Prospective Longitudinal Evaluation of AI-ECG in a NEwly Diagnosed Heart Failure

Recruiting

• Conditions: Heart Failure
• Interventions: Diagnostic Test: Eko DUO

• Registration Number: NCT05817136
• Lead Sponsor: Imperial College London

Brief Summary

Background:

Heart Failure (HF) is a condition in which the heart can no longer adequately pump blood around the body. The number of patients diagnosed with HF is increasing, consuming 4% of the NHS budget, and deadlier than most cancers. Most patients suffer from HF with reduced Ejection Fraction (HFrEF), where adequate treatment can improve quality of life and survival. Less than 50% of patients receive gold standard NHS guided medication and less than 20% receive appropriate monitoring (via echocardiography surveillance).

This study looks at the use of a 'smart stethoscope' (Eko DUO), a stethoscope that uses information collected from the heart in the form of electrical (ECG) and sounds (phonocardiogram, PCG) waveforms, to predict the pumping function of the heart via artificial intelligence (AI-ECG).

Aims:

By using the smart stethoscope, this study evaluates whether the use of an easy-to-use home self-monitoring programme can:

* Provide a solution for the current poor compliance of NHS echocardiogram surveillance programmes for people with newly diagnosed HF

* Provide real-time assessment of heart function in response to medication changes

* Improve the health economic and health outcomes of HF in the NHS

Methods:

80 participants with newly diagnosed HFrEF, due to pre-existing heart disease and non-heart related causes, will be identified by the clinical team at Imperial College NHS Trust and obtain consent for the research team to approach them. All consented participants will receive a smart stethoscope and instructions for twice-weekly, 15-second self-examination for 3-months. Participants will also be invited for an additional echocardiogram at 6 weeks post-diagnosis, in addition to the routine, standard of care NHS echocardiogram surveillance for HF.

Detailed Description

Not available

Study Timeline

• Study Start: 2023-01-11
• Status Verified: 2023-04
• Study First Submitted: 2023-04-04
• Study First Submitted QC: 2023-04-04
• Last Update Submitted: 2023-04-04
• Study First Posted: 2023-04-18
• Last Update Posted: 2023-04-18
• Primary Completion: 2024-06-11
• Study Completion: 2024-06-11

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 80

Inclusion Criteria

• Age 18 years or above
• Able to give informed consent
• Newly diagnosed with HFrEF (i.e., LVEF below 40%) assessed by a consultant cardiologist within the past two weeks.

Exclusion Criteria

• Any chest wound, skin pathology or other feature that would prohibit routine Eko DUO examination.
• Participants who have been diagnosed with HF previously

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
non-coronary HF aetiology	Eko DUO	Patients with non-coronary HF
coronary HF aetiology	Eko DUO	Patients with coronary HF

Primary Outcome Measures

Name	Time	Method
Descriptive analysis of trends and association of raw AI-ECG signals changes that correlate with HF progression.	Up to 18 months	1. AI-ECG signal changes with LV impairment.; 2. AI-ECG signal changes with medication optimization.; 3. AI-ECG signal changes with healthcare episodes.

Secondary Outcome Measures

Name	Time	Method
Sensitivity and specificity of AI-ECG to predict HF progression	Up to 18 months	1. AI-ECG prediction of HF trajectory deterioration or improvement.; 2. AI-ECG prediction of clinical congestive HF

Trial Locations

Locations (1)

Imperial College London; 🇬🇧 London, United Kingdom