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

NCT04136418 | Unknown DMC

• 1 other identifier: Worktribe 833757
• Study Type: interventional
• Target: 384
• Locations: 1 country
• Sites: 1

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.

Conditions

Chronic Obstructive Pulmonary Disease

Outcome Measures

Primary Outcomes (1)

• AECOPD-related hospital admissions

  The number of AECOPD-related hospital admissions

  For a period of 12 months post randomisation

Secondary Outcomes (9)

• Total inpatient days

  For a period of 12 months post randomisation

• Number of COPD exacerbations reported by the patient

  For a period of 12 months post randomisation

• Number of A&E visits

  For a period of 12 months post randomisation

• Symptom control markers using Anthonisen criteria

  For a period of 12 months post randomisation

• End-user experience of the App

  For a period of 12 months post randomisation

Other Outcomes (2)

• Blood C-Reactive Protein (CRP) levels

  For a period of 12 months post randomisation

• Salivary C-Reactive Protein (CRP) levels

  For a period of 12 months post randomisation

Study Arms (2)

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

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

Device: COPDPredict mobile App

Interventions

COPDPredict mobile App 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

Mobile App device

Usual care OTHER

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

Usual care

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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;

Sponsors & Collaborators

• University of Birmingham: lead
• University Hospitals of North Midlands NHS Trust: collaborator

Study Sites (1)

University Hospitals Coventry & Warwickshire Trust

Coventry, England, CV2 2DX, United Kingdom

Location

Related Publications (3)

• 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.

  PMID: 40799048 DERIVED

• 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.

  PMID: 36914185 DERIVED

• 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.

  PMID: 34495549 DERIVED

MeSH Terms

Conditions

Pulmonary Disease, Chronic Obstructive

Condition Hierarchy (Ancestors)

Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: A phase III, 2 arm, multi-centre, open label, parallel-group randomised designed clinical investigation

Study Record Dates

• First Submitted: October 21, 2019
• First Posted: October 23, 2019
• Study Start: October 7, 2020
• Primary Completion: March 31, 2023
• Study Completion: March 31, 2023
• Last Updated: November 1, 2022

Record last verified: 2022-10

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)