Chronic Obstructive Pulmonary DiseaseNCT04136418
Predict&Prevent: Use of a Personalised Early Warning Decision Support System to Predict and Prevent Acute Exacerbations of COPD
A study testing COPDPredict mobile App in people with Chronic Obstructive Pulmonary Disease, run by University of Birmingham at 1 site in United Kingdom.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Chronic Obstructive Pulmonary Disease
- What is being tested?
- COPDPredict mobile App
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 384 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 7 Oct 2020to 31 Mar 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Clinically diagnosed chronic obstructive pulmonary disease (COPD), confirmed by post-bronchodilator spirometry and defined as a ratio of Forced Expiratory VolumeFEV1 to Forced Vital Capacity <0.7 and <lower limit of normal for age post bronchodilator use
- ≥2 Acute Exacerbations of COPD (AECOPD) in the previous 12 months according to the patient and/or ≥1 hospital admission for AECOPD
- Exacerbation free for at least 6 weeks
- An age of at least 18 years
- Willing and able to comply with the data collection process out to 12 months from randomisation
- Ability to consent
- Ability to use intervention as judged by the investigator at screening, upon demonstration of the system to the patient
Who cannot join
- Life expectancy < 12 months
- Patients with active infection, unstable co-morbidities at enrolment or very severe comorbidities such as grade IV heart failure, renal failure on haemodialysis or active neoplasia or significant cognitive impairment;
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
University Hospitals Coventry & Warwickshire Trust
Coventry, England, CV2 2DX
About this study
Official title: A Randomised Designed Clinical Investigation of 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
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.
Read more
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.
Read the detailed description
Changes in dyspnoea, coughing and/or sputum production often precede exacerbations but as symptoms vary within-same day and across days, patients cannot easily judge the significance of such changes with the result that exacerbations remain unreported and untreated. Furthermore due to heterogeneity amongst COPD patients, predictions must be personalised to be clinically meaningful. Remote monitoring and POC systems have evolved rapidly but none have yet convincingly demonstrated the capability to predict exacerbations and stratify episode severity.
To address the above problem, COPDPredictTM has been created and developed. This System automatically processes information that is regularly sent by patients using COPDPredictTM), which connects to peripheral monitors via Bluetooth and uses intelligent software to determine a patient's health through a combination of wellbeing scores, lung function and measurements of key biomarkers in blood and saliva. The clinical team has access to a secure web portal (dashboard) which allows them to monitor patient data, case manage and make informed decisions on clinical practice.
Depending on the degree of change from a given patient's 'usual health', timely alerts are sent to the individual, with sign-posting to an action plan. Alerts are also sent to clinicians who support and advise patients via App's secure messaging facility. If patients fail to improve with self-treat plan or if an episode triggers an 'at high risk alert' from the start, clinicians are prompted to be involved and intervene with escalated treatment
The Clinician facing dashboard allows for "real-time" case management and the ability to remotely monitor the patients and facilitate interaction. Clinicians can choose to escalate treatments based on the results being transmitted by the patients.
This clinical investigation asks if COPDPredictTM can be used by patients with COPD at home and the clinicians managing the patients to improve self-management and help them identify exacerbations, intervene promptly and avoid hospitalisation. The clinical investigation will randomise 384 patients, from 4 hospitals in the West Midlands. United Kingdom, who have frequent AECOPD to use either the SSMP and RM (if needed according to the SSMP) or the COPDPredict App and RM (if needed according to the App self-management plan or clinician input).
- Conditions
- Chronic Obstructive Pulmonary Disease
- Being tested
- COPDPredict mobile App, Usual care
- Medical subject headings
- Pulmonary Disease, Chronic Obstructive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
COPDPredict mobile AppDevice
An App on a mobile device is used by the patient to track the status of their COPD and inform the patient's care team
Usual careOther
Patients self-manage their COPD using prescribed medication in accordance with basic guidance information
| Study group | Role | What they receive |
|---|---|---|
| Usual care | Active comparator | Patients currently self-manage their condition using antibiotics and steroids when their disease symptoms match the criteria in information provided by a clinician Receives: Other: Usual care |
| Mobile App device | Experimental | Patients enter their health status onto an App which is relayed to the healthcare team, who can then provide further information or clinical intervention should they so choose Receives: Device: COPDPredict mobile App |
What is being measured
The primary outcome is the main question the study is designed to answer.
