NCT04136418

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

Status unknown NA Last updated 1 November 2022

What this trial tests

NA trial testing COPDPredict mobile App in Chronic Obstructive Pulmonary Disease in 384 participants. Status unknown.

Timeline

7 October 2020

Primary endpoint
31 March 2023

31 March 2023

Quick facts

Lead sponsor	University of Birmingham
Phase	NA
Status	Status unknown
Study type	INTERVENTIONAL
Allocation	randomized
Design	parallel
Masking	none
Primary purpose	prevention
Enrollment	384
Start date	7 October 2020
Primary completion	31 March 2023
Estimated completion	31 March 2023
Sites	1 location across United Kingdom

Drugs / interventions tested

• COPDPredict mobile App
• Usual care

Conditions studied

• Chronic Obstructive Pulmonary Disease — all drugs for Chronic Obstructive Pulmonary Disease →

Sponsor

University of Birmingham

Who can join

18 and older, any sex, with Chronic Obstructive Pulmonary Disease. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

6 peer-reviewed publications reference this trial (live from Europe PMC):

1. Integrated disease management interventions for patients with chronic obstructive pulmonary disease.
   Poot CC, Meijer E, Kruis AL, Smidt N, et al · · 2021 · cited 48× · PMID 34495549 · DOI 10.1002/14651858.cd009437.pub3
2. Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations.
   Patel N, Kinmond K, Jones P, Birks P, et al · · 2021 · cited 21× · PMID 34188465 · DOI 10.2147/copd.s309372
3. 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 A
   Kaur D, Mehta RL, Jarrett H, Jowett S, et al · · 2023 · cited 2× · PMID 36914185 · DOI 10.1136/bmjopen-2022-061050
4. 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.
   Gkini E, Mehta RL, Tearne S, Doos L, et al · · 2025 · PMID 40799048 · DOI 10.1080/15412555.2025.2544719
5. 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.
   Hall JA, Turner AM, Gkini E, Mehta R, et al · · 2025 · PMID 40453980 · DOI 10.2147/copd.s486309
6. 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
   Gkini E, Mehta RL, Tearne S, Doos L, et al · · 2024 · DOI 10.21203/rs.3.rs-4616866/v1