Overview
Chronic obstructive pulmonary disease (COPD) causes about 3 million deaths annually and significantly burdens healthcare systems, costing the EU 38.6 billion euros, largely due to frequent hospitalizations triggered by acute exacerbations (AECOPD). AECOPD worsens patient health, accelerates lung decline, and lowers quality of life, highlighting the need for early detection. Moreover, these AECOPD events happen in an out-hospital setting and are therefore, not preventable. A clear clinical and quality-of-life need arises to reduce AECOPD-related events and consequent hospitalizations.
Mobile health (mHealth) offers a solution by monitoring patients remotely using unobtrusive wearable devices. Parameters like peripheral oxygen saturation (SpO2) and respiratory rate can detect and predict exacerbations. However, no data at home is available of AECOPD events and robust predictive algorithms are lacking. This study aims to monitor vital parameters at home, tracking physical activity, pulse, respiratory rate, SpO2, sleep, and skin temperature from the moment of ER admission until three months post-discharge. Data will be used to gain insight in the COPD progression following an AECOPD event and to construct a predictive model, enabling timely intervention, reducing hospitalizations, and improving outcomes.
Description
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 a wearable mobile device. 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.
Eligibility
Inclusion Criteria:
- Provide signed and dated informed consent
- Adults older than 18 years of age
- Previously diagnosed with COPD
- Speak and understand the Dutch language
- Need for hospitalization
Exclusion Criteria:
- Previous diagnosis of asthma
- Not in the possession of a smartphone