[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"trial:NCT06699056":3,"trial-entities:NCT06699056":291,"trial-summary:NCT06699056":295},{"id":4,"nct_id":4,"org_study_id":5,"brief_title":6,"official_title":7,"overall_status":8,"completion_date":9,"status_verified_date":10,"last_update_date":11,"start_date":12,"sponsor_name":13,"lead_sponsor_class":14,"has_dmc":15,"brief_summary":16,"detailed_description":17,"conditions":18,"keywords":34,"study_type":49,"primary_purpose":50,"phases":51,"enrollment_info":52,"interventions":55,"primary_outcomes":62,"secondary_outcomes":67,"sex":71,"minimum_age":72,"maximum_age":50,"healthy_volunteers":15,"eligibility_criteria":73,"std_ages":82,"locations":85,"central_contacts":234,"overall_officials":241,"references":255,"see_also_links":290},"NCT06699056","PBH-COREFS-1-A","AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor","AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor","RECRUITING","2027-11-15","2026-05","2026-06-01","2024-11-21","Peerbridge Health, Inc","INDUSTRY",true,"This prospective, multicenter, cluster-randomized controlled study aims to evaluate the accuracy of an investigational artificial intelligence (AI) Software as a Medical Device (SaMD) designed to compute ejection fraction (EF) severity categories based on the American Society of Echocardiography's (ASE) 4-category scale. The software analyzes continuous ECG waveform data acquired by the FDA-cleared Peerbridge COR® ECG Wearable Monitor, an ambulatory patch device designed for use during daily activities. The AI software assists clinicians in cardiac evaluations by estimating EF severity, which reflects how well the heart pumps blood.\n\nIn this study, EF severity determination will be made using 5-minute ECG recordings collected during a 15-minute resting period with participants seated upright. The results will be compared to EF severity obtained from an FDA-cleared, non-contrast transthoracic echocardiogram (TTE) predicate device. This comparison aims to validate the accuracy of the AI software.","Objective This prospective study benchmarks the accuracy of CorEFS AI software in estimating ejection fraction (EF) severity categories using continuous ECG waveforms from the FDA-cleared Peerbridge Cor® ECG device, calibrated to the American Society of Echocardiography (ASE) scale.\n\nBackground Heart failure (HF) remains a significant public health issue, particularly in older adults (75+), with high morbidity and mortality rates. Half of HF cases involve reduced EF (HFrEF), a condition associated with a 75% five-year mortality rate. Despite advancements in HF management, accessible, low-cost EF monitoring is lacking.\n\nEchocardiography (Echo) is the gold standard for EF measurement but is limited in ambulatory and home settings. Continuous ECG wearables like the Peerbridge Cor® offer a promising alternative, providing high diagnostic yield, low wear burden, and real-time EF estimation. Previous studies (References 1-11) demonstrate the potential of AI-enabled ECG analysis in EF prediction, with accuracies up to 91.4% and AUCs of 0.94 in estimating EF severity.\n\nSuccessful demonstration of the proposed endpoints to clinically acceptable statistical thresholds will provide a new and alternative capability for EF severity assessments compared to ultrasound, MRI, and other imaging modalities where access is limited.\n\nHypothesis Specific ECG changes may identify left ventricular dysfunction (LVSD) and predict EF severity, enabling low-burden, cost-effective EF monitoring in high-risk populations.\n\nStudy Design\n\nParticipant Enrollment and Setup\n\nParticipants will receive the Peerbridge Cor® wearable, with data collection occurring through:\n\nIn-clinic setup: Study staff apply and initiate device use. Patient Home Setup (PHS): Telehealth guidance for independent device application (20% of participants).