Title: my Smart COPD exacerbation management (mySmartCOPD)
Funder: Department of Health and Social Care

Abstract: Background : Chronic obstructive pulmonary disease (COPD) is a common disease and is predicted to become the 3rd leading cause of death by 2030. Because there is no cure, treatment involves long-term management of symptoms to slow the decline of patient health and improve quality of life. A key characteristic of COPD is the presence of ‘exacerbation events’ defined as an acute worsening of symptoms. Exacerbation events are detrimental to health and often lead to hospitalization. Avoiding exacerbation events is desirable because it leads to higher quality of and longer life. Our recent pilot demonstrated that machine learning can predict acute exacerbation events several days in advance. Such predictions have huge potential to improve clinical models and drive behavioural change necessary to avoid acute exacerbation. Aims : We aim to predict COPD exacerbation events and use them to trigger interventions. The objectives are to 1) develop safe and reliable AI, 2) ensure acceptance and engagement with patients and clinicians, 3) establish clinical efficacy, safety, regulatory data, 4) establish a competitive AI-enhanced digital health ecosystem. Workplan : Four work packages will address clinical, technical and adoption. WP1 “Person-Centred Care” will create patient journey and clinical model, and work with clinicians, patients, and the public to ensure effectiveness and safety. WP2 “Agile Digital Platform Engineering” will develop a digital platform that incorporates AI components from WP3 “Predictive Analytics and Visualisation”. Finally, WP4 “Dissemination, Certification and NHS Adoption” will engage stakeholders, obtain regulatory approvals, and secure investment. Impact: By predicting and mitigating exacerbations, we aim to empower patients to engage with the management of their COPD and reduce the burden on NHS services. Exacerbation frequency/severity is expected to be reduced, slowing deterioration of lung function, and increasing quality of life. ROI is anticipated from reduced use of acute care and better use of medical interventions.

Lay abstract: Background: Chronic obstructive pulmonary disease (COPD) is a common disease and is predicted to become the 3rd leading cause of death by 2030. Because there is no cure, treatment involves long-term management of symptoms to slow the decline of patient health and improve quality of life. A key characteristic of COPD is the presence of exacerbation events defined as an acute worsening of symptoms. Exacerbation events are detrimental to health and often lead to hospitalization. Avoiding exacerbation events leads to a higher quality of and longer life. Aims: Our goal is to provide a new way of managing COPD by using computers to predict exacerbation events several days in advance. A recent feasibility study we ran demonstrated that using a technique called artificial intelligence (AI), computers could learn when patients were likely to exacerbate from reported symptoms, lifestyle, and demographic data. By providing an early warning, people with COPD have an opportunity to take action to prevent an exacerbation or lessen its severity. Consequently, we expect that patients will have more control over their condition, medication will be used more appropriately and allow more efficient use of NHS resources. Workplan: Our plan has four work packages addressing clinical, technical, modelling and adoption objectives. In WP1 &ldquo;Person Centred Care&rdquo; we will create the patient journey and clinical model working with clinicians, patients, and members of the public. WP1 will also validate the application in a clinical trial to ensure it is safe and effective. In WP2 &ldquo;Agile Digital Platform Engineering&rdquo;, we will extend the nationally available NHS myCOPD application with intelligent computer algorithms from a dedicated WP3 &ldquo;Predictive Analytics and Visualisation&rdquo;. Finally, WP4 &ldquo;Dissemination, Certification and NHS Adoption&rdquo; will engage stakeholders, obtain regulatory approvals, and secure further investment. Patient and public involvement has been an integral part of the myCOPD app and mySmartCOPD proposal development. PPI will continue in this project through co-creation, testing and evaluation activities from concept through to clinical trial. PPI will influence data collection, data usage, clinical model, and user interfaces. PPI will allow the evaluation of trust, adoption, and behavioural change. PPI will inform features and priorities for the clinical trial. Existing patients from the myCOPD digital health ecosystem will participate directly in the trial. Outcome and dissemination: AI-extension of an existing COPD management platform, allowing prediction of exacerbation events and personalised condition management, ready for real-world testing. Evidence-based clinical efficacy, safety and economic viability, including regulatory approval. Findings shared across patient and clinician communities, and peer-reviewed academic publications.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: RESPIRATORY