Clinical Trial Details
— Status: Active, not recruiting
Administrative data
NCT number |
NCT04240353 |
Other study ID # |
INGN18RM173 |
Secondary ID |
|
Status |
Active, not recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
August 1, 2018 |
Est. completion date |
July 31, 2021 |
Study information
Verified date |
February 2021 |
Source |
NHS Greater Glasgow and Clyde |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
Chronic obstructive pulmonary disease (COPD) is a serious but treatable chronic health
condition. Optimised management improves symptoms, complications, quality of life and
survival. Disease exacerbations, which have adverse outcomes and often trigger hospital
admissions, underpin the rising costs of managing COPD (projected increase in the United
Kingdom (UK) to £2.3bn by 2030). The costs and care-quality gap of COPD exacerbations,
coupled with the global rising prevalence present a major healthcare challenge. This study
proposal, which has been developed in partnership with patients, clinicians, enterprise and
government representation is to conduct an implementation and effectiveness observational
cohort study to establish a continuous and preventative digital health service model for
COPD.
The implementation proposals comprise: -
- Establishing a digital resource for high-risk COPD patients which contains symptom
diaries (structured patient reported outcome questionnaires), integrates physiology
monitoring (FitBit and home NIV therapy data), enables asynchronous communication with
clinical team, supports COPD self-management and tracks interaction with the service
(for endpoint analyses).
- Establishing a cloud-based clinical COPD dashboard which will integrate background
electronic health record data, core COPD clinical dataset, patient-reported outcomes,
physiology and therapy data and patient messaging to provide clinical decision support
and practice-efficiencies, enhancing delivery of guideline-based COPD care.
- Use the acquired dataset to explore feasibility and accuracy of machine-learned
predictive modelling risk scores, via cloud-based infrastructure, which will be for
future prospective clinical trial.
Our primary endpoint for the effectiveness evaluation is number of patients screened and
recruited who successfully utilise and engage with this RECEIVER clinical service. The
implementation components of the project will be iterated during the study, based on patient
and clinical user experience and engagement. Secondary endpoints include a number of
specified clinical outcomes, clinical service outcomes, machine-learning supported
exploratory analyses, patient-centred outcomes and healthcare cost analyses.
Description:
Patients will be screened from emergency attendance or admission at South and North Sector
(Queen Elizabeth University Hospital, and Glasgow Royal Infirmary) and from referrals to the
COPD clinical team at these sites.
Patients meeting inclusion criteria will be approached and offered enrolment to the study.
Recruitment and consent timings will be individualised to be most efficient and least
burdensome for patients. For some patients it will be appropriate to do this immediately to
avoid burden of repeated attendances; for some patients, delay and consideration may be
appropriate; for some patients the enrolment and engagement may be a staged process (consent
at time of hospital attendance, study commence at follow up home or clinic visit etc).
Patients recruited will receive support information and assistance with login setups for the
digital service components. Literature with frequently asked questions (FAQs), and team
contacts for service support are available for throughout the study.
Patients enrolled will be asked, and prompted with text notifications, to complete daily
short structured COPD symptom questionnaire. There are a small number of additional questions
on a weekly basis, with quality of life questions completed once every 28 days. Patients
recruited will have a "Fitbit" wristband wearable to monitor physiology.
Patients with hypercapnic respiratory failure will additionally be on home non-invasive
ventilation (NIV) treatment - this is part of their routine clinical care rather than a study
intervention. However, the study patient resource and messaging system will be used to gather
information and support this treatment.
Selected patients, who are recruited during hospital admission or attendance and will be
attending outpatient clinic follow up, will undergo exploratory physiology measurements -
parasternal electromyography (EMG) (similar to electrocardiography (ECG) recording, takes ~20
minutes with breathing manoeuvres), oscillometry (a breathing test involving 10 resting
non-effortful breaths blown into the medical device), home pollution monitoring (a pack which
rests in patients bedroom +/- tube placed outside house) for 7 days - alongside routine
clinical care at baseline and 3 monthly intervals.
Patients will have linked access from the patient resource to curated information about COPD
diagnosis, and all aspects of management. Specific prompts about management - e.g. timing to
make appointment for annual flu vaccination - will be provided through platform-text
notifications. Self-management content of the resource will potentially be further developed
over iterations within the study; any change in content of patient materials would be advised
as a protocol amendment.
Patients will be able to message the clinical team using the patient portal. This supplements
existing availability of answer phone contact details provided as part of routine clinical
care. Automatic messages will notify patients that this is not for emergency contact, and
that replies should be expected within Mon-Fri working hours, by next working day. This
messaging system will be used to support self management, home oxygen and home NIV treatment
initiation and monitoring, and practical aspects such as appointment scheduling and equipment
consumable replenishment.
The clinical team will be able to access the data from the patients symptom diaries, wearable
and NIV physiology directly - asynchronously, rather than delayed acquisition of this data at
a clinical contact. This data visualisation will support routine clinical care, and better
inform unscheduled advice contacts from patients (e.g. help determine significance of
apparent worsening symptoms).
This data will be subject to machine-learning analysis, which will evaluate secondary
endpoints, as per protocol.