Changes in Prehospital Stroke Care and Stroke Mimic Patterns during the COVID-19 Lockdown

The impact of COVID-19 lockdown on prehospital stroke care is largely unknown. We aimed to compare stroke care patterns before and during a state-wide lockdown. Thus, we analysed prospective data of stroke alerts referred to our stroke centre between 1 December 2019 and 16 June 2020, and compared them between two periods—15 weeks before and 13 weeks during the state-wide lockdown declared in Lithuania on 16 March 2020. Among 719 referrals for suspected stroke, there was a decrease in stroke alerts (rate ratio 0.61, 95% CI (0.52–0.71)), stroke admissions (0.63, 95% CI (0.52–0.76)), and decrease in prehospital stroke triage quality (positive predictive value 72.1% vs. 79.9%, p = 0.042) during the lockdown. The onset-to-door time was longer (153.0 vs. 120.5 min, p = 0.049) and seizures and intracranial tumours were more common among stroke mimics (16.9% vs. 6.7%, p = 0.012 and 9.6% vs. 3.0%, p = 0.037, respectively). We conclude that there was a decline in prehospital stroke triage quality during the lockdown despite low COVID-19 incidence in the country. Moreover, we observed an increase in hospital arrival delays and severe conditions presenting as stroke mimics. Our findings suggest that improved strategies are required to maintain optimal neurological care during public health emergencies.

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Abstract P242: Impact of Covid-19 on Acute Ischemic Stroke Care at Henry Ford Hospital’s Detroit and West Bloomfield Campuses

Stroke ◽

10.1161/str.52.suppl_1.p242 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Hassan Aboul Nour ◽

Muhammad Affan ◽

Ali Mohamud ◽

Yazeed W Haddad ◽

Ghada Mohamed ◽

...

Keyword(s):

Statistical Significance ◽

Stroke Care ◽

Equal Proportion ◽

Stroke Mimics ◽

Discharge Outcomes ◽

First Pass ◽

Henry Ford ◽

Stroke Mimic ◽

Ischemic Attack ◽

The Impact

Background: Coronavirus 2019 (COVID19) has impacted acute stroke (AS) care with several reports globally showing drops in AS volumes during the pandemic. We studied the impact of COVID19 on AS and transient ischemic attack (TIA) care in a health system serving Southeast Michigan as we rolled out a policy aimed at limiting admissions and transfers. Methods: In this retrospective study conducted at 2 hospitals, we included consecutive patients presenting to the emergency department (ED) for whom a Stroke Alert (SA) was activated during the period 3/20 to 5/20/20 (COVID) and a similar period in 2019 (pre-COVID). We compared demographics, time metrics, and discharge outcomes. Results: 264 patients were seen pre-COVID compared to 121 during COVID (p<0.001). Patients seen during COVID had an equal proportion of males (55% vs 51%, p=0.444), were majority African American (57 vs 58%, p=0.74), but had a higher presenting NIHSS (median: 5 vs 2, p=0.01) and longer times since last-known-well to ED arrival (median: 9.4 vs 4.8 hours, p=0.03) compared to pre-COVID. Fewer patients were transferred from other centers (42 vs 27% p=0.008). SA activation on arrival (median: 9.6 vs 15 min, p=0.004) and imaging initiation from arrival (median: 26.4 vs 34.8 min, p=0.042) were faster as well as a trend toward statistical significance for time to tPA administration (median: 37.8 vs 51 min, p=0.051) compared to pre-COVID. There were higher rates of AS and TIA (69% vs 55%) and lower rates of stroke mimics (17 vs 37%, p<0.001). Patients discharged from the stroke unit had significantly higher discharge NIHSS (median: 3 vs 2, p=0.002) and were more likely to have an unfavorable discharge mRS (3-6) (56 vs 33%, p=0.004). There were no significant differences in medical, social histories, time to first pass for patient undergoing thrombectomy and stroke etiologies between the groups. In 2020, 9 patients (8%) were COVID19 positive, 2 had unfavorable mRS 3-5 while 3 died. Conclusion: There was greater than 50% reduction in stroke admissions during the COVID19 pandemic which is consistent with other reports. Although patients were managed more quickly, they tended to have more severe strokes, fewer stroke mimic diagnoses, and worse outcomes compared to patients treated in the pre-COVID period.

