Ignite Creation Date:
2025-12-24 @ 11:20 PM
Ignite Modification Date:
2025-12-25 @ 9:02 PM
Study NCT ID:
NCT06699056
Status:
RECRUITING
Last Update Posted:
2025-02-11
First Post:
2024-11-15
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D018487', 'term': 'Ventricular Dysfunction, Left'}, {'id': 'D001145', 'term': 'Arrhythmias, Cardiac'}, {'id': 'D006333', 'term': 'Heart Failure'}, {'id': 'D006349', 'term': 'Heart Valve Diseases'}, {'id': 'D017202', 'term': 'Myocardial Ischemia'}, {'id': 'D066126', 'term': 'Cardiotoxicity'}, {'id': 'D009203', 'term': 'Myocardial Infarction'}, {'id': 'D002311', 'term': 'Cardiomyopathy, Dilated'}, {'id': 'D013575', 'term': 'Syncope'}, {'id': 'D064752', 'term': 'Atrial Remodeling'}], 'ancestors': [{'id': 'D018754', 'term': 'Ventricular Dysfunction'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D014652', 'term': 'Vascular Diseases'}, {'id': 'D064420', 'term': 'Drug-Related Side Effects and Adverse Reactions'}, {'id': 'D064419', 'term': 'Chemically-Induced Disorders'}, {'id': 'D011832', 'term': 'Radiation Injuries'}, {'id': 'D014947', 'term': 'Wounds and Injuries'}, {'id': 'D007238', 'term': 'Infarction'}, {'id': 'D007511', 'term': 'Ischemia'}, {'id': 'D009336', 'term': 'Necrosis'}, {'id': 'D006332', 'term': 'Cardiomegaly'}, {'id': 'D009202', 'term': 'Cardiomyopathies'}, {'id': 'D000083083', 'term': 'Laminopathies'}, {'id': 'D030342', 'term': 'Genetic Diseases, Inborn'}, {'id': 'D009358', 'term': 'Congenital, Hereditary, and Neonatal Diseases and Abnormalities'}, {'id': 'D014474', 'term': 'Unconsciousness'}, {'id': 'D003244', 'term': 'Consciousness Disorders'}, {'id': 'D019954', 'term': 'Neurobehavioral Manifestations'}, {'id': 'D009461', 'term': 'Neurologic Manifestations'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D020763', 'term': 'Pathological Conditions, Anatomical'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-11-21', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-02', 'completionDateStruct': {'date': '2025-09', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-02-09', 'studyFirstSubmitDate': '2024-11-15', 'studyFirstSubmitQcDate': '2024-11-18', 'lastUpdatePostDateStruct': {'date': '2025-02-11', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-11-21', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-09', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Substantial Equivalence of Peerbridge Cor™ ECG-Derived EF Severity Results Across 2-Minute, 5-Minute, and 15-Minute Time Windows Compared to Reference Standard ECHO', 'timeFrame': 'Through study completion, average of 9 months.', 'description': 'Analysis will be conducted to assess for statistical agreement between the proportion of all subjects, correctly categorized in all 4 EF Severity Categories by analyzing 2-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.'}], 'primaryOutcomes': [{'measure': 'Agreement of CorEFS Software EF Severity Categories Using Peerbridge COR™ ECG Data with ASE EF Severity Categories Established by Ultrasound Echocardiography', 'timeFrame': 'Through study completion, average of 9 months.', 'description': "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."}], 'secondaryOutcomes': [{'measure': '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', 'timeFrame': 'Through study completion, average of 9 months.', 'description': '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.'}]}, 'oversightModule': {'isUsExport': True, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isUnapprovedDevice': True, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'keywords': ['ECG Patch', 'LVEF', 'Holter', 'ECG Wearable', 'COR', 'Atrial Conduction', 'ECG Biomarker', 'LV Dysfunction', 'SaMD', 'Clinical Decision Support', 'ECG', 'EF Severity', 'Ejection Fraction', 'AI', 'Atrial Enlargement', 'Electrical Remodeling'], 'conditions': ['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']}, 'referencesModule': {'references': [{'pmid': '22416086', 'type': 'BACKGROUND', 'citation': "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/eurjhf/hfs030. Epub 2012 Mar 13."}, {'pmid': '25559473', 'type': 'BACKGROUND', 'citation': '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/j.echo.2014.10.003.'}, {'pmid': '32554161', 'type': 'BACKGROUND', 'citation': '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/j.jelectrocard.2020.06.003. Epub 2020 Jun 5.'}, {'pmid': '28546456', 'type': 'BACKGROUND', 'citation': "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/JAHA.117.006023."}, {'pmid': '35330455', 'type': 'BACKGROUND', 'citation': '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/jpm12030455.'}, {'pmid': '32986471', 'type': 'BACKGROUND', 'citation': '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/CIRCEP.120.008437. Epub 2020 Aug 4.'