Title: A Personalised Early Warning Decision Support System with novel Saliva Bio-Profiling to Predict and Prevent Acute Exacerbations of Chronic Obstructive Pulmonary Disease - 'Predict&Prevent AECOPD'
Funder: Department of Health and Social Care

Abstract: Research question: Can a personalised early warning decision support system with built-in composite predictive algorithms be used by patients with COPD at home to improve self-management and help them predict exacerbations early, intervene promptly and avoid hospitalisation? Background: Patients are encouraged to recognise and treat acute exacerbations of COPD (AECOPD) but in many cases because of day-to-day variability in symptoms, events go unrecognised and untreated; this can lead to hospital admissions and long term decline. In addition, heterogeneity amongst COPD patients has hampered personalised AECOPD recognition to date. Our proprietary Early Warning Decision Support System (Predict&Prevent AECOPD) uses longitudinal home monitoring of relevant subjective and objective data (symptoms, spirometry, biomarkers) in real-time using a smart App connected to Bluetooth-enabled sensor peripherals. These data are used to construct COPD-relevant individual profiles; AI-driven algorithms can then identify changes in health status and imminent AECOPD to provide timely individualised alerts and guidance/action plans for patients. Aim and Objectives: Our overall ambition is clinical validation and commercialisation of the Early Warning Decision Support System to guide and support COPD patients in identifying exacerbations early, leading to a 25% reduction in total AECOPD-induced hospital admissions. Using a multi-skills team approach, over 36 months we will establish: Clinical- and cost- effectiveness of Predict&Prevent AECOPD in a community setting. Regulatory approval for the two system versions. Stakeholder engagement and an implementation plan. A robust business model and commercial road-map. Methods: Clinical effectiveness will be tested in a randomised controlled trial (RCT) of Predict&Prevent AECOPD conducted in 384 COPD patients who have had &ge;2 AECOPD or &ge;1 hospital admission for AECOPD in previous year. This is powered to detect a 25% reduction in AECOPD hospitalisations and will validate our exacerbation prediction models. As models are constructed to work using both blood and salivary biomarker measurements, in this study the target biomarker, CRP will be measured in both biofluids, in anticipation of salivary CRP analysers being on the market within next 2 years. This addresses end-user preferences for saliva sampling, preferred over blood/urine. Trial findings will therefore assist in validating salivary CRP analysis. The RCT data will also be used to develop an economic model to describe cost-effectiveness, which in turn will refine our current budget impact model for the NHS. Parallel work packages will assess: Acceptability of the System to patients through qualitative research with 30 participants Utility of the System to policymakers, through inquiry and stakeholder events Bottom-up market analysis for the product by our business partner. Overall, the results will enable production of a coherent value proposition and implementation plan, product CE marking and value added partnerships to assist market penetration and uptake within the NHS/other sectors. Anticipated impact and dissemination: Predict&Prevent AECOPD with its smart algorithms offers an efficient and precise solution to supporting COPD patients at home and preventing exacerbation-induced hospitalisations. Impact on COPD care pathway is anticipated through NHS adoption and uptake/commissioning partnerships and changes to national AECOPD guidance. Dissemination will occur through conferences, publications, marketing and via our PPI groups and newsletter.

Lay abstract: Chronic obstructive pulmonary disease (COPD) is among the top four causes of chronic disability and death worldwide. Patients can have acute exacerbations of their COPD (AECOPD), also called lung attacks , which cause distress, reduce quality of life, and lead to 140,000 emergency hospital admissions a year. It is important patients can recognise and treat lung attacks as quickly as possible. Yet, treatment is often delayed because worsening of symptoms goes unrecognised. If patients had an easy-to-use handheld monitor to assess their health regularly at home it might help them identify lung attacks, start treatment early and avoid going to hospital. To address this, our team has worked with patients to develop and produce a smart Early Warning Decision Support System (Predict&Prevent AECOPD). This System automatically processes information that is regularly sent by patients using a mobile phone App (COPDinCheckTM), which connects to monitors via Bluetooth and uses intelligent software to determine a patient s health through a combination of wellbeing scores, lung function and measurements made in blood and saliva. When the System detects shifts from usual health, alerts are sent to the patient plus personalised instructions on what to do. Notifications are also sent to their healthcare team who offer guidance and support via the App s inbuilt secure messaging or by phone. Preliminary studies show that patients fully engage with the System, finding it easy to use and to track their health. Results suggest the System can detect attacks and is acceptable to patients; patients demonstrated increased confidence in identifying lung attacks and in self-managing at home, avoiding hospitalisation. Building on above results, this project will test the clinical and cost benefits of Predict&Prevent on 384 patients who have frequent lung attacks. Although less than 20% of COPD patients have frequent attacks, their treatment accounts for up to 75% of COPD-related costs in the NHS, mainly due to hospitalisations and increased medications. We hope to show that Predict&Prevent provides these patients with a personalised means of performing regular COPD health checks and reduces hospital admissions from lung attacks by at least 25%. We will also improve the quality of the alerts and the efficiency of our measurements through interviewing patients and healthcare professionals and by conducting extra work in the laboratory respectively. Overall, our System should give patients better personalised care and outcomes at lower costs than current practice. Our team will work alongside patient groups to ensure Predict&Prevent remains patient-centred throughout the project; this also helps us understand how the technology integrates into patients daily lives: its benefits and challenges. We will communicate results to hospitals, clinicians and managers who provide care and services to COPD patients to ensure that Predict&Prevent can be made widely available across the NHS.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: RESPIRATORY