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Identification of Stroke Mimics in the Emergency Department Setting

Journal of brain disease ◽

10.4137/jcnsd.s2280 ◽

2009 ◽

Vol 1 ◽

pp. JCNSD.S2280 ◽

Cited By ~ 1

Author(s):

W. Oliver Tobin ◽

Joseph G. Hentz ◽

Bentley J. Bobrow ◽

Bart M. Demaerschalk

Keyword(s):

Acute Stroke ◽

Stroke Care ◽

Final Diagnosis ◽

Clinical Predictors ◽

Acute Stroke Care ◽

Stroke Mimics ◽

Emergency Department Setting ◽

Multivariable Regression ◽

Stroke Mimic ◽

Stroke Centre

Background and Purpose Previous studies have shown a stroke mimic rate of 9%–31%. We aimed to establish the proportion of stroke mimics amongst suspected acute strokes, to clarify the aetiology of stroke mimic and to develop a prediction model to identify stroke mimics. Methods This was a retrospective cohort observational study. Consecutive “stroke alert” patients were identified over nine months in a primary stroke centre. 31 variables were collected. Final diagnosis was defined as “stroke” or “stroke mimic”. Multivariable regression analysis was used to define clinical predictors of stroke mimic. Results 206 patients were reviewed. 22% were classified as stroke mimics. Multivariable scoring did not help in identification of stroke mimics. 99.5% of patients had a neurological diagnosis at final diagnosis. Discussion 22% of patients with suspected acute stroke had a stroke mimic. The aetiology of stroke mimics was varied, with seizure, encephalopathy, syncope and migraine being commonest. Multivariable scoring for identification of stroke mimics is not feasible. 99.5% of patients had a neurological diagnosis. This strengthens the case for the involvement of stroke neurologists/stroke physicians in acute stroke care.

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Exploring the Collateral Damage of the COVID-19 Pandemic on Stroke Care

Stroke ◽

10.1161/strokeaha.121.034150 ◽

2021 ◽

Author(s):

Clotilde Balucani ◽

J. Ricardo Carhuapoma ◽

Joseph K. Canner ◽

Roland Faigle ◽

Brenda Johnson ◽

...

Keyword(s):

Transient Ischemic Attack ◽

Intravenous Thrombolysis ◽

Stroke Care ◽

Unintended Consequences ◽

Stroke Subtype ◽

Hospital Arrival ◽

Time Period ◽

Ischemic Attack ◽

The Impact ◽

Emergency Measures

Background and Purpose: During the coronavirus disease 2019 (COVID-19) pandemic, the various emergency measures implemented to contain the spread of the virus and to overcome the volume of affected patients presenting to hospitals may have had unintended consequences. Several studies reported a decrease in the number of stroke admissions. There are no data on the impact of the COVID-19 pandemic on stroke admissions and stroke care in Maryland. Methods: A retrospective analysis of quality improvement data reported by stroke centers in the State of Maryland. The number of admissions for stroke, overall and by stroke subtype, between March 1 and September 30, 2020 (pandemic) were compared with the same time period in 2019 (prepandemic). Median last known well to hospital arrival time, the number of intravenous thrombolysis and thrombectomy were also compared. Results: During the initial 7 months of the pandemic, there were 6529 total admissions for stroke and transient ischemic attack, monthly mean 938 (95% CI, 837.1–1038.9) versus prepandemic 8003, monthly mean 1156.3 (CI, 1121.3–1191.2), P <0.001. A significant decrease was observed in intravenous thrombolysis treatments, pandemic 617, monthly mean 88.1 (80.7–95.6) versus prepandemic 805, monthly mean 115 (CI, 104.3–125.6), P <0.001; there was no significant decrease for thrombectomies. The pandemic decreased the probability of admissions for stroke and transient ischemic attack by 19%, for acute ischemic stroke by 20%, for the number of intravenous thrombolysis performed by 23%. There was no difference in the number of admissions for subarachnoid hemorrhage, pandemic 199, monthly mean 28.4 (CI, 22.5–34.3) versus prepandemic 217, monthly mean 31 (CI, 23.9–38.1), respectively, P =0.507. Conclusions: Our findings suggest that the COVID-19 pandemic adversely affected the acute care of unrelated cerebrovascular emergencies.

