Utilising AI Analysis of Sounds To prEdict heaRt failurE decOmpensation

NCT06555757 | Not Yet Recruiting

• 1 other identifier: STEREO (A096862)
• Study Type: observational
• Target: 250
• Locations: 0 countries
• Sites: N/A

Brief 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.

Conditions

Heart Failure

Keywords

heart failure; cardiovascular; artificial intelligence

Outcome Measures

Primary Outcomes (4)

• 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 Outcomes (26)

• Weight

  Delta congested (during HF decompensation) vs dry lungs (baseline)

• NTproBNP

  Delta congested (during HF decompensation) vs dry lungs (baseline)

• Heart rate

  Delta congested (during HF decompensation) vs dry lungs (baseline)

• Respiratory rate

  Delta congested (during HF decompensation) vs dry lungs (baseline)

• Blood pressure

  Delta congested (during HF decompensation) vs dry lungs (baseline)

Study Arms (1)

Patients with heart failure

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).

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

Interventions

Height, weight, and BMI OTHER

Height, weight measurement and BMI calculation

Patients with heart failure

Medical history OTHER

Brief medical history including medications/allergies and heart failure related healthcare utilisation over previous 12 months

Patients with heart failure

Physical examination OTHER

Brief physical examination

Patients with heart failure

Venous blood samples DIAGNOSTIC_TEST

Venous blood samples, to include WCC, HB, CRP and NTproBNP

Patients with heart failure

Resting vital signs OTHER

HR, BP, RR, oxygen saturations on air)

Patients with heart failure

Transthoracic echocardiogram DIAGNOSTIC_TEST

LVEF, IVC collapsibility, LV filling pressure, PA pressure

Patients with heart failure

Sound recordings OTHER

Sound recordings (voice/cough/chest) recorded with the in-built microphone in a smartphone

Patients with heart failure

Lung ultrasound DIAGNOSTIC_TEST

Lung ultrasound

Patients with heart failure

KCCQ questionnaire OTHER

Kansas City Cardiomyopathy Questionnaire

Patients with heart failure

ASCEND-HF score OTHER

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

Patients with heart failure

Composite Everest congestion score OTHER

A shortened version of the original 18-point score from the EVEREST trial

Patients with heart failure

Bio impedance and total body water measurement DIAGNOSTIC_TEST

Bio impedance and total body water measurement using TANITA device

Patients with heart failure

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients diagnosed with chronic stable heart failure NYHA Class 3 or 4.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• Cambridge University Hospitals NHS Foundation Trust: lead
• University of Cambridge: collaborator

MeSH Terms

Conditions

Heart Failure

Interventions

Body Height; Weights and Measures; Body Mass Index; Health Records, Personal; Restraint, Physical; Echocardiography; Sound Recordings

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Intervention Hierarchy (Ancestors)

Body Size; Body Weights and Measures; Body Constitution; Physical Examination; Diagnostic Techniques and Procedures; Diagnosis; Physical Appearance, Body; Anthropometry; Investigative Techniques; Physiological Phenomena; Growth; Growth and Development; Biometry; Epidemiologic Measurements; Public Health; Environment and Public Health; Medical Records; Records; Data Collection; Epidemiologic Methods; Behavior Control; Therapeutics; Immobilization; Cardiac Imaging Techniques; Diagnostic Imaging; Ultrasonography; Heart Function Tests; Diagnostic Techniques, Cardiovascular; Audiovisual Aids; Educational Technology; Technology; Technology, Industry, and Agriculture

Study Officials

• Joseph Cheriyan

  Cambridge University Hospitals NHS Foundation Trust

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Erdem Demir

CONTACT

01223 256621; erdem.demir1@nhs.net

Heike Templin

CONTACT

01223 250874; heike.templin@nhs.net

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Consultant Clinical Pharmacologist/Affiliated Associate Professor

Study Record Dates

• First Submitted: June 7, 2024
• First Posted: August 15, 2024
• Study Start: August 15, 2024
• Primary Completion (Estimated): August 15, 2027
• Study Completion (Estimated): August 15, 2027
• Last Updated: August 15, 2024

Record last verified: 2024-08