Clinical trial NCT04601415: Point of Care Artificial Intelligence Tool for Heart Failure Diagnosis
Official title: 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)
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Heart Failure
Lead sponsor: Imperial College London
Sponsor class: OTHER
First posted: 2020-10-23
Enrollment: 1050
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Primary care population and patients referred to echocardiography department from in/outpatient settings

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.

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)

Eligibility criteria: 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

Primary outcomes:
measure: Area under receiver operating curve
timeFrame: up to 18 months
Secondary outcomes:
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: Patrik Bachtiger
state: Non-US/Non-Canadian
Study officials:
References:
citation: 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.
citation: 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.