\n\nSubprotocols\n\nA: 30 minutes of Cor® ECG recording; 15 minutes analyzed. B: Up to 7 days of Cor® device use with periodic 15-minute sitting sessions. EF Reference Standard EF severity will be determined via FDA-cleared transthoracic echocardiography (TTE), using the Simpson's Bi-Plane Method.\n\nData Collection\n\nPeerbridge Cor® ECG Data: 30 minutes recorded; 15 minutes analyzed in 5-minute segments.\n\nEcho Study: Conducted before or during Cor® recording. 12-Lead ECG: Simultaneous recording with the Cor® device. Participants log sessions using the Cor® device's Event button. De-identified medical histories will support subgroup analyses.\n\nEndpoints Agreement between Cor® ECG-derived EF severity and Echo results will be assessed across ASE-defined categories (Normal, Mild, Moderate, Severe). Positive predictive value (PPV) adjusted for prevalence will be calculated.\n\nThis streamlined protocol validates CorEFS software for reliable, cost-effective EF monitoring and clinical decision support.",[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"Ventricular Ejection Fraction","LVF","LV Dysfunction","Atrial Enlargement","Conduction Defect","Heart Failure","Valvular Heart Disease","Ischemic Heart Disease","Cardiotoxicity","Myocardial Infarction","Dilated Cardiomyopathy","HFrEF - Heart Failure With Reduced Ejection Fraction","HFpEF - Heart Failure With Preserved Ejection Fraction","Syncope","Remodeling, Cardiac",[35,36,37,38,39,40,41,21,42,43,44,45,46,47,22,48],"ECG Patch","LVEF","Holter","ECG Wearable","COR","Atrial Conduction","ECG Biomarker","SaMD","Clinical Decision Support","ECG","EF Severity","Ejection Fraction","AI","Electrical Remodeling","OBSERVATIONAL",null,[],{"count":53,"type":54},2000,"ESTIMATED",[56],{"type":57,"name":58,"description":59,"armGroupLabels":60},"DEVICE","15-minutes of sitting during COR ECG Acquistion","Participants will follow a standardized protocol during a 15-minute seated session using the Peerbridge COR™ device. Participants will sit comfortably in an upright chair with a straight back; armrests are optional. Their feet must remain flat on the floor with legs uncrossed to ensure unobstructed blood flow and a stable posture. Arms should be relaxed and placed in their lap, on a flat surface (e.g., table), or on the armrest, ensuring they are not tensed or elevated. Participants will maintain a straight back with relaxed shoulders throughout the session.\n\nTo begin, participants will press the Event Button on the Peerbridge COR™ mobile device, marking the start of the session. They will remain seated in this position for 15 minutes. At the end of the session, participants will press the Event Button again to mark the conclusion of the seated event. This protocol ensures consistent data collection across all participants.",[61],"Cohort Breakdown to Power Accuracy Assessments",[63],{"measure":64,"description":65,"timeFrame":66},"Agreement of CorEFS Software EF Severity Categories Using Peerbridge COR™ ECG Data with ASE EF Severity Categories Established by Ultrasound Echocardiography","The primary endpoint of this trial is to demonstrate substantial agreement between EF severity categories determined by the CorEFS Software using 5 minutes of Peerbridge COR™ ECG data and the subject's EF severity category established through ultrasound echocardiography, the gold standard for EF classification. The study includes four co-primary endpoints, representing agreement measures within each of the four EF severity categories defined by the American Society of Echocardiography (ASE) Scale (Normal, Mildly Abnormal, Moderately Abnormal, Severely Abnormal). For each category the endpoint is the proportion of participants correctly classified by the test device relative to the reference standard. The goal is to demonstrate at least 80% agreement within each EF severity category.","Through study completion, average of 9 months.",[68],{"measure":69,"description":70,"timeFrame":66},"Confirmation of ≥80% Agreement