associated with EMS use were examined by multivariable logistic regression models with the generalized estimating equations. Results were compared between urban and rural districts.

Results

A total of 24,296 AIS patients were included in the analysis, and 11,190 (46.1%) were transported to hospitals by EMS. The percentage of EMS usage in urban areas was significantly higher than that in rural areas (53.6 vs. 34.4%, P < 0.001). From 2018 to 2021, EMS utilization was on the increase (P-value for trend <0.001) with a higher average annual growth rate in rural areas (12.6%) than in urban (6.4%). Factors associated with EMS utilization were age (OR: 1.20 per 10-year increase, 95% CI: 1.17–1.23), NIHSS scores, off-hour arrival (OR: 1.32, 95% CI: 1.23–1.37), treatment in tertiary hospitals (OR: 1.75, 95% CI: 1.60–1.92), and possessing comorbidities such as coronary artery disease (OR: 1.15, 95% CI: 1.17–1.24), atrial fibrillation (OR: 1.56, 95% CI: 1.41–1.73), prior stroke (OR: 0.84, 95% CI: 0.78-0.90) or dyslipidemia (OR: 0.78, 95% CI: 0.71–0.85).

Conclusion

This study demonstrated an inadequate use of EMS among AIS patients in Beijing, especially in rural areas, and revealed several associated factors. Enhanced education programs and EMS accessibility are necessary particularly for high-risk individuals and regions.

Time Window for Acute Stroke Management: A Cross-Sectional Study Among Community Healthcare Practitioners in Primary Care

Introduction

Time-to-treatment window is critical for managing acute ischaemic stroke. The community healthcare practitioners (CHPs) who deliver frontline care in the health system play an important role in stroke prevention and treatment.

Methods

A multi-stage sampling design was adopted in Guangdong province, China. A total of 997 CHPs who participated in the survey were divided into two groups (the awareness group vs the unawareness group) according to their knowledge on the time window for stroke management. Logistic regression analysis was performed to explore factors associated with the awareness of “time window”.

Results

Overall, less than half (49.1%) of CHPs were aware of the time window for stroke management. The proportion of CHPs who were able to recognise stroke symptoms were higher in the awareness group (42.7%) than that in the unawareness group (38.8%). Most CHPs (82.9%) in the awareness group had the knowledge about the effectiveness of intravenous thrombolysis in treating acute cerebral infarction, whereas this was perceived by only less than half (43.6%) of CHPs in the unawareness group. Factors associated with the knowledge of time window for stroke management included participation in cerebrovascular disease management training (adjusted odds ratio [aOR]=4.203, 95% CI: 1.707–10.348, p=0.002), awareness of the time frame for CT initiation (aOR=5.214, 95% CI: 1.803–15.078, p=0.002) and for urokinase thrombolysis administration (aOR=11.927, 95% CI: 4.393–32.382, p<0.001), accurate perceptions about the target for blood pressure lowering (aOR=4.181, 95% CI: 1.713–10.207, p=0.002) and blood glucose control (aOR=2.446, 95% CI: 1.019–5.869, p=0.045), and the familiarity with prehospital stroke management principles (aOR=3.593, 95% CI: 1.383–9.332, p=0.009).

Conclusion

The CHPs need to enhance their ability to address the acute ischaemic stroke onset promptly to provide effective treatment within the beneficial “time window”. This may help improve the stroke chain of survival with better multidisciplinary decision support systems that enable optimal stroke care delivery.

Regional Differences in Hospital Costs of Acute Ischemic Stroke in China: Analysis of Data From the Chinese Acute Ischemic Stroke Treatment Outcome Registry