}, {'pmid': '33958795', 'type': 'BACKGROUND', 'citation': '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/s41591-021-01335-4. Epub 2021 May 6.'}, {'pmid': '37489538', 'type': 'BACKGROUND', 'citation': '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/CIRCULATIONAHA.122.062646. Epub 2023 Jul 25.'}, {'pmid': '34998740', 'type': 'BACKGROUND', 'citation': '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/S2589-7500(21)00256-9. Epub 2022 Jan 5.'}, {'pmid': '38739639', 'type': 'BACKGROUND', 'citation': '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/journal.pone.0302639. eCollection 2024.'}, {'pmid': '35904538', 'type': 'BACKGROUND', 'citation': '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/anec.12998. Epub 2022 Jul 29.'}]}, 'descriptionModule': {'briefSummary': "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.", 'detailedDescription': "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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Participants will be recruited from the community population receiving treatments or affiliated with participating trial sites. These sites will include hospitals, cardiac/medical/academic research centers, and medical practice clinics. It is anticipated that 6 to 15 trial sites distributed across the United States will participate in the study.\n\nEnrollment will aim to include participants across all four ASE Ejection Fraction Severity Categories to ensure sufficient statistical power for accuracy assessment. The study population will comprise both male and female participants, as ASE Ejection Fraction Severity category thresholds differ by gender.', 'healthyVolunteers': True, 'eligibilityCriteria': "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"}, 'identificationModule': {'nctId': 'NCT06699056', 'acronym': 'EFACT', 'briefTitle': 'AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor', 'organization': {'class': 'INDUSTRY', 'fullName': 'Peerbridge Health, Inc'}, 'officialTitle': 'AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor', 'orgStudyIdInfo': {'id': 'PBH-COREFS-1-A'}, 'secondaryIdInfos': [{'id': 'PBH-COREFS-1-A', 'type': 'OTHER', 'domain': 'Peerbridge Health'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'Cohort Breakdown to Power Accuracy Assessments', 'description': 'The study will enroll up to 1,500 participants across Subprotocol A and B, with a predictive total cohort of at least 660 unique participants. Each participant must provide at least one valid paired data point, defined as ECHO results paired with at least 30 minutes of Peerbridge COR™ ECG data, acquired concurrently or within 60 minutes of ECHO completion. Enrollment will occur at a minimum of 3 trial sites, with data collection ensuring at least 165 valid paired points per EF Severity category, as determined by the reference ECHO, from different participants.\n\nA paired data point is considered invalid if all 5-minute sitting windows during a 15-minute session yield "Not Analyzable" outputs. Participants who do not comply with the protocol or do not yield valid paired data points will be excluded from analysis and study statistics. Trial site investigators may use institutional EMR databases to identify, qualify, and recruit participants from their community patient populations.', 'interventionNames': ['Device: 15-minutes of sitting during COR ECG Acquistion']}], 'interventions': [{'name': '15-minutes of sitting during COR ECG Acquistion', 'type': 'DEVICE', 'description': '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.', 'armGroupLabels': ['Cohort Breakdown to Power Accuracy Assessments']}]}, 'contactsLocationsModule': {'locations': [{'zip': '92868', 'city': 'Orange', 'state': 'California', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Brian Kolski, MD', 'role': 'CONTACT', 'email': 'bkolskimd@gmail.com', 'phone': '(714) 564-3300'}, {'name': 'Karen A Cruz', 'role': 'CONTACT', 'email': 'karen.cruz@damg.site', 'phone': '714-639-1815', 'phoneExt': '107'}, {'name': 'Brian Kolski, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Orange County Heart Institute', 'geoPoint': {'lat': 33.78779, 'lon': -117.85311}}, {'zip': '32935', 'city': 'Melbourne', 'state': 'Florida', 'status': 'NOT_YET_RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Dale Dubois', 'role': 'CONTACT', 'email': 'd.dubois@peerbridgehealth.com', 'phone': '877-960-0332'}, {'name': 'Danielle Barrett', 'role': 'CONTACT', 'email': 'd.barrett@peerbridgehealth.com', 'phone': '877-960-0332'}, {'name': 'Sandeep Gulati, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Angelo Acquista, MD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Peerbridge Health', 'geoPoint': {'lat': 28.08363, 'lon': -80.60811}}, {'zip': '48202', 'city': 'Detroit', 'state': 'Michigan', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Sacchin Parikh, MD', 'role': 'CONTACT', 'email': 'Sparikh2@hfhs.org', 