Home Monitoring to Predict Exacerbation in Cystic Fibrosis

Overview

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.

Full Title of Study: “A Standardized Multi-centre Analysis of Remote Monitoring in Cystic Fibrosis Adult Patients to Reduce Pulmonary Exacerbations”

Study Type

  • Study Type: Observational
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: January 2018

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.

Arms, Groups and Cohorts

  • Observation
    • Adult Cystic Fibrosis patients

Clinical Trial Outcome Measures

Primary Measures

  • Home monitoring possible in adult Cystic Fibrosis patients
    • Time Frame: 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 Measures

  • Whether daily monitoring can provide early warning of a new chest infection
    • Time Frame: 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
    • Time Frame: 6 months
    • Development of a web-based machine learning associated tool to predict acute pulmonary exacerbation and treatment response in patients with cystic fibrosis.

Participating in This Clinical Trial

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

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • Papworth Hospital NHS Foundation Trust
  • Collaborator
    • Cystic Fibrosis Trust
  • Provider of Information About this Clinical Study
    • Principal Investigator: Judy Ryan, Research & Database Manager – Papworth Hospital NHS Foundation Trust
  • Overall Official(s)
    • Andres Floto, Prof, Principal Investigator, Papworth Hospital NHS

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