Derivation and Validation of the Emergency Medical Stroke Assessment and Comparison of Large Vessel Occlusion Scales.

BACKGROUND: This study aims to develop a simple scale to identify patients with prehospital stroke with large vessel occlusion (LVO), without losing sensitivity for other stroke types. METHODS: The Emergency Medical Stroke Assessment (EMSA) was derived from the National Institutes of Health Stroke Scale (NIHSS) items and validated for prediction of LVO in a separate cohort. We compared the EMSA with the 3-item stroke scale (3I-SS), Cincinnati Prehospital Stroke Severity Scale (C-STAT), Rapid Arterial oCclusion Evaluation (RACE) scale, and Field Assessment Stroke Triage for Emergency Destination (FAST-ED) for prediction of LVO and stroke. We surveyed paramedics to assess ease of use and interpretation of scales. RESULTS: The combination of gaze preference, facial asymmetry, asymmetrical arm and leg drift, and abnormal speech or language yielded the EMSA. An EMSA less than 3, 75% sensitivity, and 50% specificity significantly reduced the likelihood of LVO (LR- = .489, 95% confidence interval .366-0.637) versus 3I-SS less than 4 (.866, .798-0.926). A normal EMSA, 93% sensitivity, and 47% specificity significantly reduced the likelihood of stroke (LR- = .142, .068-0.299) versus 3I-SS (.476, .330-0.688) and C-STAT (.858, .717-1.028). EMSA was rated easy to perform by 72% (13 of 18) of paramedics versus 67% (12 of 18) for FAST-ED and 6% (1 of 18) for RACE (χ2 = 27.25, P < .0001), and easy to interpret by 94% (17 of 18) versus 56% (10 of 18) for FAST-ED and 11% (2 of 18) for RACE (χ2 = 21.13, P < .0001). CONCLUSIONS: The EMSA has superior abilities to identify LVO versus 3I-SS and stroke versus 3I-SS and C-STAT. The EMSA has similar ability to triage patients with stroke compared with the FAST-ED and RACE, but is simpler to perform and interpret.

Characterization of Hemorrhagic and Ischemic Stroke in a Diverse Cohort of COVID-19 Patients.

BACKGROUND AND PURPOSE: COVID-19 is a known risk factor for stroke. There is limited data on the influence of demographics, risk factors, and hematologic function on outcomes in COVID-19 stroke patients. METHODS: All patients with acute ischemic or hemorrhagic stroke tested for COVID-19 and treated from March 13 through May 19, 2020 were retrospectively analyzed. COVID+ patients were compared to COVID- patients and a historical cohort from 2019. RESULTS: 84 patients with radiographic acute stroke from the 2020 study period and 152 patients in the historical cohort were included. Stroke incidence in COVID+ patients was 1.5%, with a significant decline in total stroke presentations during this period compared to 2019. 37 patients were COVID+ and 47 patients were COVID-. 32% of COVID+ stroke patients were Hispanic compared to 15% and 18% in the COVID- and 2019 cohorts respectively (p = 0.069 and 0.07). COVID+ stroke patients were younger, had higher rates of hemorrhagic conversion (p = 0.034), higher initial NIHSS (p < 0.001), increased cryptogenic stroke mechanism (p = 0.02), and higher mortality independent of COVID-19 severity. COVID+ patients had higher rates of thrombocytopenia (p = 0.02), and were less likely to be on antiplatelet therapy (p = 0.025). In multivariable analysis, only COVID-19 status independently predicted mortality. CONCLUSIONS: COVID status, independent of severity, was significantly associated with higher mortality in stroke patients. COVID+ stroke patients were younger and less likely to be on antiplatelets, with higher rates of thrombocytopenia, suggesting a possible role for antiplatelet use in this population.

Geographic Analysis of Mobile Stroke Unit Treatment in a Dense Urban Area: The New York City METRONOME Registry.

Background Mobile stroke units (MSUs) reduce time to intravenous thrombolysis in acute ischemic stroke. Whether this advantage exists in densely populated urban areas with many proximate hospitals is unclear. Methods and Results We evaluated patients from the METRONOME (Metropolitan New York Mobile Stroke) registry with suspected acute ischemic stroke who were transported by a bi-institutional MSU operating in Manhattan, New York, from October 2016 to September 2017. The comparison group included patients transported to our hospitals via conventional ambulance for acute ischemic stroke during the same hours of MSU operation (Monday to Friday, 9 am to 5 pm). Our exposure was MSU care, and our primary outcome was dispatch-to-thrombolysis time. We estimated mean differences in the primary outcome between both groups, adjusting for clinical, demographic, and geographic factors, including numbers of nearby designated stroke centers and population density. We identified 66 patients treated or transported by MSU and 19 patients transported by conventional ambulance. Patients receiving MSU care had significantly shorter dispatch-to-thrombolysis time than patients receiving conventional care (mean: 61.2 versus 91.6 minutes; P=0.001). Compared with patients receiving conventional care, patients receiving MSU care were significantly more likely to be picked up closer to a higher mean number of designated stroke centers in a 2.0-mile radius (4.8 versus 2.7, P=0.002). In multivariable analysis, MSU care was associated with a mean decrease in dispatch-to-thrombolysis time of 29.7 minutes (95% CI, 6.9-52.5) compared with conventional care. Conclusions In a densely populated urban area with a high number of intermediary stroke centers, MSU care was associated with substantially quicker time to thrombolysis compared with conventional ambulance care.

Clinical Information Systems Integration in New York City's First Mobile Stroke Unit.

BACKGROUND: Mobile stroke units (MSUs) reduce time to thrombolytic therapy in acute ischemic stroke. These units are widely used, but the clinical information systems underlying MSU operations are understudied. OBJECTIVE: The first MSU on the East Coast of the United States was established at New York Presbyterian Hospital (NYP) in October 2016. We describe our program's 7-month pilot, focusing on the integration of our hospital's clinical information systems into our MSU to support patient care and research efforts. METHODS: NYP's MSU was staffed by two paramedics, one radiology technologist, and a vascular neurologist. The unit was equipped with four laptop computers and networking infrastructure enabling all staff to access the hospital intranet and clinical applications during operating hours. A telephone-based registration procedure registered patients from the field into our admit/discharge/transfer system, which interfaced with the institutional electronic health record (EHR). We developed and implemented a computerized physician order entry set in our EHR with prefilled values to permit quick ordering of medications, imaging, and laboratory testing. We also developed and implemented a structured clinician note to facilitate care documentation and clinical data extraction. RESULTS: Our MSU began operating on October 3, 2016. As of April 27, 2017, the MSU transported 49 patients, of whom 16 received tissue plasminogen activator (t-PA). Zero technical problems impacting patient care were reported around registration, order entry, or intranet access. Two onboard network failures occurred, resulting in computed tomography scanner malfunctions, although no patients became ineligible for time-sensitive treatment as a result. Thirteen (26.5%) clinical notes contained at least one incomplete time field. CONCLUSION: The main technical challenges encountered during the integration of our hospital's clinical information systems into our MSU were onboard network failures and incomplete clinical documentation. Future studies are necessary to determine whether such integrative efforts improve MSU care quality, and which enhancements to information systems will optimize clinical care and research efforts.