Predict&Prevent AECOPD
Research type
Research Study
Full title
The use of a personalised early warning decision support system with novel saliva bio-profiling to predict and prevent acute exacerbations of Chronic Obstructive Pulmonary Disease - 'Predict & Prevent AECOPD'
IRAS ID
261576
Contact name
Alice Turner
Contact email
Clinicaltrials.gov Identifier
Duration of Study in the UK
2 years, 5 months, 30 days
Research summary
COPD is a common complex disease with debilitating breathlessness; mortality and reduced quality of life, accelerated by frequent lung attacks (exacerbations). Changes in breathlessness, cough and/or sputum production often change before exacerbations but patients cannot judge the importance of such changes so they remain unreported and untreated. Remote monitoring systems have been developed but none have yet convincingly shown the ability to identify these early changes of an exacerbation and how severe they can be.
This study asks if a smart digital health intervention (COPDPredict™) can be used by both COPD patients and clinicians to improve self-management, predict lung attacks early, intervene promptly, and avoid hospitalisation.
COPDPredict™ consists of a patient-facing App and clinician-facing smart early warning decision support system. It collects and processes information to determine a patient’s health through a combination of wellbeing scores, lung function and biomarker measurements. This information is combined to generate personalised lung health profiles. As each patient is monitored over time, the system detects changes from an individual’s ‘usual health’ and indicates the likelihood of imminent exacerbation of COPD. When this happens, alerts are sent to both the individual and the clinician, with instructions to the patient on what actions to take. Any advice from clinicians can be exchanged via the App’s secure messaging facility. If patients have followed the action plan but fail to improve or if an episode triggers an ‘at high risk alert’, clinicians are further prompted to case manage and intervene with escalated treatment, including home visits, if necessary.
The COPDPredict™ intervention aims to assist patients and clinicians in preventing clinical deterioration from COPD exacerbations with prompt appropriate intervention.
This study will randomise 384 patients who have frequent exacerbations, from hospitals in the West Midlands, to either (1) standard self-management plan (SSMP) with rescue medication (RM), or (2) COPDPredict™ and RM.Lay Summary of Results
Chronic Obstructive Pulmonary Disease (COPD) is a common complex disease with debilitating breathlessness; mortality and reduced quality of life, accelerated by frequent lung attacks (exacerbations). Changes in breathlessness, cough and/or sputum production often change before exacerbations but patients cannot judge the importance of such changes so they remain unreported and untreated. Remote monitoring systems have been developed but none have yet convincingly shown the ability to identify these early changes of an exacerbation and how severe they can be.
Predict and Prevent trial used a smart digital health intervention called COPDPredict™ which was used by both COPD patients and clinicians to improve self-management, predict lung attacks early, intervene promptly, and avoid hospitalisation.
COPDPredict™ consisted of a patient-facing App and a clinician-facing smart early warning decision support system. It collected and processed information to determine a patient’s health through a combination of wellbeing scores, lung function and biomarker measurements.
As each patient is monitored over time, the system detects changes from an individual’s ‘usual health’ and indicates the likelihood of imminent exacerbation of COPD. When this happens, alerts are sent to both the individual and the clinician, with instructions to the patient on what actions to take. Any advice from clinicians can be exchanged via the App’s secure messaging facility.
The COPDPredict™ intervention aimed to assist patients and clinicians in preventing clinical deterioration from COPD exacerbations with prompt appropriate intervention. Prompt exacerbation management via a digital tool, COPDPredictTM may support COPD patients to identify exacerbations earlier to reduce hospital admissions.
Predict and Prevent trial aimed to recruit 384 participants who had frequent exacerbations. However, Covid 19 pandemic significantly affected the recruitment into the trial and only 90 patients (46 males and 44 females) were recruited from 6 hospitals across England. The mean age of the participants was 64.7 years. Patients were randomised to either (1) standard self-management plan (SSMP) with rescue medication (RM), or (2) COPDPredict™ and RM. Patients were followed up for 12 months.
Although the trial under-recruited, thus was underpowered, still it was possible to complete our objectives pertaining to economic and acceptability analyses. Short reports for the trial, economic and qualitative research packages are shown below:
Results from the trial showed that patients’ ability to self-manage (whether they treated themselves when their symptoms indicated exacerbation) was similar between the two arms. Unreported exacerbation events where symptoms indicated acute exacerbation of COPD but no action was taken occurred in 24.4% of patients in the standard care arm, but only 12.8% with COPDPredictTM at 3 months. By 12 months both groups had improved, with unreported untreated events occurring in 7.2% of patients in the standard care arm and 4.7% with COPDPredictTM. The rate of hospital admissions due to acute exacerbation at 12 months was lower in the COPDPredictTM group compared to the standard care group.