Between Peerbridge Cor™ ECG Data and Reference Standard ECHO in EF Severity Categorization Using 15-Minute Continuous Monitoring: Secondary Endpoint Analysis","The secondary endpoint is to confirm at least 80% agreement between the proportion of all participants, correctly categorized in all 4 EF Severity Categories by analyzing 15-minutes of continuous Peerbridge Cor™ ECG device data compared to those that are categorized by the Reference Standard ECHO. This will be tested with a one-sided single-sample z-test at a 97.5% confidence level to see if agreement exceeds 80%, thereby rejecting the null hypothesis of ≤80% agreement in favor of significant concordance.","ALL","18 Years",{"inclusion":74,"exclusion":77,"raw_text":81},[75,76],"Age ≥ 18 years","Able and eligible to wear a Holter monitor",[78,79,80],"Receiving mechanical respiratory or circulatory support, or renal support therapy, at the time of screening or during Visit #1","Any condition that, in the investigator's opinion, could interfere with compliance with the study protocol or pose a safety risk to the participant","History of poor tolerance or severe skin reactions to ECG adhesive materials","Inclusion Criteria:\n\n* Age ≥ 18 years\n* Able and eligible to wear a Holter monitor\n\nExclusion Criteria:\n\n* Receiving mechanical respiratory or circulatory support, or renal support therapy, at the time of screening or during Visit #1\n* Any condition that, in the investigator's opinion, could interfere with compliance with the study protocol or pose a safety risk to the participant\n* History of poor tolerance or severe skin reactions to ECG adhesive materials",[83,84],"ADULT","OLDER_ADULT",[86,103,122,141,163,181,200,217],{"facility":87,"status":8,"city":88,"state":89,"zip":90,"country":91,"contacts":92,"geoPoint":100},"Orange County Heart Institute","Orange","California","92868","United States",[93,98],{"name":94,"role":95,"phone":96,"email":97},"Brian Kolski, MD","CONTACT","(714) 564-3300","bkolskimd@gmail.com",{"name":94,"role":99},"PRINCIPAL_INVESTIGATOR",{"lat":101,"lon":102},33.78779,-117.85311,{"facility":104,"status":8,"city":105,"state":89,"zip":106,"country":91,"contacts":107,"geoPoint":119},"Peerbridge Health","Pasadena","91107",[108,112,115,116],{"name":109,"role":95,"phone":110,"email":111},"Sandeep Gulati, PhD","877-960-0332","s.gulati@peerbridgehealth.com",{"name":113,"role":95,"email":114},"Lauren Pickard, MS","l.pickard@peerbridgehealth.com",{"name":109,"role":99},{"name":117,"role":118},"Angelo Acquista, MD","SUB_INVESTIGATOR",{"lat":120,"lon":121},34.14778,-118.14452,{"facility":123,"status":8,"city":124,"state":125,"zip":126,"country":91,"contacts":127,"geoPoint":138},"Henry Ford Hospital","Detroit","Michigan","48202",[128,132,136],{"name":129,"role":95,"phone":130,"email":131},"Sacchin Parikh, MD","313-916-2721","Sparikh2@hfhs.org",{"name":133,"role":95,"phoneExt":134,"email":135},"Meghan McCarthy","313-916-9419","mmccart8@hfhs.org",{"name":137,"role":99},"Sachin Parikh, MD",{"lat":139,"lon":140},42.33143,-83.04575,{"facility":142,"status":8,"city":143,"state":144,"zip":145,"country":91,"contacts":146,"geoPoint":160},"Hackensack University Medical Center","Hackensack","New Jersey","07601",[147,151,155,157,159],{"name":148,"role":95,"phone":149,"email":150},"Sameer Jamal, MD","551-996-5870","sameer.jamal@hmhn.org",{"name":152,"role":95,"phone":153,"email":154},"Manuel Castillo, RN","5519962136","Manuel.Castillo@hmhn.org",{"name":156,"role":118},"David Landers, MD",{"name":158,"role":118},"Mody Kanika, MD",{"name":148,"role":99},{"lat":161,"lon":162},40.88593,-74.04347,{"facility":164,"status":8,"city":165,"state":165,"zip":166,"country":91,"contacts":167,"geoPoint":178},"Mount Sinai Hospital","New York","10019",[168,172,176],{"name":169,"role":95,"phone":170,"email":171},"Joslin J Plathottam, MBBS, MPH","631-750-6345","JoslinJose.Plathottam@mountsinai.org",{"name":173,"role":95,"phone":174,"email":175},"Jeffrey