Chronic obstructive pulmonary disease (COPD) is a common and life-threatening lung
condition responsible for approximately three million deaths worldwide each year. The
disease poses a substantial burden not only on individuals but also on healthcare
systems. In the European Union, COPD accounts for 56% of annual healthcare costs related
to respiratory diseases, equating to 38.6 billion euros.
A significant portion of these costs arises from the worsening of disease symptoms urging
frequent (re)hospitalizations. These hospitalizations are typically triggered by
flare-ups, also known as acute exacerbations of COPD (AECOPD). Such flare-ups often have
a multifactorial origin e.g. bacterial or viral airway infection) and demand timely
medical intervention to mitigate their impact.
AECOPD adversely affects the patient's health status, accelerates the decline in lung
function, worsens prognosis, and significantly diminishes quality of life. Therefore,
early detection of exacerbations is essential to prevent further disease progression and
reduce hospital admissions.
Mobile health (mHealth) presents a promising solution for monitoring COPD patients at
home remotely. Currently, the health of COPD patients outside of the hospital remains
largely unmonitored-a "black box." By using wearable mobile technology to measure
multiple parameters (e.g. oxygen saturation, respiratory rate, etc), it may become
possible to predict disease worsening early and enable timely intervention. Previous
studies have highlighted that monitoring peripheral oxygen saturation (SpO2) and
respiratory rate can be useful in predicting AECOPD, but predicting algorithms are still
lacking.
In this clinical study, following parameters will be monitored: physical activity,
continuous heart rate, respiratory rate & breaths per minute, SpO2, sleep patterns, and
core body temperature using the Corsano 287-2 smartwatch (class IIa meddev MDR). These
parameters will be tracked from when patients are admitted to the emergency room (ER)
until three months after hospital discharge or until rehospitalization due to AECOPD. The
data collected will be used to gain insight in the COPD progression following an AECOPD
event and construct a prediction model capable of forecasting disease deterioration. This
model could enable timely medical intervention in the future, potentially preventing
hospitalizations and improving patient outcomes.