Wearable sensors and AI to detect chest infection flare-ups in adults with cystic fibrosis or bronchiectasis
Novel Sensors and Artificial Intelligence for Detection of Acute Pulmonary Exacerbations in Cystic Fibrosis and Non-Cystic Fibrosis Bronchiectasis
This study will try wearable sensors and AI to see if changes in breathing or heart rate can spot chest infection flare-ups early in adults with cystic fibrosis or bronchiectasis who are starting IV antibiotics.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 50 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | Papworth Hospital NHS Foundation Trust Government |
| Locations | 1 site (Cambridge, Cambridgeshire) |
| Trial ID | NCT07289100 on ClinicalTrials.gov |
What this trial studies
This observational study enrolls adults with cystic fibrosis or CT-confirmed non-CF bronchiectasis who are starting intravenous antibiotics for an acute pulmonary exacerbation. Participants will use novel sensors that record physiologic signals such as breathing rate and heart rate while clinical data are collected at the start and end of treatment. Artificial intelligence methods will be applied to the sensor data to look for measurable changes that correlate with the exacerbation and its resolution. There are no scheduled follow-up visits beyond the treatment episode and home monitoring data will be linked anonymised for research.
Who should consider this trial
Good fit: Ideal candidates are adults (18+) under the care of Royal Papworth Hospital with a confirmed diagnosis of cystic fibrosis or CT-proven non-CF bronchiectasis who are starting IV antibiotics for an acute pulmonary exacerbation.
Not a fit: People who have had a lung transplant, are on the transplant waiting list, use long-term oxygen or non-invasive ventilation, are unwilling/unable to consent or to allow anonymised home monitoring data use, or who are admitted for elective (non-acute) antibiotic treatment are unlikely to benefit or be eligible.
Why it matters
Potential benefit: If successful, the approach could enable earlier detection of pulmonary exacerbations so treatment can begin sooner and potentially reduce lung damage.
How similar studies have performed: Early research using wearables and AI in other respiratory conditions has shown promise, but applying these sensors to predict exacerbations in CF and bronchiectasis is still largely novel and unproven.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * ≥ 18 years old * Diagnosis of CF based on genetic testing and /or sweat chloride levels or CT confirmed diagnosis of non-CF bronchiectasis * Under the care of Royal Papworth Hospital * Starting IV antibiotics for treatment of APE as defined by the Hill Criteria ² Exclusion Criteria: * Patients unable to provide written informed consent * Lung transplant recipients or on lung transplant waiting list * Use of long-term oxygen therapy (LTOT) or non-invasive ventilation to manage respiratory failure * Patients unwilling to consent to their link anonymised data from home monitoring being used for research * Considered unsuitable for home monitoring in the opinion of the investigator * Patients being admitted for elective antibiotic treatment (i.e. not being admitted for acute APE treatment)
Where this trial is running
Cambridge, Cambridgeshire
- Royal Papworth Hospital NHS Foundation Trust — Cambridge, Cambridgeshire, United Kingdom (Recruiting)
Study contacts
- Principal investigator: Andres Floto — University of Cambridge
- Study coordinator: Lucy E Gale
- Email: lucy.gale1@nhs.net
- Phone: +44 1223 638000
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.