AECOPD-related hospital admissions
The number of AECOPD-related hospital admissions
Measured: For a period of 12 months post randomisation
Secondary outcomes (9)
Total inpatient days
Number of days a patient is in hospital
Measured: For a period of 12 months post randomisation
Number of COPD exacerbations reported by the patient
Number of patient defined exacerbations
Measured: For a period of 12 months post randomisation
Number of A&E visits
Number of times that a patient reports attending Accident \& Emergency (A\&E) due to COPD exacerbations
Measured: For a period of 12 months post randomisation
Symptom control markers using Anthonisen criteria
Presence of symptom control markers (breathlessness, colour of sputum, amount of sputum produced)
Measured: For a period of 12 months post randomisation
End-user experience of the App
technology acceptability usability/utility via bespoke qualitative questionnaires and interviews
Measured: For a period of 12 months post randomisation
COPD specific health-related quality of life
Assessed by the COPD Assessment Test validated questionnaire
Measured: 3, 6, 9 and 12 months post randomisation
Health-related quality of life
Assessed by the EQ-5D-5L validated questionnaire
Measured: 3, 6, 9 and 12 months post randomisation
Lifestyle choices
assessed via either responses to bespoke questions on the App or bespoke questionnaires and interviews
Measured: 3, 6, 9 and 12 months post randomisation
Functional expiratory volume (FEV1)
Functional expiratory volume assessed by spirometry
Measured: At 12 months post randomisation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Oct 2019
- Study start7 Oct 2020
- Primary completion31 Mar 2023 (estimated)
- Study completion31 Mar 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Chronic Obstructive Pulmonary DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- A phase III, 2 arm, multi-centre, open label, parallel-group randomised designed clinical investigation
- Masking
- None (open label)
- Enrolment
- 384 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Birmingham
- Sponsor type
- University, hospital or other
- Collaborators
- University Hospitals of North Midlands NHS Trust
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04136418
- Sponsor's protocol ID
- Worktribe 833757
- Status last verified
- Oct 2022
- First submitted
- 21 Oct 2019
- Last updated
- 1 Nov 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The data will be commercially sensitive
Publications
Gkini E, Mehta RL, Tearne S, Doos L, Jowett S, Gale N, Turner AM. 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. COPD. 2025 Dec;22(1):2544719. doi: 10.1080/15412555.2025.2544719. Epub 2025 Aug 13.
PubMed 40799048Hall JA, Turner AM, Gkini E, Mehta R, Spiteri M, Patel N, Jowett S. The Cost-Effectiveness of a Personalised Early Warning Decision Support System (The COPDPredict System) to Predict and Prevent Acute Exacerbations of Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis. 2025 May 25;20:1693-1710. doi: 10.2147/COPD.S486309. eCollection 2025.
PubMed 40453980Kaur D, Mehta RL, Jarrett H, Jowett S, Gale NK, Turner AM, Spiteri M, Patel N. Phase III, two arm, multi-centre, open label, parallel-group randomised designed clinical investigation of the use of a personalised early warning decision support system to predict and prevent acute exacerbations of chronic obstructive pulmonary disease: 'Predict & Prevent AECOPD' - study protocol. BMJ Open. 2023 Mar 13;13(3):e061050. doi: 10.1136/bmjopen-2022-061050.
PubMed 36914185Poot CC, Meijer E, Kruis AL, Smidt N, Chavannes NH, Honkoop PJ. Integrated disease management interventions for patients with chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2021 Sep 8;9(9):CD009437. doi: 10.1002/14651858.CD009437.pub3.
