Research Project
10080272
ABSTRACT Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of hospitalization in the US. Exacerbations - a worsening or ?flare up? of symptoms - cause most COPD hospitalizations. Since most exacerbations can be treated with changes of inhalers and/or oral medications, at-home detection of lung function deterioration may facilitate earlier intervention and help delay or prevent hospitalizations. The Standards of Care for monitoring lung function are spirometry, plethysmography, and CT scan. However, these are expensive methods and unsuited for continuous monitoring or at-home use. Various patient self-monitoring approaches have been tried, for example, pulse oximetry, respiratory rate monitoring, and peak flow metering, but their efficacy in reducing hospitalizations has been limited. A common finding for all forms of COPD is air trapping, defined, as an abnormal increase in the volume of air remaining in the lungs after exhalation is complete. A body of evidence definitively shows that air trapping increases during exacerbations and decreases when exacerbations resolve. Recent reports show that air trapping is an earlier harbinger of deteriorating lung function than spirometric changes, and can be measured by low-frequency ultrasound (1-40 kHz). Acoustic monitoring of air trapping could provide clinicians with a non- invasive tool when medical intervention is needed to avoid unnecessary ER visits and hospitalizations. Respira Labs has developed a low-cost, non-invasive, acoustic-based wearable device that can continually monitor lung resonance: Sylvee. The device uses known acoustic-based technology with machine- learning algorithms to detect minor changes in lung resonance, which our preliminary results suggest correspond to changes in air trapping. The overall objective of this project is to validate Sylvee's air trapping algorithms in a cohort of 20 healthy controls and 40 COPD patients with and without air trapping, respectively. In Aims 1 and 2, we will miniaturize and add sensors to the Sylvee device and develop a user interface (UI) and a mobile application. In Aims 3 and 4, we will create an Air Trapping Index Report and validate it in a cross-sectional study vis-à-vis whole body plethysmography as a control. Results of this project provide a go/no-go development decision based on device function. We can apply these results in STTR Phase II, in a larger clinical study to evaluate Sylvee as an at-home monitoring system, with a goal of reducing hospitalizations by at least 30%. Ultimately, Sylvee will allow physicians to remotely monitor their patients' lung function and adjust their medications to reduce healthcare costs and improve patients' quality of life.
