Real-World Evaluation of Eko Algorithms in a Point of Care Setting

Study Overview
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Brief Summary
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The purpose of this research is to prospectively test and validate the utility of Eko artificial intelligence (AI) plus Eko Murmur Analysis Software (EMAS) murmur characterization in algorithm in a real world, point-of-care setting.

Detailed Description
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Eko has developed a platform to aid in screening for cardiac conditions using a digital stethoscope and machine-learning algorithms to detect the presence or absence of heart conditions such as heart murmurs and atrial fibrillation. In November 2019, the US Food and Drug Administration (FDA) granted Eko a 510(k) clearance for the marketing of Eko AI, a set of machine learning algorithms that includes atrial fibrillation (AF) and heart murmur detection. The detection of heart murmurs may aid in detecting occult and dangerous valvular heart disease (VHD). Other Eko AI outputs include bradycardia, tachycardia, noisy signal, QRS duration, and unclassified data. Eko AI has accuracy comparable to physician judgment (atrial fibrillation sensitivity of 98.9% and specificity of 96.9%, murmur sensitivity of 87.6% and specificity of 87.8%). Both AF and VHD can cause significant morbidity and mortality when missed or diagnosed late. Eko has further developed the murmur detection function of Eko AI to now not only identify whether a murmur is present, but also to inform the clinician of its timing during the cardiac cycle (systole vs diastole), and whether it is innocent or structural. We are calling this product the Eko Murmur Analysis Software (EMAS) and submitted a premarket notification to FDA in December 2021. This study sets out to understand the utility of the Eko AI plus EMAS murmur characterization algorithm in real world use. Collecting data in a point-of-care setting will demonstrate how accurately the algorithm characterizes murmurs in comparison to an AI-unassisted clinical examination. Algorithm output and clinical determination will be confirmed by echocardiographic ground truth, with the results being blinded until the end of the patient visit.

Official Title
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Real-World Evaluation of Eko Algorithms in a Point of Care Setting

Conditions
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Murmur, Heart, Innocent Murmurs, Heart Murmurs, Pathologic Murmur, Atrial Fibrillation

Intervention / Treatment
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* Device: Use of Eko CORE and Eko CORE 500 electronic stethoscope


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patient consents to participation Willing to have heart sounds recorded with an electronic stethoscope Willing to undergo echocardiography Willing to undergo a 12-lead electrocardiogram Adults aged 65 years and older History of at least one of the following: hypertension, BMI ≥ 30, diabetes mellitus, hyperlipidemia, atrial fibrillation, myocardial infarction, stroke/TIA, previous coronary surgery, or previous coronary angiography No prior diagnosis of valve disease or heart murmur Exclusion Criteria: Patient is unwilling or unable to give written informed consent Patients experiencing a known or suspected acute cardiac event Under the age of 65 years old Prior diagnosis of valve disease or heart murmurs

Ages Eligible for Study
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Minimum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Device: Use of Eko CORE and Eko CORE 500 electronic stethoscope|Auscultation of heart sounds using electronic stethoscopes|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensitivity and Specificity of Eko's AI relative to ground truth | Sensitivity and specificity of Eko's murmur detection algorithm relative to ground truth. Ground truth is defined as echocardiography-confirmed clinically-significant (graded mild-to-moderate or greater severity) VHD that is associated with a murmur, as confirmed by an expert panel. | 02/20/2022 - 05/20/2024 | 
| Sensitivity and Specificity of Eko's AI relative to PCP auscultation and ground truth | Sensitivity and specificity of Eko's murmur detection algorithm relative to ground truth and PCP auscultation ground truth. Ground truth is defined as echocardiography-confirmed clinically-significant (graded mild-to-moderate or greater severity) VHD that is associated with a murmur, as confirmed by an expert panel. | 02/20/2022 - 05/20/2024 | 
| Positive and negative predictive values for AI detecting new VHD | Positive and negative predictive values of Eko's algorithms for detecting new clinically significant valvular heart disease | 02/20/2022 - 05/20/2024 | 
| Positive and negative predictive values for PCP detecting new VHD | Positive and negative predictive values of a PCP's outpatient visit for detecting new clinically significant valvular heart disease | 02/20/2022 - 05/20/2024 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensitivity and Specificity of Eko's AI relative to echocardiographic ground truth. | Sensitivity and specificity of Eko's murmur detection algorithm relative to echocardiographic ground truth. | 02/20/2022 - 05/20/2024 | 
| Performance of the machine algorithm vs. the physician | Performance of the machine algorithm vs. the physician | 02/20/2022 - 05/20/2024 | 
| Number of cardiac tests and consultations ordered | Number of cardiac tests and consultations ordered | 02/20/2022 - 05/20/2024 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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artificial intelligence, machine learning