PubMed 34495549
Study at a glance
From the registry record, last updated by the sponsor on 1 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Randomised Designed Clinical Investigation of 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
- ClinicalTrials.gov ID
- NCT04136418
- Acronym / sponsor ID
- Worktribe 833757
- Conditions
- Chronic Obstructive Pulmonary Disease
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 384 participants (estimated)
- Lead sponsor
- University of BirminghamUniversity, hospital or other
- Collaborators
- University Hospitals of North Midlands NHS Trust
- Study start
- 7 Oct 2020 (actual)
- Primary completion
- 31 Mar 2023 (estimated)
- Study completion
- 31 Mar 2023 (estimated)
- First posted
- 23 Oct 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Changes in dyspnoea, coughing and/or sputum production often precede exacerbations but as symptoms vary within-same day and across days, patients cannot easily judge the significance of such changes with the result that exacerbations remain unreported and untreated. Furthermore due to heterogeneity amongst COPD patients, predictions must be personalised to be clinically meaningful. Remote monitoring and POC systems have evolved rapidly but none have yet convincingly demonstrated the capability to predict exacerbations and stratify episode severity.
To address the above problem, COPDPredictTM has been created and developed. This System automatically processes information that is regularly sent by patients using COPDPredictTM), which connects to peripheral monitors via Bluetooth and uses intelligent software to determine a patient's health through a combination of wellbeing scores, lung function and measurements of key biomarkers in blood and saliva. The clinical team has access to a secure web portal (dashboard) which allows them to monitor patient data, case manage and make informed decisions on clinical practice.
Depending on the degree of change from a given patient's 'usual health', timely alerts are sent to the individual, with sign-posting to an action plan. Alerts are also sent to clinicians who support and advise patients via App's secure messaging facility. If patients fail to improve with self-treat plan or if an episode triggers an 'at high risk alert' from the start, clinicians are prompted to be involved and intervene with escalated treatment
The Clinician facing dashboard allows for "real-time" case management and the ability to remotely monitor the patients and facilitate interaction. Clinicians can choose to escalate treatments based on the results being transmitted by the patients.
This clinical investigation asks if COPDPredictTM can be used by patients with COPD at home and the clinicians managing the patients to improve self-management and help them identify exacerbations, intervene promptly and avoid hospitalisation. The clinical investigation will randomise 384 patients, from 4 hospitals in the West Midlands. United Kingdom, who have frequent AECOPD to use either the SSMP and RM (if needed according to the SSMP) or the COPDPredict App and RM (if needed according to the App self-management plan or clinician input).
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- A phase III, 2 arm, multi-centre, open label, parallel-group randomised designed clinical investigation
| Arm | Type | Receives |
|---|---|---|
| Usual care | Active comparator | Other: Usual carePatients currently self-manage their condition using antibiotics and steroids when their disease symptoms match the criteria in information provided by a clinician |
| Mobile App device | Experimental | Device: COPDPredict mobile AppPatients enter their health status onto an App which is relayed to the healthcare team, who can then provide further information or clinical intervention should they so choose |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (7 listed)
- Clinically diagnosed chronic obstructive pulmonary disease (COPD), confirmed by post-bronchodilator spirometry and defined as a ratio of Forced Expiratory VolumeFEV1 to Forced Vital Capacity <0.7 and <lower limit of normal for age post bronchodilator use
- ≥2 Acute Exacerbations of COPD (AECOPD) in the previous 12 months according to the patient and/or ≥1 hospital admission for AECOPD
- Exacerbation free for at least 6 weeks
- An age of at least 18 years
- Willing and able to comply with the data collection process out to 12 months from randomisation
Key exclusion criteria (2 listed)
- Life expectancy < 12 months
- Patients with active infection, unstable co-morbidities at enrolment or very severe comorbidities such as grade IV heart failure, renal failure on haemodialysis or active neoplasia or significant cognitive impairment;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| COPDPredict mobile AppAn App on a mobile device is used by the patient to track the status of their COPD and inform the patient's care team | Device | Mobile App device |
| Usual carePatients self-manage their COPD using prescribed medication in accordance with basic guidance information | Other | Usual care |
Primary outcome
AECOPD-related hospital admissions
Time frame: For a period of 12 months post randomisation
The number of AECOPD-related hospital admissions
9 secondary outcomes
Total inpatient days
Time frame: For a period of 12 months post randomisation
Number of COPD exacerbations reported by the patient
Time frame: For a period of 12 months post randomisation
Number of A&E visits
Time frame: For a period of 12 months post randomisation
Symptom control markers using Anthonisen criteria
Time frame: For a period of 12 months post randomisation
End-user experience of the App
Time frame: For a period of 12 months post randomisation
COPD specific health-related quality of life
Time frame: 3, 6, 9 and 12 months post randomisation
Health-related quality of life
Time frame: 3, 6, 9 and 12 months post randomisation
Lifestyle choices
Time frame: 3, 6, 9 and 12 months post randomisation
Functional expiratory volume (FEV1)
Time frame: At 12 months post randomisation
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Nov 2022.
