Title: Use of machine learning to predict acute pulmonary exacerbations in Cystic Fibrosis
Funder: Department of Health and Social Care

Abstract: Our application builds on our extensive experience of using home-monitoring to manage cystic fibrosis (CF), an exemplar for respiratory conditions where burden of care has been identified by patients as the highest research priority and existing standards of care models recognised as unsustainable. CF is characterised periods of relative stability punctuated by abrupt clinical deterioration known as acute pulmonary exacerbations (APEs) which drive pulmonary inflammation, progressive lung damage, and premature death. In preliminary studies, we have already demonstrated that home monitoring empowers patients and can safely reduce routine outpatient attendance by 50% to the benefit of patients, healthcare providers, and payers. Using home monitoring data from our previous national feasibility study ( SmartCareCF ), we have created a prototype AI-powered predictive algorithm that can forecast the likelihood of an impending APE an average of 10 days earlier than conventional care. We now propose to operationalise this predictive algorithm by: 1) refining the algorithm using data obtained from a US CF Foundation-funded clinical implementation study ( Project Breathe ) in 4 UK CF centres. 2) Optimising the user experience with the application through a series of beta testing evaluations by expert patients led by our PPI lead. 3) Performing a randomised trial (in adults with CF from four leading UK Centres) comparing the impact on patients of seeing home monitoring data alone or in conjunction with the continuous output of the predictive algorithm. We will evaluate the impact of seeing the algorithm output on quality of life (primary output) as well as on physical wellbeing (lung function, weight), emergency contacts to CF Centres, and days of antibiotic treatment. We would anticipate that, by the end of the study, our algorithm will achieve CE marking and be ready for commercial roll out, and could be extended to other chronic respiratory conditions such as COPD and Asthma.

Lay abstract: Cystic Fibrosis (CF) is a life-limiting inherited condition that causes a build up of thick secretions in the lung (and other organs) that leads to severe and persistent bacterial infections and subsequently lung damage and death. In common with many other lung diseases (such as Asthma and smoking-related lung disease (COPD)), people with CF are medically stable for long periods of time and then suddenly deteriorate, becoming breathless and needing treatment (at home or in hospital) with antibiotics; these periods are called acute pulmonary exacerbations (APE). We have shown that, by using Artificial Intelligence (AI), we can analysis signals from home monitoring equipment (measuring oxygen levels, lung function, activity, weight, and pulse rate) to predict an future APE over 10 days sooner than just relying on patients' symptoms. The AI technology (i) allows treatment to be given earlier, preventing or minimising the APE, and (we anticipate) protecting lung function in the long term; (ii) supports people with CF to be safely managed at home without the need for routine clinic reviews and provides data to assist video/telephone clinics; (iii) provides support for decision making by patients and healthcare providers about safely redrawing or introducing new medicines; and (iv) empowers people with CF to take control of their health. Our proposal seeks to improve our existing AI technology and allow it to be used clinically, which will involve small tests of several versions, to make sure it is user friendly and provides the information in the most appropriate way. We will improve the prediction ability and accuracy, using new data we have continued to collect from adults with CF (with their consent) who are home monitoring. Finally, we will test the AI prediction in a clinical trial, where people on home monitoring (who want to be involved in the study) will either see the AI prediction or not. We will look at user acceptability, how much time participants spend unwell, how many times they require treatment with antibiotics or need to contact their CF centre as an emergency, and how lung function and weight changes over the 12 months of the study. If we can show that the AI prediction is helpful and safe, we can then roll it out to all adults with CF in the UK (and then potentially elsewhere), and then eventually explore whether our AI approach can generate specific predictions for people with asthma and COPD.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: CONGENITAL DISORDERS