Social factors influencing hospital arrival time in acute ischemic stroke patients

Iosif, Christina; Papathanasiou, Mathilda; Staboulis, Eleftherios; Γουλιάμος, Αθανάσιος

doi:10.1007/s00234-011-0884-9

Cited by 19 publications

(15 citation statements)

References 17 publications

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“…26 Education level might influence prehospital delay. Prehospital delay can be divided into decision delay (time from noticing stroke symptoms to calling for help), which involves stroke awareness and help-seeking behavior in patients, bystanders, and family members, 12,27,28 and transport delay (time from calling for help to arrival at the hospital), which is mainly dependent on the emergency medical system's organizational qualities and priority settings 29 and distance to the hospital. A review 30 of 182 studies suggested that help-seeking behavior is more dependent on the perceived severity of symptoms than on actual knowledge of symptoms.…”

Section: Strokementioning

confidence: 99%

“…3 Despite many countries' universal healthcare systems and ambitions to reduce health gaps, social stratification still exists in terms of stroke incidence, severity, time from onset to hospital arrival, brain imaging access, and outcome. [4][5][6][7][8][9][10][11][12] Swedish studies have shown social stratification in stroke mortality, access to stroke unit care, and secondary prevention. [13][14][15] Socioeconomic status (SES) aspects of stroke thrombolysis have only been explored in a few studies from the United States, Canada, the United Kingdom, and Austria, with inconsistent results.…”

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confidence: 99%

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Education Level and Inequalities in Stroke Reperfusion Therapy

Stecksén

Glader

Asplund

et al. 2014

Stroke

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22on quality of acute care, rehabilitation, secondary prevention, and community support 23 and allows for a nationwide perspective on the quality of Swedish stroke care. Data registration guidance is available online. The data entry software accepts valid alternatives only.Background and Purpose-Previous studies have revealed inequalities in stroke treatment based on demographics, hospital type, and region. We used the Swedish Stroke Register (Riksstroke) to test whether patient education level is associated with reperfusion (either or both of thrombolysis and thrombectomy) treatment. Methods-We included 85 885 patients with ischemic stroke aged 18 to 80 years registered in Riksstroke between 2003 and 2009. Education level was retrieved from Statistics Sweden, and thrombolysis, thrombectomy, patient, and hospital data were obtained from Riksstroke. We used multivariable logistic regression to analyze the association between reperfusion therapy and patient education. Results-A total of 3649 (4.2%) of the patients received reperfusion therapy. University-educated patients were more likely to be treated (5.5%) than patients with secondary (4.6%) or primary education (3.6%; P<0.001). The inequality associated with education was still present after adjustment for patient characteristics; university education odds ratio, 1.14; 95% confidence interval, 1.03 to 1.26 and secondary education odds ratio, 1.08; 95% confidence interval, 1.00 to 1.17 compared with primary education. Higher hospital specialization level was also associated with higher reperfusion levels (P<0.001). In stratified multivariable analyses by hospital type, significant treatment differences by education level existed only among large nonuniversity hospitals (university education odds ratio, 1.20; 95% confidence interval, 1.04-1.40; secondary education odds ratio, 1.14; 95% confidence interval, 1.01-1.29). Conclusions-We demonstrated a social stratification in reperfusion, partly explained by patient characteristics and the local hospital specialization level. Further studies should address treatment delays, stroke knowledge, and means to improve reperfusion implementation in less specialized hospitals. (Stroke. 2014;45:2762-2768.)

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Section: Strokementioning

confidence: 99%

mentioning

confidence: 99%

Education Level and Inequalities in Stroke Reperfusion Therapy

Stecksén

Glader

Asplund

et al. 2014

Stroke

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“…The latter is recommended by guidelines in the US, 8 but receives little attention in the UK. 7 9 Such recording is known to vary widely, 3 12 and is important because patients with an unknown time of onset are not eligible for prealert or subsequent thrombolysis due to uncertainty about whether they fall within the treatment time window. 13 …”

