Point of Care Artificial Intelligence Tool for Heart Failure Diagnosis (DUO-EF-19)

Diagnosis of Heart Failure in the Post-COVID-19 Clinic, Primary Care and Hospital Setting Using a Digital Stethoscope With Artificial Intelligence (AI) Electrocardiogram (ECG)

Abbreviations/acronyms:

DUO-EF = prediction of ejection fraction (EF) using the Eko-DUO digital stethoscope algorithm HF = heart failure HFrEF = heart failure with reduced ejection fraction COVID-19 = coronavirus disease 2019 Eko DUO = digital stethoscope device cMRI = cardiac magnetic resonance imaging ECG = electrocardiogram

Prospective observational study of left ventricular ejection fraction predicted by application of artificial intelligence to single-lead ECG acquired by a digital stethoscope; in the post-covid-19 follow up clinic, in patients presenting with heart failure symptoms in primary care, and in patients attending for echocardiography and cardiac MRI.

Study Overview

• Status: Completed
• Conditions: Heart Failure
• Intervention / Treatment: Diagnostic test: ECG from handheld device

Detailed Description

Abbreviations/acronyms:

DUO-EF = prediction of ejection fraction (EF) using the Eko-DUO digital stethoscope algorithm HF = heart failure HFrEF = heart failure with reduced ejection fraction COVID-19 = coronavirus disease 2019 Eko DUO = digital stethoscope device cMRI = cardiac magnetic resonance imaging ECG = electrocardiogram

AIMS

To demonstrate DUO-EF can identify heart failure (HF) with reduced ejection fraction (HFrEF) post-COVID-19 where diagnosis would otherwise be missed/delayed To demonstrate DUO-EF can reliably and accurately diagnose new HFrEF in the primary care setting To further validate DUO-EF diagnostic performance at-scale against gold-standard investigations (echocardiography and cardia MRI) To measure if DUO-EF suggestive of HFrEF but with normal gold standard investigations predicts future risk of developing HFrEF

Methods To demonstrate DUO-EF can identify heart failure (HF) with reduced ejection fraction (HFrEF) post-COVID-19 where diagnosis would otherwise be missed/delayed To demonstrate DUO-EF can reliably and accurately diagnose new HFrEF in the primary care setting To further validate DUO-EF diagnostic performance at-scale against gold-standard investigations (echocardiography and cardiac magnetic resonance imaging - cMRI) To measure if DUO-EF suggestive of HFrEF but with normal gold standard investigations predicts future risk of developing HFrEF

DUO-EF prediction of ejection fraction in patients attending COVID-19 follow up clinic and comparison with:

subsequent DUO-EF at time of gold-standard investigation for HF ejection fraction as calculated by gold-standard investigation

DUO-EF prediction of ejection fraction in patients where their GP suspects new heart failure and comparison with:

subsequent DUO-EF at time of gold-standard investigation ejection fraction as calculated by gold-standard investigation DUO-EF prediction of ejection fraction in unselected patients attending for echocardiography or cardiac MRI, comparing DUO-EF predicted with gold-standard calculated ejection fraction Telephone call follow-up at 24 months for all patients with DUO-EF suggestive of HFrEF but normal gold standard investigations

OUTCOME MEASURES Area under curve (AUC) of DUO-EF calibrated for detection of EF below 40%; classification accuracy Positive predictive value of DUO-EF in COVID-19 clinic and GP context based on subsequent gold-standard estimation of EF; negative predictive value of DUO-EF in COVID-19 follow up cohort; positive predictive value of DUO-EF at 24 months in those with negative gold standard investigations Qualitative measurement of patient and clinical end user acceptability of Eko DUO

POPULATION Group 1: Patients seen in the COVID-19 follow-up clinic (n = 400) Group 2: Patients seen in primary care with symptoms newly suggestive of heart failure (n = 400) Group 3: All-comers to echocardiography departments across Imperial College Healthcare NHS Trust (n = 1,500) Group 4: patients undergoing cardiac MRI investigation (n = 100)

• Study Type: Observational
• Enrollment (Actual): 1050

Contacts and Locations

Study Locations

• United Kingdom
  • Non-US/Non-Canadian
    • London, Non-US/Non-Canadian, United Kingdom, E5 0LJ
      • Patrik Bachtiger

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Primary care population and patients referred to echocardiography department from in/outpatient settings

Description

Inclusion Criteria:

• Attendance at GP with ?HF symptoms
• Referral from GP or elsewhere for echocardiogram in hospital
• Age >18

Exclusion Criteria:

• Unable to give informed consent

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
GP Referrals; Patients with HF referred by GP to echo department	Diagnostic test: ECG from handheld device; Acquisition of a single-lead ECG at time of presentation to GP and at echo appointment
Echo patients; Non-selected patients attending echo department in hospital	Diagnostic test: ECG from handheld device; Acquisition of a single-lead ECG at time of presentation to GP and at echo appointment

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Area under receiver operating curve	Area under curve (AUC) where maximum value is '1', describing ability of algorithm to discriminate low from not-low ejection fraction	up to 18 months

Collaborators and Investigators

• Sponsor: Imperial College London

Publications and helpful links

General Publications

• Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019 Sep 7;394(10201):861-867. doi: 10.1016/S0140-6736(19)31721-0. Epub 2019 Aug 1.
• Bachtiger P, Petri CF, Scott FE, Ri Park S, Kelshiker MA, Sahemey HK, Dumea B, Alquero R, Padam PS, Hatrick IR, Ali A, Ribeiro M, Cheung WS, Bual N, Rana B, Shun-Shin M, Kramer DB, Fragoyannis A, Keene D, Plymen CM, Peters NS. Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study. Lancet Digit Health. 2022 Feb;4(2):e117-e125. doi: 10.1016/S2589-7500(21)00256-9. Epub 2022 Jan 5.

Study record dates

Study Major Dates

• Study Start (Actual): February 6, 2021
• Primary Completion (Actual): May 27, 2021
• Study Completion (Actual): May 27, 2021

Study Registration Dates

• First Submitted: August 26, 2020
• First Submitted That Met QC Criteria: October 19, 2020
• First Posted (Actual): October 23, 2020

Study Record Updates

• Last Update Posted (Actual): October 11, 2023
• Last Update Submitted That Met QC Criteria: October 9, 2023
• Last Verified: October 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Heart Diseases; Cardiovascular Diseases; Heart Failure
• Other Study ID Numbers: 285417

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Identifiable data will be held within the clinical investigator team

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