- Derived from this study: PubMed 40799048Gkini E, Mehta RL, Tearne S, Doos L, Jowett S, Gale N, Turner AM. 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. COPD. 2025 Dec;22(1):2544719. doi: 10.1080/15412555.2025.2544719. Epub 2025 Aug 13.
- Derived from this study: PubMed 40453980Hall JA, Turner AM, Gkini E, Mehta R, Spiteri M, Patel N, Jowett S. The Cost-Effectiveness of a Personalised Early Warning Decision Support System (The COPDPredict System) to Predict and Prevent Acute Exacerbations of Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis. 2025 May 25;20:1693-1710. doi: 10.2147/COPD.S486309. eCollection 2025.
- Derived from this study: PubMed 36914185Kaur D, Mehta RL, Jarrett H, Jowett S, Gale NK, Turner AM, Spiteri M, Patel N. Phase III, two arm, multi-centre, open label, parallel-group randomised designed clinical investigation of the use of a personalised early warning decision support system to predict and prevent acute exacerbations of chronic obstructive pulmonary disease: 'Predict & Prevent AECOPD' - study protocol. BMJ Open. 2023 Mar 13;13(3):e061050. doi: 10.1136/bmjopen-2022-061050.
- Derived from this study: PubMed 34495549Poot CC, Meijer E, Kruis AL, Smidt N, Chavannes NH, Honkoop PJ. Integrated disease management interventions for patients with chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2021 Sep 8;9(9):CD009437. doi: 10.1002/14651858.CD009437.pub3.
- Individual participant dataSharing planned: No. The data will be commercially sensitive
Related trials for Chronic Obstructive Pulmonary Disease
Active, not recruiting
Symptoms and Functions in Patients With COPD and Chronic Bronchitis Switching From CIG to THS
COPD, Smoking
Not yet recruitingPhase 2/3
An Open-Label Extension Study to Evaluate Inhaled Treprostinil SMI in Participants With Pulmonary Hypertension Associated With Chronic Obstructive Pulmonary Disease (PH-COPD)
Pulmonary Hypertension Associated With Chronic Obstructive Pulmonary Disease
Not yet recruiting
The Aim of the Study is to Decipher the Alterations in Signalling Pathways of Skeletal Muscle Stem Cells (Satellite Cells (SCs)) in Individuals With COPD, as Well as to Examine the Effect of Exercise Training on SCs.
COPD (Chronic Obstructive Pulmonary Disease), Sarcopenia in Elderly
Not yet recruitingPhase 2
Study of Treprostinil Administered Via Soft Mist Inhaler (SMI) in Participants With Pulmonary Hypertension Associated With Chronic Obstructive Pulmonary Disease (PH-COPD)
Pulmonary Hypertension Associated With Chronic Obstructive Pulmonary Disease
RecruitingPhase 3
A Study to Investigate the Efficacy and Safety of Belumosudil Compared With Best Available Therapy in Participants Aged 12 Years or Older With Chronic Graft-versus-host Disease
Chronic Graft Versus Host Disease
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing COPDPredict mobile App in people with Chronic Obstructive Pulmonary Disease, run by University of Birmingham at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 1 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AECOPD-related hospital admissions (time frame: For a period of 12 months post randomisation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04136418, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Nov 2022.