Deep Learning Detection of Pulmonary Hypertension and Low Ejection Fraction Via Digital Stethoscope and 3-Lead ECG
NCT ID: NCT07087613
Last Updated: 2025-07-28
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
3850 participants
OBSERVATIONAL
2025-06-15
2026-08-31
Brief Summary
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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.
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Detailed Description
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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.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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All Participants
Adults aged 18 years and older undergoing clinically indicated transthoracic echocardiography in an outpatient setting. Participants will have phonocardiogram (PCG) and 3-lead ECG recordings collected using the Eko CORE 500 digital stethoscope. Data will be used to develop and validate artificial intelligence algorithms to detect pulmonary hypertension and low left ventricular ejection fraction.
Eko CORE 500 Digital Stethoscope
The FDA-cleared Eko CORE 500 digital stethoscope is used to collect phonocardiogram (PCG) and three-lead ECG recordings from participants. This observational study uses these recordings to develop and validate artificial intelligence algorithms to detect pulmonary hypertension and low left ventricular ejection fraction. No modifications to the device or device functionality are being tested.
Interventions
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Eko CORE 500 Digital Stethoscope
The FDA-cleared Eko CORE 500 digital stethoscope is used to collect phonocardiogram (PCG) and three-lead ECG recordings from participants. This observational study uses these recordings to develop and validate artificial intelligence algorithms to detect pulmonary hypertension and low left ventricular ejection fraction. No modifications to the device or device functionality are being tested.
Eligibility Criteria
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Inclusion Criteria
* Able and willing to provide informed consent
* Completed a clinical echocardiogram within 7 days before or after study procedures
Exclusion Criteria
* Patients who are hospitalized
* Patients undergoing echocardiography with a limited echocardiogram
18 Years
ALL
No
Sponsors
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Eko Devices, Inc.
INDUSTRY
Responsible Party
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Principal Investigators
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Rose McDonough, MD
Role: STUDY_DIRECTOR
Senior Manager, Medical Affairs
Locations
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Prairie Cardiovascular
O'Fallon, Illinois, United States
Prairie Education & Research Cooperative
Springfield, Illinois, United States
St Johns Hospital, Springfield
Springfield, Illinois, United States
Countries
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Central Contacts
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Facility Contacts
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References
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Rees PJ, Hay JG, Webb JR. Premedication for fibreoptic bronchoscopy. Thorax. 1983 Aug;38(8):624-7. doi: 10.1136/thx.38.8.624.
Maron BA, Hess E, Maddox TM, Opotowsky AR, Tedford RJ, Lahm T, Joynt KE, Kass DJ, Stephens T, Stanislawski MA, Swenson ER, Goldstein RH, Leopold JA, Zamanian RT, Elwing JM, Plomondon ME, Grunwald GK, Baron AE, Rumsfeld JS, Choudhary G. Association of Borderline Pulmonary Hypertension With Mortality and Hospitalization in a Large Patient Cohort: Insights From the Veterans Affairs Clinical Assessment, Reporting, and Tracking Program. Circulation. 2016 Mar 29;133(13):1240-8. doi: 10.1161/CIRCULATIONAHA.115.020207. Epub 2016 Feb 12.
Assad TR, Maron BA, Robbins IM, Xu M, Huang S, Harrell FE, Farber-Eger EH, Wells QS, Choudhary G, Hemnes AR, Brittain EL. Prognostic Effect and Longitudinal Hemodynamic Assessment of Borderline Pulmonary Hypertension. JAMA Cardiol. 2017 Dec 1;2(12):1361-1368. doi: 10.1001/jamacardio.2017.3882.
Strange G, Stewart S, Celermajer DS, Prior D, Scalia GM, Marwick TH, Gabbay E, Ilton M, Joseph M, Codde J, Playford D; NEDA Contributing Sites. Threshold of Pulmonary Hypertension Associated With Increased Mortality. J Am Coll Cardiol. 2019 Jun 4;73(21):2660-2672. doi: 10.1016/j.jacc.2019.03.482.
Choudhary G, Jankowich M, Wu WC. Elevated pulmonary artery systolic pressure predicts heart failure admissions in African Americans: Jackson Heart Study. Circ Heart Fail. 2014 Jul;7(4):558-64. doi: 10.1161/CIRCHEARTFAILURE.114.001366. Epub 2014 Jun 5.
Maron BA, Choudhary G, Khan UA, Jankowich MD, McChesney H, Ferrazzani SJ, Gaddam S, Sharma S, Opotowsky AR, Bhatt DL, Rocco TP, Aragam JR. Clinical profile and underdiagnosis of pulmonary hypertension in US veteran patients. Circ Heart Fail. 2013 Sep 1;6(5):906-12. doi: 10.1161/CIRCHEARTFAILURE.112.000091. Epub 2013 Jun 27.
Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019 Jan;25(1):70-74. doi: 10.1038/s41591-018-0240-2. Epub 2019 Jan 7.
Guo L, Khobragade N, Kieu S, Ilyas S, Nicely PN, Asiedu EK, Lima FV, Currie C, Lastowski E, Choudhary G. Development and Evaluation of a Deep Learning-Based Pulmonary Hypertension Screening Algorithm Using a Digital Stethoscope. J Am Heart Assoc. 2025 Feb 4;14(3):e036882. doi: 10.1161/JAHA.124.036882. Epub 2025 Feb 3.
Colman R, Whittingham H, Tomlinson G, Granton J. Utility of the physical examination in detecting pulmonary hypertension. A mixed methods study. PLoS One. 2014 Oct 24;9(10):e108499. doi: 10.1371/journal.pone.0108499. eCollection 2014.
Guo L, Pressman GS, Kieu SN, Marrus SB, Mathew G, Prince J, Lastowski E, McDonough RV, Currie C, Tiwari U, Maidens JN, Al-Sudani H, Friend E, Padmanabhan D, Kumar P, Kersh E, Venkatraman S, Qamruddin S. Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope: A Large Cohort Validation. JACC Adv. 2025 Mar;4(3):101619. doi: 10.1016/j.jacadv.2025.101619. Epub 2025 Feb 20.
Marcus GM, Vessey J, Jordan MV, Huddleston M, McKeown B, Gerber IL, Foster E, Chatterjee K, McCulloch CE, Michaels AD. Relationship between accurate auscultation of a clinically useful third heart sound and level of experience. Arch Intern Med. 2006 Mar 27;166(6):617-22. doi: 10.1001/archinte.166.6.617.
Other Identifiers
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2025.2
Identifier Type: -
Identifier Source: org_study_id
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