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| Name | Class |
|---|---|
| Cystic Fibrosis Trust | OTHER |
| King's College Hospital NHS Trust | OTHER |
| Royal Brompton & Harefield | UNKNOWN |
| The Leeds Teaching Hospitals NHS Trust |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Observation | Adult Cystic Fibrosis patients |
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| Measure | Description | Time Frame |
|---|---|---|
| Home monitoring possible in adult Cystic Fibrosis patients | This will be measured by the number of patients recruited into the study and the patients compliance / adherence to the study protocol | 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Whether daily monitoring can provide early warning of a new chest infection | Identification of predictive signals for early detection of an acute pulmonary exacerbations and treatment response in patients with cystic fibrosis | 6 months |
| Development of a web-based machine learning tool |
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Inclusion Criteria:
Exclusion Criteria:
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Adult Cystic Fibrosis patients who produce sputum daily and have not had a previous organ transplant, with a history of at least one pulmoanry exacerbation within the past 12 months.
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| Name | Affiliation | Role |
|---|---|---|
| Andres Floto, Prof | Papworth Hospital NHS | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Papworth Hospital NHS Trust | Cambridge | Cambridgeshire | CB23 3RE | United Kingdom | ||
| Papworth Hospital NHS Trust |
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| ID | Term |
|---|---|
| D003550 | Cystic Fibrosis |
| ID | Term |
|---|---|
| D010182 | Pancreatic Diseases |
| D004066 | Digestive System Diseases |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
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| OTHER |
| University Hospital Southampton NHS Foundation Trust | OTHER |
| Microsoft Research | INDUSTRY |
| Frimley Park Hospital NHS Trust | OTHER |
| University Hospitals Bristol and Weston NHS Foundation Trust | OTHER |
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Daily sputum samples
Development of a web-based machine learning associated tool to predict acute pulmonary exacerbation and treatment response in patients with cystic fibrosis. |
| 6 months |
| Cambridge |
| CB23 3RE |
| United Kingdom |
| D030342 |
| Genetic Diseases, Inborn |
| D009358 | Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
| D007232 | Infant, Newborn, Diseases |