Predict&Prevent: Use of a Personalised Early Warning Decision Support System to Predict and Prevent Acute Exacerbations of COPD

NCT ID: NCT04136418

Last Updated: 2022-11-01

Basic Information

• Recruitment Status: UNKNOWN
• Clinical Phase: NA
• Total Enrollment: 384 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-10-07
• Study Completion Date: 2023-03-31

Brief 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.

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

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: PREVENTION
• Blinding Strategy: NONE

Study Groups

Usual care

Patients currently self-manage their condition using antibiotics and steroids when their disease symptoms match the criteria in information provided by a clinician

Group Type: ACTIVE_COMPARATOR

Usual care

Intervention Type: OTHER

Patients self-manage their COPD using prescribed medication in accordance with basic guidance information

Mobile App device

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

Group Type: EXPERIMENTAL

COPDPredict mobile App

Intervention Type: DEVICE

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

Interventions

COPDPredict mobile App

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

Intervention Type: DEVICE

Usual care

Patients self-manage their COPD using prescribed medication in accordance with basic guidance information

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• 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

Exclusion Criteria

• 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;

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University Hospitals of North Midlands NHS Trust

OTHER

Sponsor Role: collaborator

University of Birmingham

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

University Hospitals Coventry & Warwickshire Trust

Coventry, England, United Kingdom

Countries

United Kingdom

References

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.

Reference Type: DERIVED

PMID: 40799048 (View on PubMed)

Kaur 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.

Reference Type: DERIVED

PMID: 36914185 (View on PubMed)

Poot 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.

Reference Type: DERIVED

PMID: 34495549 (View on PubMed)

Other Identifiers

Worktribe 833757

Identifier Type: -

Identifier Source: org_study_id