Sensing intracranial bioimpedance through anatomic windows for classifying stroke type

Dartmouth College, Hanover NH

Investigators

Abstract

ABSTRACT “Time lost is brain lost - every minute counts” – according to the Center for Disease Control (CDC) in reference to strokes. Stroke is the 5th leading cause of death in the United States, 2nd in the world and will cost the U.S. $183 billion annually by 2030. Every year 800,000 people will suffer a stroke in the U.S. alone and this high incidence contributes to stroke being a leading cause of serious, long-term disability in the US. There are two primary types of stroke: ischemic and hemorrhagic. Ischemic stroke involves blood or fatty plaque blocking a vessel of the brain while hemorrhagic is defined by a vessel rupture or brain bleed. Each type requires significantly different treatments, and treatment of the wrong type could have lethal consequences. This makes stroke type identification crucial to receiving treatment. Thus, neurologic monitoring and timely intervention are key for acute stroke recovery, yet currently no bedside monitor capable of detecting a recurrent stroke, hemorrhagic transformation and/or evolving stroke at onset exists. Today’s standard-of-care relies on monitoring general patient vitals and periodic CT/MRI scans to image the intracranial state; unfortunately, the large time periods between scans delays possible detection of a high-consequence change in condition. With every minute of pre-intervention time equating to an increase in lasting disability odds, a real-time monitor could not only save lives, but save the quality of life for this vulnerable population. We propose to develop a small form-factor, on- scene device capable of mapping the intracranial space and differentiating ischemic from hemorrhagic stroke. During this program we will take the significant step of developing this technology with translation in mind and demonstrating proof of feasibility in a pre-clinical human study of high-risk patients undergoing monitoring after being admitted for stroke. We will develop a non-invasive sensing approach to intracranial monitoring (Aim I), a key innovation for stroke use, and validate this sensing ability in a cohort of patients being monitored following stroke (Aim II). By assessing the feasibility of our novel approach to non-invasive intracranial monitoring in a tightly controlled patient cohort (post-stroke monitoring), we can validate our ability to 1) detect the presence of stroke and 2) differentiate stroke type. This technology has the potential to not only aid in the clinic as a monitor for detecting stroke onset within patients at high-risk for recurrent stroke or worsening status, but also in the field for mobile stroke type discrimination. Because this system has a small form-factor, is non-invasive, is relatively inexpensive (<$10k for an intracranial bioimpedance monitoring system), and is potentially able to discriminate stroke type at first contact with the patient, this technology has the potential of being easily translated to and accepted by the clinic for the benefit of diagnosing or tracking patients experiencing a stroke. We expect that by the end of this program we will be in a position to optimize and miniaturize our technology and to conduct a larger human study to demonstrate efficacy of our intracranial impedance monitoring technique.