Background and Purpose: Studies on the regional differences in hospital costs of acute ischemic stroke (AIS) are scarce in China. We aimed to explore the regional differences in hospital costs and identify the determinants of hospital costs in each region. Methods: Data were collected from the Chinese Acute Ischemic Stroke Treatment Outcome Registry (CASTOR), a multicenter prospective study on patients diagnosed with AIS and hospitalized from 2015 to 2017. Univariate and multivariate analyses were undertaken to identify the determinants of hospital costs of AIS. Results: A total of 8,547 patients were included in the study, of whom 3,700 were from the eastern area, 2,534 were from the northeastern area, 1,819 were from the central area, and 494 were from the western area. The median hospital costs presented a significant difference among each region, which were 2175.9, 2175.1, 2477.7, and 2282.4 dollars in each area, respectively. Each region showed a similar hospital cost proportion size order of cost components, which was Western medicine costs, other costs, diagnostic costs, and traditional medicine costs, in descending order. Male sex, diabetes mellitus, severe stroke symptoms, longer length of stay, admission to the intensive care unit, in-hospital complications of hemorrhage, and thrombectomy were independently associated with hospital costs in most regions. Conclusion: Hospital costs in different regions showed a similar proportion size order of components in China. Each region had different determinants of hospital costs, which reflected its current medical conditions and provided potential determinants for increasing medical efficiency according to each region's situation.

Treatment delay from aneurysmal subarachnoid hemorrhage to endovascular treatment: a high-volume hospital experience

Background

Early treatment for patients with aneurysmal subarachnoid hemorrhage (aSAH) could significantly reduce the risk of re-bleeding and improve clinical outcomes. We assessed the different time intervals from the initial hemorrhage, admission, and endovascular treatment and identified the risk factors contributing to delay.

Methods

Between February 2017 and December 2019, 422 consecutive aSAH patients treated in a high-volume hospital were collected and reviewed. Risk factors contributing to admission delay and treatment delay were analyzed with univariate and multivariate analysis.

Results

One hundred twenty-two (28.9%) were admitted to the high-volume hospital at the day of symptom onset and 386 (91.5%) were treated with endovascular management at the same day of admission. The multivariate analysis found that younger age (P = 0.022, OR = 0.981, 95% CI 0.964–0.997) and good Fisher score (P = 0.002, OR = 0.420, 95% CI 0.245–0.721) were independent risk factors of admission delay. None was found to be related with treatment delay. Multivariate analysis (OR (95% CI)) showed that higher age 1.027 (1.004–1.050), poorer Fisher score 3.496 (1.993–6.135), larger aneurysmal size 1.112 (1.017–1.216), and shorter interval between onset to admission 1.845 (1.018–3.344) were independent risk factors of poorer clinical outcome.

Conclusion

Treatment delay was mainly caused by pre-hospital delay including delayed admission and delayed transfer. Our experience showed that cerebrovascular team could provide early treatment for aSAH patients. Younger age and good Fisher score were significantly related with admission delay. However, admission delay was further significantly correlated with better clinical outcome.

Delayed Stroke Treatment during COVID-19 Pandemic in China

BACKGROUND

Social distance, quarantine, pathogen testing, and other preventive strategies implemented during CO-VID-19 pandemic may negatively influence the management of acute ischemic stroke (AIS).

OBJECTIVE

The current study aimed to evaluate the impacts of COVID-19 pandemic on treatment delay of AIS in China.

METHODS

This study included patients with AIS admitted in 2 hospitals in Jiangsu, China. Patients admitted before and after the COVID-19 pandemic outbreak (January 31, 2020, as officially announced by the Chinese government) were screened to collect sociodemographic data, medical history information, and symptom onset status from clinical medical records and compared for pre- (measured as onset-to-door time [ODT]) and posthospital delay (measured as door-to-needle time [DNT]). The influencing factors for delayed treatment (indicated as onset-to-needle time >4.5 h) were analyzed with multivariate logistic regression analysis.

RESULTS

A total of 252 patients were included, of which 153 (60.7%) were enrolled before and 99 (39.3%) after the COVID-19 pandemic. ODT increased from 202 min (interquartile range [IQR] 65-492) before to 317 min (IQR 75-790) after the COVID-19 pandemic (p = 0.001). DNT increased from 50 min (IQR 40-75) before to 65 min (IQR 48-84) after the COVID-19 pandemic (p = 0.048). The proportion of patients with intravenous thrombolysis in those with AIS was decreased significantly after the pandemic (15.4% vs. 20.1%; p = 0.030). Multivariate logistic regression analysis indicated that patients after COVID-19 pandemic, lower educational level, rural residency, mild symptoms, small artery occlusion, and transported by other means than ambulance were associated with delayed treatment.

