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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:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| coronary HF aetiology | Patients with coronary HF |
| |
| non-coronary HF aetiology | Patients with non-coronary HF |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Eko DUO | Diagnostic Test | Acquisition of a single-lead ECG via patients self-examine themselves twice a week for 12 months. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Descriptive analysis of trends and association of raw AI-ECG signals changes that correlate with HF progression. |
| Up to 18 months |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity and specificity of AI-ECG to predict HF progression |
| Up to 18 months |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Abdullah Alrumayh | Contact | +447412335336 | a.alrumayh21@imperial.ac.uk | |
| Nicholas Peters | Contact | +44 (0)20 7594 1880 | n.peters@imperial.ac.uk |
| Name | Affiliation | Role |
|---|---|---|
| Nicholas Peters | Imperial College London | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Imperial College London | Recruiting | London | United Kingdom |
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| ID | Term |
|---|---|
| D006333 | Heart Failure |
| ID | Term |
|---|---|
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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