Health TrialsbyHealthPanel

Utilising AI Analysis of Sounds To prEdict heaRt failurE decOmpensation

ConditionHeart Failure
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorCambridge University Hospitals NHS Foundation Trust

About this trial

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

This trial does not accept healthy volunteers

Qualifiers

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.

Disqualifiers

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.

Trial population

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

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • Height, weight, and BMI

    Other intervention

    Height, weight measurement and BMI calculation

  • Medical history

    Other intervention

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

  • Physical examination

    Other intervention

    Brief physical examination

  • Venous blood samples

    Diagnostic test

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

  • Resting vital signs

    Other intervention

    HR, BP, RR, oxygen saturations on air)

  • Transthoracic echocardiogram

    Diagnostic test

    LVEF, IVC collapsibility, LV filling pressure, PA pressure

  • Sound recordings

    Other intervention

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

  • Lung ultrasound

    Diagnostic test

    Lung ultrasound

  • KCCQ questionnaire

    Other intervention

    Kansas City Cardiomyopathy Questionnaire

  • ASCEND-HF score

    Other intervention

    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

  • Composite Everest congestion score

    Other intervention

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

  • Bio impedance and total body water measurement

    Diagnostic test

    Bio impedance and total body water measurement using TANITA device

Treatment groups

250 Participants
are divided into 1 treatment group
Group A: Patients with heart failure12 interventions

Trial outcomes

Primary outcomes

1

Area under receiver operating curve (AUC)

The maximum value is "1", describing ability of the AI algorithm to discriminate between dry and congested lungs

Time frame
Up to 48 months for data collection (includes part A (pilot) + part B (definitive study))
2

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

Time frame
Up to 48 months for data collection (includes part A (pilot) + part B (definitive study))
3

Sensitivity

The ability of the AI algorithm to correctly identify when a heart failure patient has pulmonary congestion

Time frame
Up to 48 months for data collection (includes part A (pilot) + part B (definitive study))
4

Specificity

The ability of the AI algorithm to correctly identify when a heart failure patient has no pulmonary congestion (dry lungs)

Time frame
Up to 48 months for data collection (includes part A (pilot) + part B (definitive study))

Secondary outcomes

1

Weight

Kg

Time frame
Delta congested (during HF decompensation) vs dry lungs (baseline)
2

NTproBNP

Ng/L

Time frame
Delta congested (during HF decompensation) vs dry lungs (baseline)
3

Heart rate

beats/minute

Time frame
Delta congested (during HF decompensation) vs dry lungs (baseline)
4

Respiratory rate

breaths/min

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

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Cambridge University Hospitals NHS Foundation Trust

Lead sponsor

University of Cambridge

Collaborator

Source ClinicalTrials.gov
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