CONCLUSIONS

COVID-19 pandemic has remarkable impacts on the management of AIS. Both pre- and posthospital delays were prolonged significantly, and proportion of patients arrived within the 4.5-h time window for intravenous thrombolysis treatment was decreased. Given that anti-COVID-19 measures are becoming medical routines, efforts are warranted to shorten the delay so that the outcomes of stroke could be improved.

Knowledge, attitude, and practice of stroke and thrombolysis among students preparing for undergraduate medical entrance examination in Kathmandu, Nepal

BACKGROUND

Stroke is a major disabling disease, especially for low and middle-income countries like Nepal. The aim of our study is to assess knowledge, attitude, and practice (KAP) among the students preparing for undergraduate medical entrance examination regarding risk factors, warning signs and symptoms, and management of stroke.

METHODS

A cross-sectional, single staged study using self-structured questionnaire intended to assess KAP about stroke and thrombolysis was conducted.

RESULTS

A total of 378 students participated in our study (53% male; mean age = 18.12 ± 0.97). Majority of the participants (88.4%) had heard about stroke. The more common risk factors identified by them were hypertension (86.2%), oily food (48%), alcohol (37.8%), and smoking (32.8%). Limb weakness, slurring of speech and facial weakness as symptoms and signs of stroke were indicated by 43.4%, 30.2%, and 18.8% of the participants, respectively. Only 23.8% of the participants had heard about thrombolysis and 10% of all could rightly mention the window period of thrombolysis. Male participants had better knowledge about smoking [86 (43.0) vs 38 (21.3); P < .001] and oily food [108 (54.0) vs 73 (41.0); P = .012] being risk factors and facial weakness [50 (37.6) vs 21 (11.8); P = .001] being symptom of stroke compared with females. Similarly, male participants had heard more about thrombolysis than females [68 (34.0) vs 22 (12.4); P < .001].

CONCLUSION

Knowledge regarding risk factors and signs and symptoms of stroke was adequate among the students preparing for undergraduate medical entrance examination. However, knowledge about thrombolysis was poor. Male participants had better knowledge about risk factors, warning signs and symptoms of stroke, and thrombolysis compared with female.

A systematic review of the cost-effectiveness of emergency interventions for stroke in low- and middle-income countries

Background

Stroke is a leading cause of death and disability globally, with an increasing incidence in low- and middle-income countries (LMICs). The successful treatment of acute stroke requires an organized, efficient and well-resourced emergency care system. However, debate exists surrounding the prioritization of stroke treatment programs given the high costs of treatment and the increased incidence of hemorrhagic stroke in LMICs. Economic data is helpful to guide evidence-based priority setting in health systems development, particularly in low-resource settings where scarcity requires careful stewardship of resources. This systematic review surveys the existing evidence surrounding the cost-effectiveness of interventions to address acute stroke in LMIC settings.

Methods

The authors conducted a PRISMA style systematic review of economic evaluations of interventions to address acute stroke in LMICs. Five databases were systematically searched for articles, which were then reviewed for inclusion.

Results

Of the 153 unique articles identified, 11 met the inclusion criteria. Four studies demonstrate the heavy economic burden on patients and households due to stroke. Two studies estimate that preventive measures are more cost-effective than acute treatments. Four studies directly examine the cost-effectiveness of thrombolysis and thrombectomy in three middle-income countries (Iran, China, and Brazil) with results ranging from roughly $2578 to $34,052 (2019 USD) per quality adjusted life-year saved. These results are similar to the cost-effectiveness ratios estimated in high-income settings. Finally, one study examined a care bundle that included acute treatment elements.

Conclusions

The findings reinforce the need for additional research support informed decision-making. The available evidence suggests that preventive measures should be prioritized over emergency treatment for acute stroke, particularly in settings of resource scarcity. Cost-effectiveness ratios do not compare favorably to estimates for other emergency care interventions in LMICs, such as basic emergency care training, implementation of triage systems, and basic trauma care. Cost-effectiveness is also likely to vary depending on local epidemiology. Overall, decision-makers should balance the economic evidence alongside social, political and cultural priorities when making resource allocation choices.

