RECEIVER: Digital Service Model for Chronic Obstructive Pulmonary Disease (COPD)
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|ClinicalTrials.gov Identifier: NCT04240353|
Recruitment Status : Active, not recruiting
First Posted : January 27, 2020
Last Update Posted : February 3, 2021
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.
|Condition or disease||Intervention/treatment|
|Chronic Obstructive Pulmonary Disease||Other: COPD digital support service|
|Study Type :||Observational|
|Estimated Enrollment :||400 participants|
|Official Title:||Remote-management of COPD: Evaluating Implementation of Digital Innovations to Enable Routine Care|
|Actual Study Start Date :||August 1, 2018|
|Estimated Primary Completion Date :||July 31, 2021|
|Estimated Study Completion Date :||July 31, 2021|
Patients with high-risk COPD with recent exacerbation requiring hospitalisation (within last 12 months) or hypercapnia respiratory failure and/or sleep disordered breathing meeting criteria for provision of home NIV.
Other: COPD digital support service
Use of COPD digital services to record patient symptoms, integrate physiology monitoring, communicate with the clinical team and track interaction
Other Name: Lenus Health COPD
- Patient utilisation of digital service [ Time Frame: 24 months (12 months recruiting) ]Proportion of enrolled high-risk COPD patients successfully utilising remote-management in a digital service model
- Clinical outcomes [ Time Frame: 24 months (12 months recruiting) ]Impact of digitally-enabled remote management on clinical outcomes including COPD exacerbations, unscheduled care contact, hospitalisation and occupied bed days, compared to historical and contemporary SafeHaven cohort (electronic health care record dataset) cohorts
- Clinical service outcomes [ Time Frame: 24 months (12 months recruiting) ]Impact of digital service model on clinical service outcomes including number, nature and complexity of reviews for remotely managed NIV and supported self management.
- Machine-learning analyses [ Time Frame: 24 months (12 months recruiting) ]Machine-learning supported exploratory analyses of associations and relative predictive importance of electronic health record, patient-reported outcomes, wearables physiology and NIV parameters
- Patient-centred outcomes [ Time Frame: 24 months (12 months recruiting) ]Impact of digital service on patient-centred outcomes including health related quality of life (EQ5-D) at baseline and monthly during study and qualitative user research with semi-structured user experience interviews.
- Healthcare cost analyses of digital service model [ Time Frame: 24 months (12 months recruiting) ]Assessment of healthcare costs associated with the digital service model including development and installation costs, recurring costs, and projected direct/indirect costs savings compared to previous service model
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04240353
|Queen Elizabeth University Hospital|
|Glasgow, Scotland, United Kingdom, G51 4TF|
|Principal Investigator:||Chris Carlin||NHS Greater Glasgow and Clyde|