The goal of this study is to develop an objective tool for the fast and easy assessment of stroke, by developing a novel analytics platform that quickly identifies potential stroke from movement signatures captured with wearable sensors. The tool uses unobtrusive wearable sensors and does not require medical training to operate, and thus will be valuable for families and caregivers of older Americans with high stroke risk who are either living at home or in an assisted living facility. Ischemic stroke affects nearly 700,000 Americans each year, costs approximately $33 billion annually, and is the fifth leading cause of death and a leading cause of disability in the US. IV tissue plasminogen activator (tPA) has been an FDA approved therapy for ischemic stroke since 1995, yet only 5-10% of eligible patients receive this therapy. Arrival time in the emergency room (ER) after initial stroke symptoms is directly associated with better outcomes after tPA and endovascular therapy, specifically 4.5 hours and up to 24 hours in select patients, respectively. It is well established that treatment with tPA decreases the lifetime cost of stroke to third party payers by $25,000 per patient. As a result, health plans will be the ultimate economic buyer for this new technology. Here, we are not developing a new therapy for stroke, where so many others have failed. We seek, rather, to fill a well-accepted and clear gap for stroke patients: the early recognition of stroke symptoms that prevents them from accessing PROVEN but time dependent treatments in stroke, whose impact on outcomes is well established. Early identification of symptoms and timely treatment is the single most important determinant of outcomes in ischemic stroke. However, despite massive public health campaigns, most people do not identify stroke symptoms, resulting in costly delays in calling 9-1-1 and patients arriving in the hospital too late to benefit from ischemic stroke treatments. This is a growing problem, given the aging US population of which a significant portion lives at home alone, the staffing crisis at assisted living and nursing facilities, and the prevalence of stroke which is expected to increase by 20% by 2030. Given these trends, administrators and aging experts are increasingly recognizing the value of remote monitoring and aging in place initiatives. Thus, there a clear and urgent, unmet need for a method to assist untrained stroke victims, their families and caregivers in the early recognition of stroke. Bridging this gap with a fast, easy and objective solution would have significant public health impact, because millions of patients could access therapies in a window that is already known to improve outcomes for brain injury. To address this problem, we have leveraged our team?s unique expertise in stroke neurology and developed a set of activation tasks coupled with a wearable solution to assess and quantify ischemic stroke. Once deployed, our solution will improve stroke emergency response and increase the number of patients receiving IV tPA and other reperfusion therapies in the acute setting.

Public Health Relevance

Ischemic stroke affects nearly 700,000 Americans, costs approximately $33 billion annually, and is the fifth leading cause of death and a leading cause of disability in the US. Though treatment for stroke has been available for over 20 years, only a very small number of eligible patients receive it because individuals who have suffered a stroke do not reach the emergency room in time. We have developed a method for quick, easy, real-time assessment for stroke, which will dramatically improve the number of patients receiving timely therapy after stroke and improve the outcome for stroke.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
1R43NS110338-01A1
Application #
9847511
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Chen, Daofen
Project Start
2019-09-18
Project End
2020-08-31
Budget Start
2019-09-18
Budget End
2020-08-31
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Alva Health, Inc.
Department
Type
DUNS #
080790803
City
New Haven
State
CT
Country
United States
Zip Code
06510