Utilising AI Analysis of Sounds To prEdict heaRt failurE decOmpensation
NCT Number: NCT06555757
Acronym: STEREO
Status: NOT_YET_RECRUITING
Study Type: OBSERVATIONAL
Conditions: Heart Failure
Interventions: OTHER: Height, weight, and BMI|OTHER: Medical history|OTHER: Physical examination|DIAGNOSTIC_TEST: Venous blood samples|OTHER: Resting vital signs|DIAGNOSTIC_TEST: Transthoracic echocardiogram|OTHER: Sound recordings|DIAGNOSTIC_TEST: Lung ultrasound|OTHER: KCCQ questionnaire|OTHER: ASCEND-HF score|OTHER: Composite Everest congestion score|DIAGNOSTIC_TEST: Bio impedance and total body water measurement
Sponsor: Cambridge University Hospitals NHS Foundation Trust
Collaborators: University of Cambridge
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 250
Study Design: Observational Model: |Time Perspective: p
Start Date: 2024-08-15
Completion Date: 2027-08-15

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.

Primary Outcome Measures: Area under receiver operating curve (AUC), The maximum value is "1", describing ability of the AI algorithm to discriminate between dry and congested lungs, Up to 48 months for data collection (includes part A (pilot) + part B (definitive study))|Negative and positive predictive value (NPV and PPV), NPV and PPV describe the proportions of the positive (congested lungs) and negative (dry lungs) results predicted by the AI algorithm that are true results, Up to 48 months for data collection (includes part A (pilot) + part B (definitive study))|Sensitivity, The ability of the AI algorithm to correctly identify when a heart failure patient has pulmonary congestion, Up to 48 months for data collection (includes part A (pilot) + part B (definitive study))|Specificity, The ability of the AI algorithm to correctly identify when a heart failure patient has no pulmonary congestion (dry lungs), Up to 48 months for data collection (includes part A (pilot) + part B (definitive study))
Secondary Outcome Measures: Weight, Kg, Delta congested (during HF decompensation) vs dry lungs (baseline)|NTproBNP, Ng/L, Delta congested (during HF decompensation) vs dry lungs (baseline)|Heart rate, beats/minute, Delta congested (during HF decompensation) vs dry lungs (baseline)|Respiratory rate, breaths/min, Delta congested (during HF decompensation) vs dry lungs (baseline)|Blood pressure, mmHg, Delta congested (during HF decompensation) vs dry lungs (baseline)|Oxygen saturation (on air), Delta congested (during HF decompensation) vs dry lungs (baseline)|Left ventricular ejection fraction (ECHO), Delta congested (during HF decompensation) vs dry lungs (baseline)|Inferior vena cava collapsibility (ECHO), mm, Delta congested (during HF decompensation) vs dry lungs (baseline)|Left ventricular filling pressure (ECHO), Delta congested (during HF decompensation) vs dry lungs (baseline)|Pulmonary artery pressure, Low, intermediate and High probability with combination of different echo parameters (Tricuspid regurgitation velocity, Pulmonary artery acceleration time, right heart size \& pulmonary artery size), Delta congested (during HF decompensation) vs dry lungs (baseline)|Speech biomarker - Fundamental frequency, Hz, Delta congested (during HF decompensation) vs dry lungs (baseline)|Speech biomarker - Jitter and Shimmer, Delta congested (during HF decompensation) vs dry lungs (baseline)|Speech biomarker - Pause duration, ms, Delta congested (during HF decompensation) vs dry lungs (baseline)|Speech biomarker - Mel Frequency Spectral Coefficients, Delta congested (during HF decompensation) vs dry lungs (baseline)|KCCQ (Kansas City Cardiomyopathy Questionnaire) questionnaire, Overall scaled score (0-100) - higher score, better health status.

Average scores for each of the domains will also be calculated/ analysed separately, Delta congested (during HF decompensation) vs dry lungs (baseline)|ASCEND-HF score, An in-hospital congestion score which risk stratifies patients admitted with worsening heart failure, developed for the Acute study of clinical effectiveness of Nesiritide in decompensated heart failure trial

1-8 (higher score - increased congestion), Delta congested (during HF decompensation) vs dry lungs (baseline)|Composite Everest congestion score, A shortened version of the original 18-point score from the EVEREST trial

0-9 (higher score-increased congestion), Delta congested (during HF decompensation) vs dry lungs (baseline)|Physiological measures derived from a patient's own pacemaker or CRT device (such as thoracic impedance), Delta congested (during HF decompensation) vs dry lungs (baseline)|Bio-Impedance (TANITA), Ohms, Delta congested (during HF decompensation) vs dry lungs (baseline)|Total body water (TANITA), Delta congested (during HF decompensation) vs dry lungs (baseline)|Number of General Practitioner (GP) reviews, Number of GP reviews for heart failure exacerbations /12 months, 12 months|Number of heart failure specialist nurse reviews, Number of heart failure specialist nurse reviews / 12 months, 12 months|Number of A&E presentations for heart failure exacerbation, Number of A\&E presentations for heart failure exacerbation / 12 months, 12 months|Total overnight hospital admissions due to HF exacerbations, Total overnight hospital admissions / 12 months due to heart failure exacerbations, 12 months|Total days admitted as inpatient in hospital due to HF exacerbation, Total days admitted as inpatient in hospital due to HF exacerbation over last 12 months, 12 months|8-point method to detect pulmonary congestion (lung US), Count of B-lines in each of the 8 zones, Delta congested (during HF decompensation) vs dry lungs (baseline)