Home Monitoring to Predict Exacerbation in Cystic Fibrosis

Cystic Fibrosis Clinical Trial

— SmartCare  

Official title:

A Standardized Multi-centre Analysis of Remote Monitoring in Cystic Fibrosis Adult Patients to Reduce Pulmonary Exacerbations

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT02416375
• Other study ID #: PO1955
• Status: Completed
• Start date: May 2015
• Est. completion date: January 2019

Study information

• Verified date: March 2021
• Source: Papworth Hospital NHS Foundation Trust
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 147
• Est. completion date: January 2019
• Est. primary completion date: January 2018
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Cystic Fibrosis
• Fibrosis

Locations

Country	Name	City	State
United Kingdom	Papworth Hospital NHS Trust	Cambridge	Cambridgeshire
United Kingdom	Papworth Hospital NHS Trust	Cambridge

Sponsors (9)

Lead Sponsor	Collaborator
Papworth Hospital NHS Foundation Trust	Cystic Fibrosis Trust, Frimley Park Hospital NHS Trust, King's College Hospital NHS Trust, Microsoft Research, Royal Brompton & Harefield, The Leeds Teaching Hospitals NHS Trust, University Hospital Southampton NHS Foundation Trust, University Hospitals Bristol and Weston NHS Foundation Trust

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Secondary	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
Secondary	Development of a web-based machine learning tool	Development of a web-based machine learning associated tool to predict acute pulmonary exacerbation and treatment response in patients with cystic fibrosis.	6 months