Clinical trial NCT05817136: Prospective Longitudinal Evaluation of AI-ECG in a NEwly Diagnosed Heart Failure
Official title: Prospective Longitudinal Evaluation of AI-ECG in a NEwly Diagnosed Heart Failure
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Heart Failure
Keywords: Heart failure
Remote monitoring
Machine learning
Lead sponsor: Imperial College London
Sponsor class: OTHER
First posted: 2023-04-18
Enrollment: 80
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: * Group 1: Newly diagnosed HFrEF patient with coronary causes.
* Group 2: Newly diagnosed HFrEF patient with non-coronary causes.

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.


Eligibility criteria: 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

Primary outcomes:
measure: Descriptive analysis of trends and association of raw AI-ECG signals changes that correlate with HF progression.
timeFrame: Up to 18 months
Secondary outcomes:
measure: Sensitivity and specificity of AI-ECG to predict HF progression
timeFrame: Up to 18 months
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: Imperial College London
Study officials:
affiliation: Imperial College London
name: Nicholas Peters
role: PRINCIPAL_INVESTIGATOR
References: