Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of hospitalization in the United States. Exacerbations, a worsening or flare up of symptoms cause most COPD hospitalizations. Early detection of lung function deterioration would facilitate early intervention and help prevent hospitalizations, since most exacerbations can be treated with changes of inhalers and/or oral medications. Air trapping, defined as an abnormal increase in the volume of air remaining in the lungs after exhalation, is a common finding in all forms of COPD. Air trapping has been shown to increase during exacerbations and decrease when exacerbations resolve. Moreover, increasing recent evidence indicates that air trapping is an earlier harbinger of deteriorating lung function than spirometric changes. Recent research shows that lung air trapping can be measured by low-frequency ultrasound (1-40 kHz). Thus, acoustic monitoring of air trapping could provide clinicians with a non-invasive tool to when medical intervention is needed to avoid unnecessary ER visits and hospitalizations. The investigators have developed a low-cost, non-invasive, acoustic-based wearable device, Sylvee that is capable of continuous monitoring of lung resonance. The device has machine-learning algorithms that can detect minor changes in lung resonance, which our preliminary results suggest corresponds to changes in air trapping. The overall objective of this pilot project is to validate Sylvee's algorithms in a cohort of 60 patients with COPD and variable degree of air trapping. 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.
This is a pilot observational study during which the investigators will conduct a longitudinal assessment of air trapping (with up to 2 visits) in 40 patients with COPD and variable degrees of air trapping and 20 healthy controls using ARIA. The investigators will characterize the clinical phenotype of the subjects by administering health and symptom-based questionnaires and obtaining lung function testing at rest and during exertion, and will then correlate and validate the ARIA-based indices with those of the more traditional physiologic measures of static and dynamic air trapping.