Abstract 13831: Handheld Wireless Digital Phonocardiography for Machine Learning-Based Detection of Mitral Regurgitation

BackgroundMitral regurgitation (MR) is a common disease which can be detected as a murmur on auscultation, but studies show that the majority of new primary care physicians do not detect MR murmurs which are confirmed by transthoracic echocardiography (TTE). The FDA-approved Eko CORE device is a digital stethoscope wirelessly paired with the Eko Mobile application to allow recording and analysis of phonocardiograms (PCG). These PCG data drive a machine learning-based detection algorithm to identify clinically significant MR, validated by TTE, as part of the ongoing Phono- and Electrocardiogram Assisted Detection of Valvular Disease (PEA-Valve) Study.MethodsPatients undergoing TTE at Northwestern Medicine underwent PCG recording by the Eko CORE device. Recordings 15 seconds long were obtained at four standard auscultation positions. A TensorFlow-based machine learning algorithm assessed the presence or absence of murmur with dominant localization to the cardiac apex indicating clinically significant MR, defined as moderate or greater on TTE (Figure 1).ResultsTo date, 234 patients with 626 recordings have been enrolled, with 32 patients (13.7%) found to have significant MR on TTE. The receiver-operating characteristic curve had an area of 0.764, yielding a sensitivity of 61.5% (95% CI, 42.9-80.0%) and a specificity of 86.3% (95% CI, 76.5-94.7%) for the detection of MR (Figure 2).ConclusionPCG assessment using the Eko CORE device and machine learning interpretation is a fast and effective method to screen for significant MR and should be validated in a primary care setting, which may lead to more appropriate referrals for echo.