Cystic Fibrosis
Conditions
Brief summary
The study aims to establish if it is possible for people with Cystic Fibrosis to monitor a number of parameters on a daily basis at home which might predict respiratory infections before they have symptoms and which might also predict treatment failures before this is obvious with conventional measures.
Detailed description
Participants will collect the following clinical information daily: pulse rate and oxygen saturations, wellness and cough scores, spirometry measurements, physical activity, temperature, weight and sleep quantity and quality. The patients will also collect daily sputum samples. Data will be collected via Bluetooth-enabled devices and transmitted via a Smart-phone to a secure National Health Service approved web-based site to be analyzed. The information obtained will allow the investigators to develop a software program that will identify signals that can predict the onset of a chest infection before symptoms develop. The investigators will also measure specific substances in sputum to identify changes before, during and after chest infections. The investigators hope this additional information will enable them to more accurately predict the onset of chest infections in cystic fibrosis. The results of this study will determine if it is possible to develop a simple sputum test for patients to use at home in combination with other home-based assessments of well-being to provide an early warning system of a chest infection before patients feel unwell.
Interventions
None listed
Sponsors
Cystic Fibrosis Trust
King's College Hospital NHS Trust
Royal Brompton & Harefield
The Leeds Teaching Hospitals NHS Trust
University Hospital Southampton NHS Foundation Trust
Microsoft Research
Frimley Park Hospital NHS Trust
University Hospitals Bristol and Weston NHS Foundation Trust
Papworth Hospital NHS Foundation Trust
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Diagnosis of Cystic Fibrosis based on genetic testing and/or sweat chloride. 2. Age ≥ 18 years of age. 3. A history of at least 1 acute pulmonary exacerbation in the past 12 months. 4. Able to provide written informed consent. 5. Willing and able to produce daily sputum samples. 6. Willing and able to provide daily telemetric measure of several physiological parameters.
Exclusion criteria
1. Patients unable to provide written informed consent 2. Patients unable to produce daily sputum samples 3. Less than 1 infective pulmonary exacerbation in 12 months 4. Lung transplant recipients
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Home monitoring possible in adult Cystic Fibrosis patients | 6 months | This will be measured by the number of patients recruited into the study and the patients compliance / adherence to the study protocol |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Whether daily monitoring can provide early warning of a new chest infection | 6 months | Identification of predictive signals for early detection of an acute pulmonary exacerbations and treatment response in patients with cystic fibrosis |
| Development of a web-based machine learning tool | 6 months | Development of a web-based machine learning associated tool to predict acute pulmonary exacerbation and treatment response in patients with cystic fibrosis. |
Countries
United Kingdom
Outcome results
None listed