Point of Care Artificial Intelligence Tool for Heart Failure Diagnosis

NCT04601415 | Completed FDA Device

• 1 other identifier: 285417
• Study Type: observational
• Target: 1,050
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Heart Failure

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (2)

GP Referrals

Patients with HF referred by GP to echo department

Diagnostic Test: ECG from handheld device

Echo patients

Non-selected patients attending echo department in hospital

Diagnostic Test: ECG from handheld device

Interventions

ECG from handheld device DIAGNOSTIC_TEST

Acquisition of a single-lead ECG at time of presentation to GP and at echo appointment

Echo patients; GP Referrals

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

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

You may not qualify if:

• Unable to give informed consent

Sponsors & Collaborators

• Imperial College London: lead

Study Sites (1)

Patrik Bachtiger

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

Location

Related Publications (2)

• 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.

  PMID: 31378392 BACKGROUND

• 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.

  PMID: 34998740 DERIVED

MeSH Terms

Conditions

Heart Failure

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 26, 2020
• First Posted: October 23, 2020
• Study Start: February 6, 2021
• Primary Completion: May 27, 2021
• Study Completion: May 27, 2021
• Last Updated: October 11, 2023

Record last verified: 2023-10

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)