Utilising AI Analysis of Sounds To prEdict heaRt failurE decOmpensation

Heart failure impacts more than 2% of people in the UK (United Kingdom) and leads to about 5% of emergency hospital visits. Patients might have slowly worsening symptoms or suddenly face acute decompensated heart failure (ADHF), marked by intense difficulty in breathing due to fast-developing lung congestion. This is a serious emergency requiring in-hospital treatment and monitoring. Once stable, patients usually have a phase where symptoms remain constant. But as time goes on, those with heart failure often face more frequent and prolonged episodes of ADHF. Fluid build-up (pulmonary congestion) in the lungs is a key issue in heart failure, and catching it early helps avoid unexpected hospital stays. Spotting these early signs outside the hospital can be tough, as symptoms aren't always clear. Study investigators are working on a new, non-invasive way to identify these early signs using AI (artificial intelligence) to analyse subtle changes in a patient's voice, cough, and breathing sounds. This tool will act as an early warning for patients and their heart care teams, allowing quicker treatment. This could make heart failure episodes less severe and reduce the need for hospital visits. This research has two parts. First, a small pilot trial with up to 50 patients. The findings will guide and inform a larger study involving up to 200 patients. From this larger study, investigators will develop the final version of the AI algorithm. The results from the Part A and Part B of this research will guide the investigators in planning a future clinical trial. This trial will confirm if the AI algorithm can be effectively used as a medical tool for heart failure care within the NHS (National Health Service). Study investigators will seek the necessary ethical approval before starting this trial.

NCT Number	NCT06555757
Lead Sponsor	Cambridge University Hospitals NHS Foundation Trust Collaborators: University of Cambridge
Conditions	Heart Failure
Enrollment	250 participants
Start Date	2024-09-18
Primary Completion	2027-08-15 (estimated)
Study Completion	2027-08-15 (estimated)
Updated on ClinicalTrials.gov	2026-05-12