The main objective of this study is to evaluate a machine learning model's ability to detect murmurs indicative of structural heart disease ("structural murmur") by analyzing phonocardiogram waveforms-and simultaneous electrocardiogram waveforms when available-in multiple auscultatory positions per subject. Diagnosis of structural murmur will be confirmed by gold-standard echocardiography and reviewed by an expert panel of cardiologists.
18+ years old
Patient or patient's legal healthcare proxy consents to participation
Documented history of SHD
Undergoing (or has undergone, within 30 days) a complete echocardiogram
Patient or proxy is unwilling/unable to give written informed consent
Unable to complete a complete echocardiogram, or none recent completed within the last 30 days
No documented history of SHD
Experiencing a known or suspected acute cardiac event
Potential research subjects will be screened for eligibility from cardiology clinics, echocardiogram clinics, and inpatient areas. In order to be eligible, a patient must be 18+ years of age, have a documented history of structural heart disease, and has undergone an echocardiogram within the last 30 days. If a patient meets all of the specified inclusion criteria, and none of the specified exclusion criteria, the patient or their proxy will be approached to discuss participation in the study.
Cohort
Prospective
Eko digital stethoscopes
DeviceUse of the Eko CORE 500 digital stethoscope and 3M Littmann CORE Digital Stethoscope to auscultate and record cardiac phonocardiogram and (when available) electrocardiogram waveforms, as well as heart sounds.
Primary outcome
Evaluate a machine learning model's ability to detect murmurs indicative of structural heart disease ("structural murmur") by analyzing phonocardiogram waveforms-and simultaneous electrocardiogram waveforms when available-in multiple auscultatory positions per subject. Diagnosis of structural murmur will be confirmed by gold-standard echocardiography and review by an expert panel of cardiologists.
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