Cost-effectiveness analyses from the UK National Health Service perspective compared the cost-effectiveness of COPDPredictTM with standard care for a COPD GOLD stage B & D cohort. We found that COPDPredictTM is likely cost-effective for COPD B and D patients. However, the small samples sizes upon which results are obtained warrant further investigation.Trial researchers conducted interviews with individuals who had no prior experience of the COPDPredictTM App (stakeholders) and those who used the App. We found that stakeholders were strongly supportive of self-management strategies that could help patients manage their symptoms of COPD. This included the need for better diagnostics that could help patients better detect and manage their exacerbations. Exacerbations were seen to be quite difficult to detect both by patients and health care professionals. There was agreement on the need for objective measures to help detect exacerbations accurately. Some participants had already had previous experiences of using health technologies. Nevertheless, some health care professionals felt quite “sceptical” about using technology to help patients self-manage due to past negative experiences. They often spoke about a lack of supportive evidence base which has led to the doubts of adopting new technologies.
Within participants who were diagnosed with COPD and living with the condition, attitudes towards the use of new technology was mainly positive if it was simple and perceived to be beneficial in terms of their experience self-managing their chronic condition.
Overall, health care professionals were willing to try new technologies if there is an evidence base to the beneficial outcomes the App would provide. Patients diagnosed with COPD seemed to be less resistant to engaging with Apps specifically if they are familiar and confident in using technology. There was an overall acceptance towards the instant communication link with health care professionals for further advice/guidance during instances of uncertainties for instance: when to start rescue packs. They also indicated benefits around aspects of not having to visit their GP surgeries and sitting for long periods in waiting rooms for fear of getting worse. All participants expressed that the uptake of technology has certainly moved forward due to the COVID-19 pandemic. People are more willing and flexible to learn and engage with new ways of working and managing health conditions remotely.
The COPDPredictTM intervention was well received by all users. All patients in the intervention felt a sense of security and reassurance with the two-way communication. After initial set up and learning to use the App (and the spirometry), patients found the App easy to use and navigate through. They felt well-supported by their health care professionals (HCPs) and received adequate training to use the system efficiently. Patients received prompt responses from their HCPs when contacted. Most patients felt that tasks required for the intervention did not impact their daily routine. Patients felt the system helped with identifying a change in their normal symptoms which enabled them to use their rescue pack at an appropriate time and prevented hospitalisation. Some felt that the COPDPredictTM enabled them to access HCPs during pandemic (COVID-19) as they expressed some difficulties in being able to see their own doctors.Has the registry been updated to include summary results?: Yes
If yes - please enter the URL to summary results:
If no – why not?:
Did you follow your dissemination plan submitted in the IRAS application form (Q A51)?: Yes
If yes, describe or provide URLs to disseminated materials: - Protocol paper has been published in the BMJ open in 2023 0.1136/bmjopen-2022-061050
- Phase III two Arm, multi-centre, open label, parallel-group randomised trial of the use of a personalised early warning decision support system to Predict and Prevent Acute Exacerbations of COPD: 'Predict & Prevent AECOPD' - Final Analysis Results. An abstract accepted for ATS conference May 2024. San Diego, CA, USA -Use of a personalised early warning decision support system for acute exacerbations of chronic obstructive pulmonary disease: results of the ‘Predict & Prevent’ phase III trial. End of the trial manuscript . A manuscript in the final draft and to be submitted soon to BMC soon
- A health economic manuscript is in a late draft stage to be submitted to Int J for COPD soon.
-Results from the qualitative sub-study will be published soon in a relevant peer reviewed journal.If pending, date when dissemination is expected:
If no, explain why you didn't follow it:
Have participants been informed of the results of the study?: Pending
If yes, describe and/or provide URLs to materials shared and how they were shared:
If pending, date when feedback is expected: 14/06/2024
If no, explain why they haven't:
Have you enabled sharing of study data with others?: No
If yes, describe or provide URLs to how it has been shared:
If no, explain why sharing hasn't been enabled:
Have you enabled sharing of tissue samples and associated data with others?: Yes
If yes, describe or provide a URL:
If no, explain why:
Submitted on: 30/05/2024REC name
London - Stanmore Research Ethics Committee
REC reference
19/LO/1939
Date of REC Opinion
18 Feb 2020
REC opinion
Further Information Favourable Opinion