'phone': '313-916-2721'}, {'name': 'Meghan McCarthy', 'role': 'CONTACT', 'email': 'mmccart8@hfhs.org', 'phoneExt': '313-916-9419'}, {'name': 'Sachin Parikh, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Henry Ford Hospital', 'geoPoint': {'lat': 42.33143, 'lon': -83.04575}}, {'zip': '07601', 'city': 'Hackensack', 'state': 'New Jersey', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Sameer Jamal, MD', 'role': 'CONTACT', 'email': 'sameer.jamal@hmhn.org', 'phone': '551-996-5870'}, {'name': 'Manuel Castillo, RN', 'role': 'CONTACT', 'email': 'Manuel.Castillo@hmhn.org', 'phone': '5519962136'}, {'name': 'David Landers, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Mody Kanika, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Sameer Jamal, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Hackensack University Medical Center', 'geoPoint': {'lat': 40.88593, 'lon': -74.04347}}, {'zip': '10019', 'city': 'New York', 'state': 'New York', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Joslin J Plathottam, MBBS, MPH', 'role': 'CONTACT', 'email': 'JoslinJose.Plathottam@mountsinai.org', 'phone': '631-750-6345'}, {'name': 'Jeffrey Bander, MD, FACC', 'role': 'CONTACT', 'email': 'Jeffrey.bander@mountsinai.org', 'phone': '212-381-0918'}, {'name': 'Johanna Contreras, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Mount Sinai Hospital', 'geoPoint': {'lat': 40.71427, 'lon': -74.00597}}, {'zip': '27401', 'city': 'Greensboro', 'state': 'North Carolina', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Jennifer Knapp', 'role': 'CONTACT', 'email': 'Jennifer.knapp@conehealth.com', 'phone': '336-832-3795'}, {'name': 'Kimberly Lutterloh', 'role': 'CONTACT', 'email': 'Kimberly.lutterloh@conehealth.com', 'phone': '336-832-3748'}, {'name': 'Daniel Bensimhon, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Moses H. Cone Memorial Hospital', 'geoPoint': {'lat': 36.07264, 'lon': -79.79198}}, {'zip': '78705', 'city': 'Austin', 'state': 'Texas', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Andrea Natale, MD', 'role': 'CONTACT', 'email': 'tcarfan@gmail.com', 'phone': '512-807-3150'}, {'name': 'Deb Cardinal', 'role': 'CONTACT', 'email': 'dscardinal@austinheartbeat.com'}, {'name': 'Andrea Natale, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Texas Cardiac Arrhythmia Research Foundation', 'geoPoint': {'lat': 30.26715, 'lon': -97.74306}}, {'zip': '78596', 'city': 'Weslaco', 'state': 'Texas', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Frank Mazzola, MD', 'role': 'CONTACT', 'email': 'frank.mazzola@southheartclinic.org', 'phone': '877-426-7457'}, {'name': 'Nathalie Guajardo', 'role': 'CONTACT', 'email': 'Nathalie.guajardo@southheartclinic.org', 'phone': '9564285522'}, {'name': 'Frank Mazzola, MD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'South Heart Clinic', 'geoPoint': {'lat': 26.15952, 'lon': -97.99084}}], 'centralContacts': [{'name': 'Sandeep Gulati, PhD', 'role': 'CONTACT', 'email': 's.gulati@peerbridgehealth.com', 'phone': '8182162958'}, {'name': 'Chris Darland, MBA', 'role': 'CONTACT', 'email': 'c.darland@peerbridgehealth.com', 'phone': '814-572-7138'}], 'overallOfficials': [{'name': 'Andrea Natale, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Texas Cardiac Arrhythmia Research Foundation'}, {'name': 'Johanna P Contreras, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'MOUNT SINAI HOSPITAL'}, {'name': 'Sachin Parikh, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Henry Ford Hospital'}, {'name': 'Brian Kolski, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Orange County Heart Institute'}, {'name': 'Daniel Bensimhon, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Moses H. Cone Memorial Hospital'}, {'name': 'Sandeep Gulati, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Peerbridge Health, Inc'}, {'name': 'Frank Mazzola, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'South Heart Clinic'}, {'name': 'Sameer Jamal, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Hackensack Meridian Health'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ICF', 'CSR'], 'timeFrame': '60 days after Trial Scoring is Completed. Expected Around July/Aug 2025. Will be available for 3 years after the date.', 'ipdSharing': 'YES', 'description': 'De-Identified Case Report Forms; Peerbridge COR ECG Waveform Data in standards representation (MIT-BIH Physionet Format); 12-Lead ECG PDF and DICOM files; and ECHO Reports. Adverse Event Information.', 'accessCriteria': 'Trial Site PI, Sub-Investigators, Trial Site Research Staff, Trial Site Affiliates, Data Safety Monitoring Board (DSMB)Members, KOLs invited to Data Reviews and Affiliates; Publication submissions sites if required.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Peerbridge Health, Inc', 'class': 'INDUSTRY'}, 'responsibleParty': {'type': 'SPONSOR'}}}}