Prospective Longitudinal Evaluation of AI-ECG in a NEwly Diagnosed Heart Failure ((PLANE-HF))

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.

Study Overview

• Status: Recruiting
• Conditions: Heart Failure
• Intervention / Treatment: Diagnostic test: Eko DUO

• Study Type: Observational
• Enrollment (Anticipated): 80

Contacts and Locations

• Study Contact: Name: Abdullah Alrumayh; Phone Number: +447412335336; Email: a.alrumayh21@imperial.ac.uk
• Study Contact Backup: Name: Nicholas Peters; Phone Number: +44 (0)20 7594 1880; Email: n.peters@imperial.ac.uk

Study Locations

• United Kingdom
  • London, United Kingdom
    • Recruiting
    • Imperial College London
    • Contact: Abdullah Alrumayh

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• 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.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
coronary HF aetiology; Patients with coronary HF	Diagnostic test: Eko DUO; Acquisition of a single-lead ECG via patients self-examine themselves twice a week for 12 months.
non-coronary HF aetiology; Patients with non-coronary HF	Diagnostic test: Eko DUO; Acquisition of a single-lead ECG via patients self-examine themselves twice a week for 12 months.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Descriptive analysis of trends and association of raw AI-ECG signals changes that correlate with HF progression.	AI-ECG signal changes with LV impairment.; AI-ECG signal changes with medication optimization.; AI-ECG signal changes with healthcare episodes.	Up to 18 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity and specificity of AI-ECG to predict HF progression	AI-ECG prediction of HF trajectory deterioration or improvement.; AI-ECG prediction of clinical congestive HF	Up to 18 months

Collaborators and Investigators

• Sponsor: Imperial College London
• Investigators: Principal Investigator: Nicholas Peters, Imperial College London

Study record dates

Study Major Dates

• Study Start (Actual): January 11, 2023
• Primary Completion (Anticipated): June 11, 2024
• Study Completion (Anticipated): June 11, 2024

Study Registration Dates

• First Submitted: April 4, 2023
• First Submitted That Met QC Criteria: April 4, 2023
• First Posted (Actual): April 18, 2023

Study Record Updates

• Last Update Posted (Actual): April 18, 2023
• Last Update Submitted That Met QC Criteria: April 4, 2023
• Last Verified: April 1, 2023

More Information

Terms related to this study

• Keywords: Heart failure; Remote monitoring; Machine learning
• Additional Relevant MeSH Terms: Heart Diseases; Cardiovascular Diseases; Heart Failure
• Other Study ID Numbers: 22HH7900

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: Yes