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

OBJECTIVES

This study aimed to identify the influencing factors associated with long onset-to-door time and establish predictive models that could help to assess the probability of prehospital delay in populations with a high risk for stroke.

MATERIALS AND METHODS

Patients who were diagnosed with acute ischemic stroke (AIS) and hospitalized between 1 November 2018 and 31 July 2019 were interviewed, and their medical records were extracted for data analysis. Two machine learning algorithms (support vector machine and Bayesian network) were applied in this study, and their predictive performance was compared with that of the classical logistic regression models after using several variable selection methods. Timely admission (onset-to-door time < 3 hr) and prehospital delay (onset-to-door time ≥ 3 hr) were the outcome variables. We computed the area under curve (AUC) and the difference in the mean AUC values between the models.

RESULTS

A total of 450 patients with AIS were enrolled; 57 (12.7%) with timely admission and 393 (87.3%) patients with prehospital delay. All models, both those constructed by logistic regression and those by machine learning, performed well in predicting prehospital delay (range mean AUC: 0.800-0.846). The difference in the mean AUC values between the best performing machine learning model and the best performing logistic regression model was negligible (0.014; 95% CI: 0.013-0.015).

CONCLUSIONS

Machine learning algorithms were not inferior to logistic regression models for prediction of prehospital delay after stroke. All models provided good discrimination, thereby creating valuable diagnostic programs for prehospital delay prediction.

Knowledge on Signs and Risk Factors in Stroke Patients

Background: There is a pressing need to contribute evidence to the improvement in the early identification of signs and symptoms associated with strokes, and address the treatment-seeking delays. The objective of this study is to describe the knowledge regarding the warning signs and risk factors (RFs) among stroke patients, as well as of their attitudes toward a suspected event, and the analysis of its possible relationship with the socio-demographic and clinical characteristics of these patients. Method: A cross-sectional study was designed, in which all stroke patients admitted consecutively to the Burgos University Hospital (Spain) were included. The principal outcomes were the patient’s ability to identify two RFs and two warning signs and the patient’s hypothetical response to a possible stroke event. The possible factors associated with the knowledge of warning signs, RFs, and the correct response to a new event were studied using univariate and multivariate regression analysis. Results: A total of 529 patients were included. Having a higher education level or a history of prior stroke were associated with a greater degree of knowledge of warning signs (odds ratio (OR) 3.19, 95% confidence interval (CI) 1.70–5.74, p = 0.003; OR 3.54, 95%CI 2.09–5.99, p ≤ 0.001, respectively), RFs (OR 3.15, 95%CI 1.75–5.67, p = 0.008; OR 4.08, 95%CI 2.41–6.91, p = 0.002, respectively), and the correct response to a possible stroke (OR 1.82, 95%CI 1.16–2.86; p = 0.030; OR 2.11, 95%CI 1.29–3.46, p = 0.022, respectively). Conclusion: Knowledge of warning signs or stroke RFs is low in the hospitalized patients. A previous stroke or secondary/higher education levels are the predictor factors that increase the probability of knowledge of warning signs, RFs, or reaction to possible event.

Socioeconomic disparities in prehospital stroke care

Background and purpose

Recent studies have revealed socioeconomic disparities in stroke outcomes. Here, we investigated whether prehospital stroke care differs with respect to socioeconomic status (SES).

Methods

Consecutive stroke and TIA patients (n = 3006) admitted to stroke units at Sahlgrenska University Hospital, Gothenburg, Sweden, from 1 November 2014 to 31 July 2016, were included. Data on prehospital care were obtained from a local stroke register. Socioeconomic status was classified according to the average level of income and education within each patient’s neighbourhood (postcode area).

Results

The median system delay from calling the emergency medical communication centre (EMCC) to start of brain computed tomography on hospital arrival was 3 h 47 min (95% confidence interval (CI) 3 h 30 min to 4 h 05 min) for patients within the lowest SES tertile and 3 h 17 min (95% CI 3 h 00 min to 3 h 37 min) for the highest tertile (p < 0.05). Patients with a lower SES were less likely to receive the highest priority in the ambulance (p < 0.05) and had lower rates of prehospital recognition of stroke/TIA (p < 0.05) than those with a high SES. No inequities were found concerning EMCC prioritisation or the probability of ambulance transport.

