Deep Learning Detection of Pulmonary Hypertension and Low Ejection Fraction Via Digital Stethoscope and 3-Lead ECG

Overview

This is a prospective, observational study evaluating whether heart sounds (phonocardiograms) and three-lead electrocardiograms (ECGs) recorded using the Eko CORE 500 digital stethoscope can help detect pulmonary hypertension (PH) and low left ventricular ejection fraction (EF ≤ 40%). PH is a condition characterized by high blood pressure in the pulmonary arteries, which can lead to heart failure and carries significant risks if undiagnosed. Low EF, which indicates reduced pumping ability of the heart, is also associated with increased risk of severe cardiac events but can remain undetected because patients often have no symptoms or only nonspecific symptoms. In this study, adults undergoing clinically indicated echocardiograms at outpatient sites will be invited to participate. Participants will complete a single study session lasting about 20 minutes, during which heart sounds and a three-lead ECG will be collected using the Eko CORE 500 device. If participants have had a clinical 12-lead ECG within 30 days of their echocardiogram, those data may also be used for analysis. The echocardiogram performed as part of routine care within seven days before or after the Eko CORE 500 recording will serve as the reference standard to confirm the presence or absence of PH and low EF. Up to 3,850 participants may be enrolled across multiple sites to ensure that approximately 3,500 complete the study. The data collected will be used to develop and validate artificial intelligence (AI) algorithms that aim to detect PH and identify low EF, potentially enabling earlier and simpler screening for these conditions in clinical practice.

Pulmonary hypertension (PH) and low left ventricular ejection fraction (EF) are significant cardiovascular conditions associated with increased morbidity and mortality but often remain underdiagnosed due to the need for specialized imaging such as echocardiography or invasive right heart catheterization. Early detection tools could enable timely intervention and improved patient outcomes. This prospective, observational study aims to determine whether acoustic heart sounds (phonocardiograms, PCG) and three-lead electrocardiograms (ECG) recorded with the Eko CORE 500 digital stethoscope can identify patients with PH or low EF (defined as EF ≤ 40%) when compared with echocardiographic findings as the reference standard. The study will enroll adult patients undergoing clinically indicated transthoracic echocardiography at outpatient sites. Participants will complete a single study visit, lasting approximately 20 minutes, during which heart sounds and three-lead ECG signals will be recorded at four standard auscultation sites (aortic, pulmonic, tricuspid, and mitral) while seated. Each recording lasts approximately 15 seconds. If a participant has undergone a 12-lead ECG within 30 days of their echocardiogram, de-identified ECG data will also be included for comparison purposes. Poor-quality recordings will be repeated once before moving to the next auscultation site. No results from the CORE 500 device or developed algorithms will be shared with participants or entered into the medical record. De-identified demographic data collected will include age, race/ethnicity, and sex. Clinical data will include past medical history, relevant laboratory results (such as BNP or NT-proBNP), electrocardiographic findings, and echocardiographic measurements including tricuspid regurgitant jet velocity, pulmonary artery pressures, chamber size, and left ventricular ejection fraction. Data will be analyzed by Eko Health, Inc. using machine learning techniques, including transformer-based models implemented in Python with PyTorch. Models will initially be pre-trained on unlabeled data and then fine-tuned on labeled data, optimizing performance using the Adam optimizer and binary cross-entropy loss. Algorithm performance will be evaluated based on sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and other diagnostic metrics. Confidence intervals for sensitivity and specificity will be calculated to assess the statistical reliability of results. Sample size calculations account for the estimated prevalence of each condition. For PH, an anticipated prevalence of 25-30% and target algorithm sensitivity and specificity exceeding 0.7 drive a required enrollment of approximately 2,400 participants to achieve statistical confidence. For low EF, assuming a prevalence of 10%, a minimum of 2,000 participants is required to adequately power analyses for sensitivity and specificity above 0.7. The primary endpoint of this study is to evaluate sensitivity and specificity for the PH and low EF detection algorithms, respectively. The secondary endpoint is to measure algorithm accuracy, area under the ROC curve, negative predictive value, and positive predictive value for detecting low EF. The study plans to enroll up to 3,850 participants across multiple sites to ensure sufficient evaluable data from approximately 3,500 participants. The intended outcome is to develop and validate AI-based tools that may facilitate non-invasive, point-of-care screening for PH and low EF using the Eko CORE 500 digital stethoscope, potentially reducing the burden of undiagnosed cardiovascular disease.