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
Conditions
Intervention / Treatment
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
Enrollment (Actual)
Contacts and Locations
Study Locations
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Non-US/Non-Canadian
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London, Non-US/Non-Canadian, United Kingdom, E5 0LJ
- Patrik Bachtiger
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
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
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
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GP Referrals
Patients with HF referred by GP to echo department
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Acquisition of a single-lead ECG at time of presentation to GP and at echo appointment
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Echo patients
Non-selected patients attending echo department in hospital
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Acquisition of a single-lead ECG at time of presentation to GP and at echo appointment
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Area under receiver operating curve
Time Frame: up to 18 months
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Area under curve (AUC) where maximum value is '1', describing ability of algorithm to discriminate low from not-low ejection fraction
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up to 18 months
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Collaborators and Investigators
Sponsor
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)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 285417
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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