Flare-CF Innovation Hub

Forecasting Lung Health and using Artificial Intelligence to Determine Treatment Recommendations for Exacerbations & Infections in Cystic Fibrosis

What's the problem?

People with CF are susceptible to hard-to-treat lung infections that can be a serious risk to health. From time to time, they can experience a sudden worsening of lung symptoms called an exacerbation. It’s hard to predict when exacerbations might be about to flare up and they can require intensive treatment, often with courses of intravenous (IV) antibiotics. Together these cause a great deal of disruption to day-to-day life and may lead to permanent lung damage.

How is it being solved?

The Flare-CF Innovation Hub aims to use cutting-edge methods including machine learning to identify and understand early signs of exacerbations. This knowledge will be used to develop at-home tests to detect current infections and predict the best combination of antibiotics people with CF might need in the future. The programme of research is divided into two workstreams.

Workstream 1: Forecasting and detecting exacerbations 

This workstream will build on the team’s successful previous analyses of sputum, blood and breath samples, combined with their established home monitoring setup (Project Breathe), to evaluate the development of an eventual home point-of-care test detecting current infection or predicting the future need for antibiotic treatment for an exacerbation.

Workstream 2: Predicting the best combinations of existing antibiotics for each CF individual

In this workstream, the Flare-CF team will use machine learning, combining a range of clinical and biological data, to develop a new way to predict and select the best combination of antibiotic treatments for chronic lung infections and exacerbations in CF. 

How will this make a difference?

Ultimately, the Flare-CF Innovation Hub could help to reduce treatment burden for people with cystic fibrosis and help to limit side-effects including antibiotic resistance.