Novel Sensors and Artificial Intelligence for Detection of Acute Pulmonary Exacerbations in Cystic Fibrosis and Non-Cystic Fibrosis Bronchiectasis

What this Trial Is About

This research aims to explore how new sensors and artificial intelligence AI can detect acute pulmonary exacerbations APEs early in adults with cystic fibrosis CF or non-CF bronchiectasis. These conditions cause lung problems that worsen with sudden infections, leading to symptoms like coughing and breathlessness. The study focuses on measuring changes in body signals, such as heart rate, during chest infections treated with intravenous antibiotics, to better understand and detect these flare-ups. Participants will use novel sensors named OMED, ARIA, and ANNE that monitor physiological signs like heart rate, temperature, and cough. They will wear or use these sensors daily throughout their intravenous antibiotic treatment, usually lasting around two weeks. Participants may choose which sensors to try, and if a second infection occurs shortly after, sensor use continues during the new treatment. The study is observational and does not alter usual care. During the study, participants will record daily cough and wellness scores on a simple scale. Researchers will collect clinical data such as lung function tests, blood markers, and treatment details from routine care records. The main measurement is the change in sensor readings from the start to the end of antibiotic treatment. There are no extra follow-up visits, and safety monitoring includes checking for any technical issues with the equipment. Participation lasts up to about five weeks depending on treatment courses.

Novel Sensors and Artificial Intelligence for Detection of Acute Pulmonary Exacerbations in Cystic Fibrosis and Bronchiectasis