Primary Faculty Mentor Name

Guillermo Linares, M.D.

Project Collaborators

Paul Jarvis M.D., Nathan Dreyfus MS-III, Sharon Kenney, Roz Bidad, Lindsey Simpson

Secondary Mentor NetID

dwolfson

Secondary Mentor Name

Daniel Wolfson, M.D.

Status

Medical Students

Student College

Larner College of Medicine

Program/Major

Neuroscience

Primary Research Category

Health Sciences

Presentation Title

Novel approach to prehospital stroke screening using a smartphone application

Time

2:10 PM

Location

Mildred Livak Ballroom

Abstract

Cerebrovascular disease continues to be one of the leading causes of morbidity and mortality in the United States and worldwide. Large vessel occlusion strokes (LVOS) – defined as an occlusion of at least one of the major arteries in the brain – are associated with a worse outcome and prognosis. Patients with LVOS may have better outcomes with direct transport by emergency medical services (EMS) to comprehensive stroke centers (CSCs) capable of performing endovascular thrombectomy (EVT), rather than initial transport to a local Emergency Department (ED) that cannot perform this procedure. A need exists for a methodology for EMS to accurately identify patients with a high probability of LVOS and triage to the most appropriate destination hospital in order to shorten times to definitive endovascular treatments, improve patient outcomes and save costs in a rural EMS system. FAST-ED is a smartphone application that was designed specifically to stratify patients based on their likelihood of LVOS, and has been shown to be as accurate in predicting LVOS as NIHSS – the existing standard of practice – while being easier to use. Our multidisciplinary team at UVMMC has created and delivered a comprehensive training program to EMS personnel on the background of stroke care and the use of FAST-ED as a stroke severity scale. Since December 1st, EMS providers have been performing a FAST-ED score for each stroke alert they encounter, which is then repeated by the ED physician, and we will be comparing these scores. Our hypothesis is that EMS providers, in a rural EMS system, can utilize the FAST-ED scale via a smartphone application to accurately identify patients with a high probability of large vessel occlusion strokes, when compared to the scores found by physicians upon arrival to the Emergency Department.

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Novel approach to prehospital stroke screening using a smartphone application

Cerebrovascular disease continues to be one of the leading causes of morbidity and mortality in the United States and worldwide. Large vessel occlusion strokes (LVOS) – defined as an occlusion of at least one of the major arteries in the brain – are associated with a worse outcome and prognosis. Patients with LVOS may have better outcomes with direct transport by emergency medical services (EMS) to comprehensive stroke centers (CSCs) capable of performing endovascular thrombectomy (EVT), rather than initial transport to a local Emergency Department (ED) that cannot perform this procedure. A need exists for a methodology for EMS to accurately identify patients with a high probability of LVOS and triage to the most appropriate destination hospital in order to shorten times to definitive endovascular treatments, improve patient outcomes and save costs in a rural EMS system. FAST-ED is a smartphone application that was designed specifically to stratify patients based on their likelihood of LVOS, and has been shown to be as accurate in predicting LVOS as NIHSS – the existing standard of practice – while being easier to use. Our multidisciplinary team at UVMMC has created and delivered a comprehensive training program to EMS personnel on the background of stroke care and the use of FAST-ED as a stroke severity scale. Since December 1st, EMS providers have been performing a FAST-ED score for each stroke alert they encounter, which is then repeated by the ED physician, and we will be comparing these scores. Our hypothesis is that EMS providers, in a rural EMS system, can utilize the FAST-ED scale via a smartphone application to accurately identify patients with a high probability of large vessel occlusion strokes, when compared to the scores found by physicians upon arrival to the Emergency Department.