Conclusions

We found socioeconomic inequities in prehospital stroke care which could affect the efficacy of acute stroke treatment. The ambulance nurses’ ability to recognise stroke/TIA may partly explain the observed inequities.

Response to Symptoms and Prehospital Delay in Stroke Patients. Is It Time to Reconsider Stroke Awareness Campaigns?

BACKGROUND

Despite recent advances in acute stroke care, reperfusion therapies are given to only 1%-8% of patients. Previous studies have focused on prehospital or decision delay. We aim to give a more comprehensive view by addressing different time delays and decisions.

METHODS

A total of 382 patients with either acute stroke or transient ischemic attack were prospectively included. Sociodemographic and clinical parameters and data on decision delay, prehospital delay, and first medical contact were recorded. Multivariate logistic regression analyses were conducted to identify factors related to decision delay of 15 minutes or shorter, calling the Extrahospital Emergency Services, and prehospital delay of 60 minutes or shorter and 180 minutes or shorter.

RESULTS

Prehospital delay was 60 minutes or shorter in 11.3% of our patients and 180 minutes or shorter in 48.7%. Major vascular risk factors were present in 89.8% of patients. Severity was associated with decision delay of 15 minutes or shorter (odds ratio [OR] 1.08; confidence interval [CI] 1.04-1.13), calling the Extrahospital Emergency Services (OR 1.17; CI 1.12-1.23), and prehospital delay of 180 minutes or shorter (OR 1.08; CI 1.01-1.15). Adult children as witnesses favored a decision delay of 15 minutes or shorter (OR 3.44; CI 95% 1.88-6.27; P < .001) and calling the Extrahospital Emergency Services (OR 2.24; IC 95% 1.20-4.22; P = .012). Calling the Extrahospital Emergency Services favored prehospital delay of 60 minutes or shorter (OR 5.69; CI 95% 2.41-13.45; P < .001) and prehospital delay of 180 minutes or shorter (OR 3.86; CI 95% 1.47-10.11; P = .006).

CONCLUSIONS

Severity and the bystander play a critical role in the response to stroke. Calling the Extrahospital Emergency Services promotes shorter delays. Future interventions should encourage immediately calling the Extrahospital Emergency Services, but the target should be redirected to those with known risk factors and their caregivers.

Access to Thrombolysis for Non-Resident and Resident Stroke Patients-A Registry-Based Comparative Study from Berlin

Objectives

Stroke can happen to people away from home. It is unknown whether non-resident and resident stroke patients have equal access to thrombolysis.

Materials and methods

Consecutive patients cared for by the Stroke Emergency Mobile between 2011 and 2016 after prompting suspicion of acute stroke during the emergency call were included in our registry. Patients were categorized as residents or non-residents based on their main address. Clinical characteristics, thrombolysis rates, and time intervals from symptom onset/last seen well to alarm and to thrombolysis were compared between groups adjusting for age, pre-stroke modified Rankin Scale (mRS) score, and National Institutes of Health Stroke Scale (NIHSS) score.

Results

Of 4,254 patients for whom a stroke dispatch was activated, 2,451 had ischemic or hemorrhagic strokes, including 73 non-residents. Non-resident stroke patients were younger (median 69.4 vs. 76.6 years, p < 0.001), had less pre-stroke disability (mRS ≥ 2:17.8 vs. 47.5%, p < 0.001) and less severe strokes (median NIHSS 4 vs. 5, p = 0.02). Thrombolysis rates were higher in non-residents (30.9 vs. 22.0% of ischemic stroke patients, p = 0.04) and emergency calls were made faster (symptom onset/last-seen-well-to-alarm time 35 vs. 144 min, p = 0.04). A lower proportion of non-residents had unknown time of symptom onset (21.9 vs. 46.4%, p < 0.001). For patients with known time of symptom onset, thrombolysis rates, and prehospital delays were similar among non-residents and residents.

Conclusion

In this study, non-resident stroke patients had higher rates of thrombolysis than residents. This may be explained by a lower proportion of patients with unknown time of symptom onset.