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01 Development and validation of a pragmatic prehospital tool to identify stroke MIMIC patients

Emergency Medicine Journal ◽

10.1136/emermed-2019-999.1 ◽

2019 ◽

Vol 36 (1) ◽

pp. e1.1-e1

Author(s):

Graham McClelland ◽

Darren Flynn ◽

Helen Rodgers ◽

Chris Price

Keyword(s):

Clinical Characteristics ◽

List Type ◽

Stroke Patients ◽

Stroke Mimics ◽

Primary Hospital ◽

Stroke Services ◽

Suspected Stroke ◽

Stroke Mimic ◽

High Level ◽

Development And Validation

BackgroundStroke mimics (SM) are non-stroke conditions producing similar symptoms to stroke. Prehospital stroke identification tools prioritise sensitivity over specificity, therefore >25% of prehospital suspected stroke patients are SM. Failure to identify SM Results in inefficient use of ambulances and specialist stroke services. We developed a pragmatic tool for paramedics, using information often available in the prehospital setting, to identify SM amongst suspected stroke patients.MethodsThe initial tool was developed using a systematic literature review to identify SM characteristics, a survey of UK paramedics to explore the acceptability of SM identification and regression analysis of clinical variables documented in ambulance records of suspected stroke patients linked to their primary hospital diagnoses (n=1,650, 40% SM).The initial tool was refined using two focus groups with paramedics (n=3) and hospital clinicians (n=9) and analysis of an expanded prehospital dataset (n=3,797, 41% SM) to produce the final STEAM tool.ResultsSTEAM scores six variables:1 point for Systolic blood pressure <90 mmHg1 point for Temperature >38.5°C with heart rate >90 bpm1 point for seizures or 2 points for seizures with diagnosed Epilepsy1 point for Age <40 years or 2 points for age <30 years1 point for headache with diagnosed Migraine1 point for FAST–veA score of ≥2 on STEAM predicted SM diagnosis in the expanded derivation dataset with 5.5% sensitivity, 99.6% specificity and positive predictive value (PPV) of 91.4%. STEAM was validated using an external dataset (n=1,848, 33% SM) of prehospital suspected stroke patients where STEAM was 5.5% sensitive, 99.4% specific with a PPV of 82.5%.ConclusionsSTEAM uses common clinical characteristics to identify a small number of SM patients with a high level of certainty. The benefits of reducing SM admissions to specialist stroke services should be weighed against delayed admission for the small number of stroke patients identified as a SM.

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Abstract P651: Should I Stay or Go: Seeking Stroke Care During Covid-19

Stroke ◽

10.1161/str.52.suppl_1.p651 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Kathyrn J Libby ◽

Linda Couts ◽

Paige Schoenheit-Scott ◽

Lindsay L Olson-Mack ◽

Amelia Kenner Brininger ◽

...

Keyword(s):

