Utilising AI Analysis of Sounds To prEdict heaRt failurE decOmpensation
Trial ID: NCT06555757
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-18
Completion date: 2027-08-15
Sponsor: Cambridge University Hospitals NHS Foundation Trust
Collaborators: University of Cambridge
Conditions: Heart Failure
Interventions: Height, weight, and BMI; Medical history; Physical examination; Venous blood samples; Resting vital signs; Transthoracic echocardiogram; Sound recordings; Lung ultrasound; KCCQ questionnaire; ASCEND-HF score; Composite Everest congestion score; Bio impedance and total body water measurement
Locations: Cambridge University Hospitals NHS Foundation Trust
Countries: United Kingdom
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
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.

Eligibility criteria:
Inclusion Criteria:

* 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.

Exclusion Criteria:

* Unable to provide consent
* Patients requiring continuous oxygen therapy at flow rates that cannot be provided through nasal cannula
* Patients with currently known pneumonia
* Patients with known significant pulmonary disease including asthma, COPD, pulmonary fibrosis/interstitial lung disease, pulmonary hemorrhage.
* Patients with current Pulmonary embolus
* Patients with other intercurrent acute symptomatic illness (e.g., viral/bacterial infection) at time of recording
* Patients requiring continuous oxygen therapy at flow rates that cannot be provided through nasal cannula
* Patients with tracheostomy or who have undergone a surgical procedure to the head/neck/larynx which would affect the normal functioning of the vocal cords.
* Aphasic
* Patients excluded at PI discretion

Outcomes:
Area under receiver operating curve (AUC); Negative and positive predictive value (NPV and PPV); Sensitivity; Specificity