Section: Introductionmentioning

confidence: 99%

The association between prehospital care and in-hospital treatment decisions in acute stroke: a cohort study

et al. 2013

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BackgroundHospital prealerting in acute stroke improves the timeliness of subsequent treatment, but little is known about the impact of prehospital assessments on in-hospital care.ObjectiveExamine the association between prehospital assessments and notification by emergency medical service staff on the subsequent acute stroke care pathway.MethodsThis was a cohort study of linked patient medical records. Consenting patients with a diagnosis of stroke were recruited from two urban hospitals. Data from patient medical records were extracted and entered into a Cox regression analysis to investigate the association between time to CT request and recording of onset time, stroke recognition (using the Face Arm Speech Test (FAST)) and sending of a prealert message.Results151 patients (aged 71±15 years) travelled to hospital via ambulance and were eligible for this analysis. Time of symptom onset was recorded in 61 (40%) cases, the FAST test was positive in 114 (75%) and a prealert message was sent in 65 (44%). Following adjustment for confounding, patients who had time of onset recorded (HR 0.73, 95% CI 0.52 to 1.03), were FAST-positive (HR 0.54, 95% CI 0.37 to 0.80) or were prealerted (HR 0.26, 95% CI 0.18 to 0.38), were more likely to receive a timely CT request in hospital.ConclusionsThis study highlights the importance of hospital prealerting, accurate stroke recognition, and recording of onset time. Those not recognised with stroke in a prehospital setting appear to be excluded from the possibility of rapid treatment in hospital, even before they have been seen by a specialist.

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“…For example, Kothari et al (10) found transport of patients by ambulance (OR 4.0, 95% CI 1.3-12.1) and those of Caucasian race (OR 3.5 95% CI 1.3-10) were independently associated with ED arrival with 3 hours of symptom onset.. Transport by ambulance was also important in the Iosif et al study (5) that reported 41% of 907 patients were transported by ambulance and 12% of those arrived within 3 hours at the hospital from the time of symptom onset. Other factors significantly associated with early arrival at the hospital were age, living with a friend and educational level (5). In Australia in 2013, 80% of patients with suspected stroke were transported to hospital by ambulance: 49% arriving within 3 hours and 58% arriving within 4.5 hours of stroke onset (12).…”

Section: Introductionmentioning

confidence: 91%

“…In addition, pre-hospital notification enables rapid referrals from emergency department (ED) staff to stroke teams and rapid access to imaging (2-4). The time from onset of symptoms to definitive diagnosis and treatment is important: less than 3 hours has been shown to improve patient outcomes (4)(5)(6)(7)(8)(9). Patients transported by ambulance are more likely to arrive at an ED within 3 hours compared to those brought to the ED by other modes of transportation (5,10,11).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Stroke Identification by Paramedics in a Metropolitan Prehospital Setting: A Cohort Study

Williams

Blacker

Arendts

et al. 2017

Australasian Journal of Paramedicine

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ObjectivesAcute stroke is a medical emergency. Identifying patients suffering a stroke is crucial if paramedics are to maximize delivery of appropriate management. One suggested stroke recognition tool is ‘FAST’ (Face, Arms, Speech, Time) but the accuracy of identifying stroke is unknown. We aimed to (1) examine how paramedics identify patients with stroke; (2) compare paramedic identification of stroke with the Emergency Department (ED) discharge diagnosis of stroke.Methods A retrospective cohort study was conducted in the Perth metropolitan area in Western Australia between July 2012 and June 2014 using linked data from ambulance and ED databases. Patients aged 45+ years, transported to ED by road ambulance and assigned the ambulance problem code or ED discharge diagnosis of stroke were selected. Positive predictive value (PPV), negative predictive value (NPV), specificity and sensitivity were calculated. Text fields were examined for documentation that patients met FAST criteria.Results There were 2,217 patients were identified as stroke by paramedics. Of 1834 patients diagnosed as stroke in ED, 876 patients were not identified as stroke by paramedics. Sensitivity for identification of stroke was 958/1834 (52.2%). Of 2,096 patients who were identified as stroke by paramedics and had an ED record, 958 patients were identified as stroke by paramedics and in ED. PPV was 958/2096 (45.7%), NPV 99.5% and specificity 99.4%. Paramedics recorded 2 or 3 stroke signs and symptoms in 1,137 (51%) patients.Conclusion A systematic approach is needed to better identify patients with stroke in the prehospital setting.

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Social factors influencing hospital arrival time in acute ischemic stroke patients

Cited by 19 publications

References 17 publications

Education Level and Inequalities in Stroke Reperfusion Therapy

Education Level and Inequalities in Stroke Reperfusion Therapy

The association between prehospital care and in-hospital treatment decisions in acute stroke: a cohort study

Accuracy of Stroke Identification by Paramedics in a Metropolitan Prehospital Setting: A Cohort Study

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