San Diego ◽

Arrival Time ◽

Stroke Care ◽

Time Frame ◽

San Diego County ◽

Stroke Patients ◽

Discharge Data ◽

Hospital Arrival ◽

The Impact ◽

At Home

Introduction: On March 16, 2020 San Diego County implemented a stay at home order in response to COVID-19 pandemic; followed by the state of California instituting a shelter in place order. Locally, San Diego County’s stroke receiving centers (SRC) determined a 30% drop in stroke code activations between March-April 2020 compared to the same time in 2019 indicating a possible delay in seeking care. Utilizing discharge data, we sought to understand the impact of the stay at home order on the timeliness of seeking care. Hypothesis: We hypothesized an increase in last known normal (LKN) to hospital arrival time and a decrease in alteplase (tPA) and endovascular therapy (EVT) treatment rates between March 16-June 30 2020 compared to March 16-June 30 2019. Methods: AIS patients presenting to one of 16 SRC in San Diego County between March 16-June 30 in 2019 and 2020, discharged from the hospital or treated in the ED and transferred to another facility were included. Patients arriving as transfers from another facility were excluded. Results: In 2019, of 1,342 AIS cases LKN time was recorded for 85.6% of cases; of 1,092 cases in 2020 86.4% of cases had a LKN. Average LKN to arrival was 20.5 hours in 2019 and 32.4 hours in 2020 (p = .001, 95% CI [4.79, 18.93]). In 2019, 209 (15.6%) received tPA and 91 (6.8%) had EVT. In 2020, 144 (13.2%) received tPA and 75 (6.9%) had EVT. Odds that a case in 2019 received tPA was 1.21 times that of cases in 2020 (p=.09). Odds that a case in 2019 had EVT was .99 times that of cases in 2020 (p=.93). Conclusion: Ischemic stroke patients arriving between March 16-June 30, 2020 had a longer LKN to arrival time compared to the same time frame in 2019. The longer time to arrival may have been due to patients waiting longer to seek care, as anecdotal information from patients eluded to. The odds of receiving tPA or EVT treatment in 2020 compared to 2019 were not statistically significant. This may be due to patients experiencing acute symptoms accessing healthcare at the same rate in 2020 as 2019. Analysis of percent of patients arriving within 4 hours of LKN and average NIHSS are important next steps to determine this. Regardless, during a time of community crisis, it is important to broadcast community messaging focusing on the importance of seeking emergency care for stroke-like symptoms.

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Abstract WP395: Can Nursing-driven Stroke Alert Activations be Effective?

Stroke ◽

10.1161/str.48.suppl_1.wp395 ◽

2017 ◽

Vol 48 (suppl_1) ◽

Author(s):

Nicole L Anderson ◽

Amy L Castle ◽

Ganesh Asaithambi

Keyword(s):

Emergency Department ◽

Acute Stroke ◽

Treatment Delays ◽

Stroke Mimics ◽

Intravenous Alteplase ◽

Stroke Mimic ◽

High Level ◽

The Impact

Background: Earlier stroke alert activations in the emergency department can assemble needed resources quickly in order to shorten treatment delays among eligible patients. We compared the impact of nursing-driven stroke alert activations to EMS- or physician-directed stroke alert activations. Methods: From January 2015 to June 2016, we reviewed data from all emergency department stroke alert activations at an acute stroke ready hospital. We compared nursing (RN)-driven to paramedic (EMS)- and physician (MD)-driven stroke alert activations to determine rates of stroke mimic diagnoses at discharge and use of intravenous alteplase as well as median door-to-stroke alert, door-to-CT, and door-to-needle times. Results: There were 175 stroke alert activations during the study period (42 RN, 87 pre-hospital, 46 MD). Stroke mimics prompting stroke alert activations were not significantly different between RN- and EMS-activations (26.2% RN vs 34.5% EMS, p=0.42) but was significantly higher for MD activations (50% MD, p=0.04). Compared to RN-activations, EMS-activations had shorter door to stroke alert (-7 [-10, -5] minutes EMS vs 4 [1, 7] minutes RN, p<0.01) and door to CT (0 minutes EMS vs 14 [8, 16] minutes RN, p<0.01) times; MD-activations had longer door to stroke alert (11.5 [6, 22] minutes MD, p<0.01) and door to CT (20.5 [14, 30.75] minutes MD, p<0.01). Door-to-needle times were similar between RN- and MD-activations (51 [38, 54] minutes RN vs 58 [49, 63] MD, p=0.25); there was a trend towards quicker DNTs for EMS-activations (39 [31, 43] minutes EMS, p=0.057). Rates of alteplase usage were similar for RN-activations (19%) compared to EMS- (12.6%, p=0.43) and MD- (23.9%, p>0.99) activations. Conclusion: Because of a high level of accuracy, nursing-driven stroke alert activations should be encouraged if indicated in order to shorten stroke alert time metrics when pre-hospital alerts have not occurred. Further studies are needed to examine the impact of nursing-driven stroke alert activations.

