Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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.
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)
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| GP Referrals | Patients with HF referred by GP to echo department |
| |
| Echo patients | Non-selected patients attending echo department in hospital |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| ECG from handheld device | Diagnostic Test | Acquisition of a single-lead ECG at time of presentation to GP and at echo appointment |
|
| 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 |
Not provided
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Primary care population and patients referred to echocardiography department from in/outpatient settings
Not provided
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Patrik Bachtiger | London | Non-US/Non-Canadian | E5 0LJ | United Kingdom |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31378392 | Background | 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. | |
| 41684548 |
Not provided
Not provided
Identifiable data will be held within the clinical investigator team
Not provided
Not provided
Not provided
Not provided
| Type | Date | Date Unknown |
|---|---|---|
| Release | Nov 9, 2023 | |
| Reset | May 2, 2024 | |
| Release | Oct 23, 2024 | |
| Reset | Dec 6, 2024 | |
| Release | Sep 23, 2025 | |
| Reset | Oct 9, 2025 |
Not provided
| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Feb 4, 2021 | Oct 9, 2023 | Prot_000.pdf |
Not provided
Not provided
| Release Date | Unrelease Date | Unrelease Date Unknown | Reset Date | MCP Release Number |
|---|---|---|---|---|
| Nov 9, 2023 | May 2, 2024 | |||
| Oct 23, 2024 |
| ID | Term |
|---|---|
| D006333 | Heart Failure |
| ID | Term |
|---|---|
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
Not provided
Not provided
Not provided
Not provided
Not provided
| Derived |
| McDonald A, Gales M, Rana BS, Shun-Shin M, Lukban BF, Adrego R, Papachristidis A, Hajee F, Shapiro L, Wilson J, Prothero T, Kennedy A, Myerson S, Prendergast B, Bachtiger P, Kelshiker MA, Peters N, Steeds R, Agarwal A. Development and validation of AI-Enhanced auscultation for valvular heart disease screening through a multi-centre study. NPJ Cardiovasc Health. 2026 Feb 10;3:5. doi: 10.1038/s44325-026-00103-y. eCollection 2026. |
| 34998740 | Derived | 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. |
| Dec 6, 2024 |
| Sep 23, 2025 | Oct 9, 2025 |