Bander, MD, FACC","212-381-0918","Jeffrey.bander@mountsinai.org",{"name":177,"role":99},"Johanna Contreras, MD",{"lat":179,"lon":180},40.71427,-74.00597,{"facility":182,"status":8,"city":183,"state":184,"zip":185,"country":91,"contacts":186,"geoPoint":197},"Moses H. Cone Memorial Hospital","Greensboro","North Carolina","27401",[187,191,195],{"name":188,"role":95,"phone":189,"email":190},"Jennifer Knapp","336-832-3795","Jennifer.knapp@conehealth.com",{"name":192,"role":95,"phone":193,"email":194},"Kimberly Lutterloh","336-832-3748","Kimberly.lutterloh@conehealth.com",{"name":196,"role":99},"Daniel Bensimhon, MD",{"lat":198,"lon":199},36.07264,-79.79198,{"facility":201,"status":8,"city":202,"state":203,"zip":204,"country":91,"contacts":205,"geoPoint":214},"Texas Cardiac Arrhythmia Research Foundation","Austin","Texas","78705",[206,210,213],{"name":207,"role":95,"phone":208,"email":209},"Andrea Natale, MD","512-807-3150","tcarfan@gmail.com",{"name":211,"role":95,"email":212},"Deb Cardinal","dscardinal@austinheartbeat.com",{"name":207,"role":99},{"lat":215,"lon":216},30.26715,-97.74306,{"facility":218,"status":8,"city":219,"state":203,"zip":220,"country":91,"contacts":221,"geoPoint":231},"South Heart Clinic","Weslaco","78596",[222,226,230],{"name":223,"role":95,"phone":224,"email":225},"Frank Mazzola, MD","877-426-7457","frank.mazzola@southheartclinic.org",{"name":227,"role":95,"phone":228,"email":229},"Nathalie Guajardo","9564285522","Nathalie.guajardo@southheartclinic.org",{"name":223,"role":118},{"lat":232,"lon":233},26.15952,-97.99084,[235,237],{"name":109,"role":95,"phone":236,"email":111},"8182162958",{"name":238,"role":95,"phone":239,"email":240},"Chris Darland, MBA","814-572-7138","c.darland@peerbridgehealth.com",[242,243,246,247,248,249,250,251,253],{"name":207,"affiliation":201,"role":99},{"name":244,"affiliation":245,"role":99},"Johanna P Contreras, MD","MOUNT SINAI HOSPITAL",{"name":137,"affiliation":123,"role":99},{"name":94,"affiliation":87,"role":99},{"name":196,"affiliation":182,"role":99},{"name":109,"affiliation":13,"role":99},{"name":223,"affiliation":218,"role":99},{"name":148,"affiliation":252,"role":99},"Hackensack Meridian Health",{"name":207,"affiliation":254,"role":99},"HCA Los Robles Hospital & Medical Center",[256,260,263,266,269,272,275,278,281,284,287],{"pmid":257,"type":258,"citation":259},"22416086","BACKGROUND","Murtagh G, Dawkins IR, O'Connell R, Badabhagni M, Patel A, Tallon E, O'Hanlon R, Ledwidge MT, McDonald KM. Screening to prevent heart failure (STOP-HF): expanding the focus beyond asymptomatic left ventricular systolic dysfunction. Eur J Heart Fail. 2012 May;14(5):480-6. doi: 10.1093\u002Feurjhf\u002Fhfs030. Epub 2012 Mar 13.",{"pmid":261,"type":258,"citation":262},"25559473","Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T, Lancellotti P, Muraru D, Picard MH, Rietzschel ER, Rudski L, Spencer KT, Tsang W, Voigt JU. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2015 Jan;28(1):1-39.e14. doi: 10.1016\u002Fj.echo.2014.10.003.",{"pmid":264,"type":258,"citation":265},"32554161","Alhamaydeh M, Gregg R, Ahmad A, Faramand Z, Saba S, Al-Zaiti S. Identifying the most important ECG predictors of reduced ejection fraction in patients with suspected acute coronary syndrome. J Electrocardiol. 2020 Jul-Aug;61:81-85. doi: 10.1016\u002Fj.jelectrocard.2020.06.003. Epub 2020 Jun 5.",{"pmid":267,"type":258,"citation":268},"28546456","O'Neal WT, Mazur M, Bertoni AG, Bluemke DA, Al-Mallah MH, Lima JAC, Kitzman D, Soliman EZ. Electrocardiographic Predictors of Heart Failure With Reduced Versus Preserved Ejection Fraction: The Multi-Ethnic Study of Atherosclerosis. J Am Heart Assoc. 2017 May 25;6(6):e006023. doi: 10.1161\u002FJAHA.117.006023.",{"pmid":270,"type":258,"citation":271},"35330455","Chen HY, Lin CS, Fang WH, Lou YS, Cheng CC, Lee CC, Lin C. Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis. J Pers Med. 2022 Mar 13;12(3):455. doi: 10.3390\u002Fjpm12030455.",{"pmid":273,"type":258,"citation":274},"32986471","Adedinsewo D, Carter RE, Attia Z, Johnson P, Kashou AH, Dugan JL, Albus M, Sheele JM, Bellolio F, Friedman PA, Lopez-Jimenez F, Noseworthy PA. Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. Circ Arrhythm Electrophysiol. 2020 Aug;13(8):e008437. doi: 10.1161\u002FCIRCEP.120.008437. Epub 2020 Aug 4.",{"pmid":276,"type":258,"citation":277},"33958795","Yao X, Rushlow DR, Inselman JW, McCoy RG, Thacher TD, Behnken EM, Bernard ME, Rosas SL, Akfaly A, Misra A, Molling PE, Krien JS, Foss RM, Barry BA, Siontis KC, Kapa S, Pellikka PA, Lopez-Jimenez F, Attia ZI, Shah ND, Friedman PA, Noseworthy PA. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021 May;27(5):815-819. doi: 10.1038\u002Fs41591-021-01335-4. Epub 2021 May 6.",{"pmid":279,"type":258,"citation":280},"37489538","Sangha V, Nargesi AA, Dhingra LS, Khunte A, Mortazavi BJ, Ribeiro AH, Banina E, Adeola O, Garg N, Brandt CA, Miller EJ, Ribeiro ALP, Velazquez EJ, Giatti L, Barreto SM, Foppa M, Yuan N, Ouyang D, Krumholz HM, Khera R. Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images. Circulation. 2023 Aug 29;148(9):765-777. doi: 10.1161\u002FCIRCULATIONAHA.122.062646. Epub 2023 Jul 25.",{"pmid":282,"type":258,"citation":283},"34998740","Bachtiger P, Petri CF, Scott FE, Ri Park S, Kelshiker MA, Sahemey HK, Dumea B, Alquero R, Padam PS, Hatrick IR, Ali A, Ribeiro M, Cheung WS, Bual N, Rana B, Shun-Shin M, Kramer DB, Fragoyannis A, Keene D, Plymen CM, Peters NS. Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study. Lancet Digit Health. 2022 Feb;4(2):e117-e125. doi: 10.1016\u002FS2589-7500(21)00256-9. Epub 2022 Jan 5.",{"pmid":285,"type":258,"citation":286},"38739639","Al Younis SM, Hadjileontiadis LJ, Khandoker AH, Stefanini C, Soulaidopoulos S, Arsenos P, Doundoulakis I, Gatzoulis KA, Tsioufis K. Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning. PLoS One. 2024 May 13;19(5):e0302639. doi: 10.1371\u002Fjournal.pone.0302639. eCollection 2024.",{"pmid":288,"type":258,"citation":289},"35904538","Garcia-Escobar A, Vera-Vera S, Jurado-Roman A, Jimenez-Valero S, Galeote G, Moreno R. Subtle QRS changes are associated with reduced ejection fraction, diastolic dysfunction, and heart failure development and therapy responsiveness: Applications for artificial intelligence to ECG. Ann Noninvasive Electrocardiol. 2022 Nov;27(6):e12998. doi: 10.1111\u002Fanec.12998. Epub 2022 Jul 29.",[],{"nct_id":4,"conditions":292,"biomarkers":294},[29,24,26,28,32,293],"Valvular Heart Disorder",[],{"nct_id":4,"found":15,"summary":296,"prompt_version":306},{"design":297,"status":298,"heading":299,"summary":300,"follow_up":301,"word_count":302,"commitments":303,"compensation":304,"drugs_mentioned":305},"This is an observational study involving 2000 participants. It aims to compare the AI software's results with standard ultrasound measurements.","completed","AI-Enabled Ejection Fraction Assessment Using COR ECG Wearable Monitor","This study is looking at a new way to measure how well your heart pumps blood, called ejection fraction (EF). It uses an artificial intelligence (AI) software with the Peerbridge COR™ ECG Wearable Monitor, a device you wear. The goal is to see if this AI software can accurately tell the severity of your heart's pumping ability, similar to how an ultrasound (echocardiography) does. This could offer an easier, more accessible way to check heart function. We are looking for 2000 participants aged 18 or older who are able to wear a Holter monitor. The study will measure the agreement between the AI software's results and ultrasound results over about 9 months.","The primary measurement will be taken through study completion, which is an average of 9 months.",112,"You would wear the Peerbridge COR™ device and sit for 15 minutes during the ECG acquisition. The study will last for an average of 9 months.","Not stated in the trial record.",[],"v2"]