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Abstract TP250: A Substantial Rate of Neurological and Non-Neurological Stroke Mimics Are Transported by Emergency Medical Services for Acute Stroke Evaluation

Stroke ◽

10.1161/str.48.suppl_1.tp250 ◽

2017 ◽

Vol 48 (suppl_1) ◽

Author(s):

Joel N Briard ◽

Rahel T Zewude ◽

Mahesh Kate ◽

Ken Butcher ◽

Laura C Gioia

Keyword(s):

Acute Stroke ◽

Emergency Medical Services ◽

Final Diagnosis ◽

Medical Services ◽

Cross Sectional ◽

Stroke Mimics ◽

Emergency Medical ◽

Number Of Patients ◽

Suspected Stroke ◽

The Impact

Introduction: The impact of prehospital triage of stroke mimics to designated stroke centers may be considerable, yet little information exists regarding stroke mimics in the prehospital setting. We aimed to describe the rate and clinical characteristics of neurological and non-neurological stroke mimics transported by Emergency Medical Services (EMS) to the Emergency Department (ED) for acute stroke evaluation. Methods: A retrospective, cross-sectional, observational analysis of a centralized EMS database of patients transported by EMS to the ED for suspected stroke during an 18-month period. Hospital charts and neuroimaging were utilized to determine the final diagnosis (acute stroke, stroke mimic, as well as specific underlying diagnoses). Results: A total of 960 patients were transported by EMS to the ED with suspected stroke, among whom 405 (42.2%) were stroke mimics (mean age ± SD: 66.9 ± 17.1 years; 54% male). Stroke mimics were neurological in origin in 223 (55.1%) patients and non-neurological in 182 (44.9%). Most common neurological diagnoses were seizures (n=44,19.7%), migraines (n=42,18.8%) and peripheral neuropathies (n=25, 11.2%). Most common non-neurological mimics included cardiovascular (15.9%), psychiatric (11.9%), and infectious (8.9%) diagnoses. Neurological mimics were younger (64.1 ± 17.3 years) than non-neurological mimics (70.5 ± 16.1 years, p<0.001). Median prehospital Glasgow Coma Scale scores were similar between groups (15 vs. 15, p=0.26). Mean prehospital systolic blood pressure was slightly higher in neurological (147.8±24.2 mmHg) than non-neurological mimics (141.2±26.2 mmHg, p=0.01). Conclusions: Stroke mimics represent a substantial number of patients transported by EMS for suspected stroke, with a considerable amount being non-neurological in origin. Prospective prehospital studies are warranted to help refine prehospital identification of acute stroke and thus minimize the number of stroke mimics transported by EMS for acute stroke evaluation.

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MSC 2021 Virtual E-Abstract

Journal Of Cardiovascular, Neurovascular & Stroke ◽

10.32896/cvns.v3n3.1-7 ◽

2021 ◽

Vol 3 (3) ◽

pp. 1-7

Author(s):

Malaysian Stroke Conference

Keyword(s):

Observational Study ◽

Stroke Care ◽

Efficacy And Safety ◽

Stroke Patients ◽

Single Centre ◽

Acute Stroke Care ◽

Young Stroke ◽

Single Centre Experience ◽

The Impact ◽

Stroke Centre

1. A Malaysian Single Centre Experience of NOAC Efficacy And Safety For Stroke Prevention in NVAF.2. An Observational Study On The Overview Of Young Stroke Patients.3. An Overview of Stroke Patterns from A Stroke Ready Hospital.4. Mortality After Stroke: A 9-Month Observational Study.5. The Characteristics of Post-Stroke Patients from Hospital Seberang Jaya.6. The Impact of COVID-19 Pandemic on Acute Stroke Care: An Experience from a Primary Stroke Centre in Malaysia.7. Young Stroke On Prevalence Of Epidemiological Factors, Stroke Subtypes And Stroke Events - An Observational Study.

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Abstract P275: Predicting Stroke Mimics in Telestroke Consultations

Stroke ◽

10.1161/str.52.suppl_1.p275 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Jennifer Siriwardane ◽

Holly Martin ◽

Brandon Edwards ◽

Michelle LaPradd ◽

Joanne Daggy ◽

...

