ClinicaliQ Trial Snapshot
- Utilising AI Analysis of Sounds To prEdict heaRt failurE decOmpensation — Recruiting • Cardiology / Cardiovascular • NCT06555757.
- What is being tested: AI-powered analysis of respiratory and cardiac sounds to predict acute decompensated heart failure (ADHF) before clinical deterioration occurs, potentially enabling earlier intervention.
- Patient eligibility overview: The trial includes heart failure patients at risk of decompensation, focusing on those who may experience either gradual symptom worsening or sudden acute episodes requiring hospitalisation.
Use This Page For
- Quick orientation before opening the registry record.
- Checking recruitment status, phase and sponsor at a glance.
- Connecting this trial to nearby guidelines, Drug Science and education.
What This Trial Is Studying
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)…
Eligibility Snapshot
- : * Male or Female, aged 18 years or above. * Diagnosed with chronic stable heart failure NYHA Class 3 or 4 (either during most recent cardiology/heart failure clinic visit, or ADHF during recent/current hospitalization). * Participant is willing and able to give informed consent for participation in the study. * Participant has a smartphone device and can download a purposely designed mobile application on their phone (with guidance from the study investigators) or is willing to have sound recordings via a smartphone device loaned for the purpose of the study.
Use the source registry for the full inclusion and exclusion criteria before discussing referral or enrolment.