Keyword(s):

Atrial Fibrillation ◽

Alcohol Abuse ◽

Stroke Care ◽

Bivariate Analysis ◽

P Value ◽

Metabolic Encephalopathy ◽

Stroke Mimics ◽

Chi Squared ◽

History Of ◽

Stroke Mimic

Background: Telestroke consultation is increasingly used to provide stroke care. Much like in-person stroke consults, stroke mimics are common. This study sought to identify patient and hospital characteristics more likely to be associated with a Telestroke diagnosis of stroke mimic. Methods: We analyzed 2 years of video consults by the VA National Telestroke Program (NTSP). Stroke mimic was defined as a Telestroke consult coded as a diagnosis of “other.” Text responses for “other” diagnoses were grouped into clinical categories. We used Chi-squared and t-tests analysis to compare characteristics of patients with a stroke mimic diagnosis and those without. Co-variates studied included age, gender, co-morbid conditions (history of dementia, cancer, or alcohol abuse), NIHSS, time of consult (night/weekend vs day), location of consult (emergency department vs inpatient), hospital rurality; hospital consult volume, and duration of institutional participation in NTSP at time of consult. Variables with a p-value < 0.25 in the bivariate analysis were included in the multivariate model. Results: There were 561 stroke mimics. The most common mimics were toxic metabolic encephalopathy (19%) and seizure (12%). Variables significantly associated with stroke mimic in bivariate analyses were age, gender, history of alcohol abuse, history of atrial fibrillation, history of dementia, NIHSS, nights/weekend consults, and hospital rurality. In multivariate analyses, female sex [OR=1.63, p=0.001], inpatient consultations [OR=1.55, p= 0.019], history of dementia [OR=1.85, p=0.0002], and alcohol abuse [OR=1.42, p=0.002] were associated with a stroke mimic. Consults during nights/weekends [OR=0.76, p=0.001], and patients with atrial fibrillation [OR=0.81, p=0.031], increasing age [OR=0.90, p=0.019], and increasing NIHSS [OR=0.97, p=0.0042] were less likely to be a mimic. Conclusion: Patient and consult characteristics influenced the likelihood of a stroke mimic diagnosis. Medical history may reflect conditions likely to cause neurologic symptoms, and patients hospitalized who have new symptoms represent a challenging subset to accurately distinguish stroke from mimics. Awareness of these factors may alert providers to diagnoses other than stroke.

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The impact of tele-neurologists on acute stroke care at an advanced primary stroke centre

Journal of Telemedicine and Telecare ◽

10.1177/1357633x20921039 ◽

2020 ◽

pp. 1357633X2092103

Author(s):

Scott Gutovitz ◽

Jonathan Leggett ◽

Leslie Hart ◽

Samuel M Leaman ◽

Heather James ◽

...

Keyword(s):

Acute Stroke ◽

Mechanical Thrombectomy ◽

Stroke Care ◽

Final Diagnosis ◽

Stroke Patients ◽

Acute Stroke Care ◽

Care Guidelines ◽

Stroke Center ◽

The Impact ◽

Stroke Centre

Introduction We evaluated the impact of tele-neurologists on the time to initiating acute stroke care versus traditional bedside neurologists at an advanced stroke center. Methods This observational study evaluated time to treatment for acute stroke patients at a single hospital, certified as an advanced primary stroke centre, with thrombectomy capabilities. Consecutive stroke alert patients between 1 March, 2016 and 31 March, 2018 were divided into two groups based on their neurology consultation service (bedside neurology: 1 March, 2016–28 February, 2017; tele-neurology: 1 April, 2017–31 March, 2018). Door-to-tPA time and door-to-IR time for mechanical thrombectomy were compared between the two groups. Results Nine hundred and fifty-nine stroke patients met the inclusion criteria (436 bedside neurology, 523 tele-neurology patients). There were no significant differences in sex, age, or stroke final diagnosis between groups ( p > 0.05). 85 bedside neurology patients received tPA and 35 had mechanical thrombectomy, 84 and 44 for the tele-neurology group respectively. Door-to-tPA time (median (IQR)) was significantly higher among tele-neurology (64 min (51.5–83.5)) than bedside neurology patients (45 min (34–69); p < 0.0001). There was no difference in door-to-IR times (mean ± SD) between bedside neurology (87.2 ± 33.3 min) and tele-neurology (90.4 ± 33.4 min; p = 0.67). Discussion At this facility, our tele-neurology services vendor was associated with a statistically significant delay in tPA administration compared with bedside neurologists. There was no difference in door-to-IR times. Delays in tPA administration make it harder to meet acute stroke care guidelines and could worsen patient outcomes.

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