Ventricular Ejection FractionEFACTNCT06699056
AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor
An observational study of Ventricular Ejection Fraction and LVF, run by Peerbridge Health, Inc at 8 sites in United States.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Ventricular Ejection Fraction, LVF, LV Dysfunction
- What is being tested?
- 15-minutes of sitting during COR ECG Acquistion
- Type of treatment
- Medical device
- How many people
- 2,000 participants
- Where
- 8 sitesUnited States
- Study dates
- 21 Nov 2024to 15 Nov 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: 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. Enrollment 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.
Who may be able to join
- Age ≥ 18 years
- Able and eligible to wear a Holter monitor
Who cannot join
- 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
Trial locations and contacts
8 sites in 1 country. Contact the study team or a site near you to ask about taking part.
8 sites in 1 country
United States8 sites, 8 recruiting
- Recruiting
Texas Cardiac Arrhythmia Research Foundation
Austin, Texas, 78705
Andrea Natale, MD, 512-807-3150, tcarfan@gmail.com
Deb Cardinal, dscardinal@austinheartbeat.com
- Recruiting
Henry Ford Hospital
Detroit, Michigan, 48202
Sacchin Parikh, MD, 313-916-2721, Sparikh2@hfhs.org
Meghan McCarthy, mmccart8@hfhs.org
- Recruiting
Moses H. Cone Memorial Hospital
Greensboro, North Carolina, 27401
Jennifer Knapp, 336-832-3795, Jennifer.knapp@conehealth.com
Kimberly Lutterloh, 336-832-3748, Kimberly.lutterloh@conehealth.com
- Recruiting
Hackensack University Medical Center
Hackensack, New Jersey, 07601
Sameer Jamal, MD, 551-996-5870, sameer.jamal@hmhn.org
Manuel Castillo, RN, 5519962136, Manuel.Castillo@hmhn.org
- Recruiting
Mount Sinai Hospital
New York, New York, 10019
Joslin J Plathottam, MBBS, MPH, 631-750-6345, JoslinJose.Plathottam@mountsinai.org
Jeffrey Bander, MD, FACC, 212-381-0918, Jeffrey.bander@mountsinai.org
- Recruiting
Orange County Heart Institute
Orange, California, 92868
Brian Kolski, MD, (714) 564-3300, bkolskimd@gmail.com
Brian Kolski, MD
- Recruiting
Peerbridge Health
Pasadena, California, 91107
Sandeep Gulati, PhD, 877-960-0332, s.gulati@peerbridgehealth.com
Lauren Pickard, MS, l.pickard@peerbridgehealth.com
- Recruiting
South Heart Clinic
Weslaco, Texas, 78596
Frank Mazzola, MD, 877-426-7457, frank.mazzola@southheartclinic.org
Nathalie Guajardo, 9564285522, Nathalie.guajardo@southheartclinic.org
About this study
Official title: AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor
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.
In 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.
Read the detailed description
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.
Background 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.
Echocardiography (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.
Successful 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.
Hypothesis Specific ECG changes may identify left ventricular dysfunction (LVSD) and predict EF severity, enabling low-burden, cost-effective EF monitoring in high-risk populations.
Study Design
Participant Enrollment and Setup
Participants will receive the Peerbridge Cor® wearable, with data collection occurring through:
In-clinic setup: Study staff apply and initiate device use. Patient Home Setup (PHS): Telehealth guidance for independent device application (20% of participants).
Subprotocols
A: 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.
Data Collection
Peerbridge Cor® ECG Data: 30 minutes recorded; 15 minutes analyzed in 5-minute segments.
Echo 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.
Endpoints 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.
This streamlined protocol validates CorEFS software for reliable, cost-effective EF monitoring and clinical decision support.
- Conditions
- Ventricular Ejection Fraction, LVF, LV Dysfunction, Atrial Enlargement, Conduction Defect, Heart Failure
- Being tested
- 15-minutes of sitting during COR ECG Acquistion
- 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
- Medical subject headings
- Ventricular Dysfunction, Left, Arrhythmias, Cardiac, Heart Failure, Heart Valve Diseases, Myocardial Ischemia, Cardiotoxicity, Myocardial Infarction, Cardiomyopathy, Dilated
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
15-minutes of sitting during COR ECG AcquistionDevice
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.
To 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.
| Study group | Role | What they receive |
|---|---|---|
| Cohort Breakdown to Power Accuracy Assessments | The study will enroll up to 2000 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 3 hours 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.
A 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. Receives: Device: 15-minutes of sitting during COR ECG Acquistion |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, average of 9 months.
Secondary outcomes (1)
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.
Measured: Through study completion, average of 9 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Nov 2024
- Study start21 Nov 2024
- Primary completion15 Nov 2027 (estimated)
- Study completion15 Nov 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Ventricular Ejection FractionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peerbridge Health, Inc
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Andrea Natale, MD, Principal investigator, Texas Cardiac Arrhythmia Research FoundationJohanna P Contreras, MD, Principal investigator, MOUNT SINAI HOSPITALSachin Parikh, MD, Principal investigator, Henry Ford HospitalBrian Kolski, MD, Principal investigator, Orange County Heart InstituteDaniel Bensimhon, MD, Principal investigator, Moses H. Cone Memorial HospitalSandeep Gulati, PhD, Principal investigator, Peerbridge Health, IncFrank Mazzola, MD, Principal investigator, South Heart ClinicSameer Jamal, MD, Principal investigator, Hackensack Meridian Health
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06699056
- Sponsor's protocol ID
- PBH-COREFS-1-A
- Other IDs
- PBH-COREFS-1-A
- Acronym
- EFACT
- Status last verified
- May 2026
- First submitted
- 15 Nov 2024
- Last updated
- 1 Jun 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Yes: 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.
Publications
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.
PubMed 22416086Lang 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.
PubMed 25559473Alhamaydeh 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.
PubMed 32554161O'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.
PubMed 28546456Chen 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.
PubMed 35330455Adedinsewo 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.
PubMed 32986471Yao 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.
PubMed 33958795Sangha 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.
PubMed 37489538Bachtiger 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.
PubMed 34998740Al 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.
PubMed 38739639Garcia-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.
PubMed 35904538
Study at a glance
From the registry record, last updated by the sponsor on 1 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor
- ClinicalTrials.gov ID
- NCT06699056
- Acronym / sponsor ID
- EFACT / PBH-COREFS-1-A
- Conditions
- Ventricular Ejection Fraction, LVF, LV Dysfunction, Atrial Enlargement, Conduction Defect, Heart Failure
- Keywords
- ECG Patch, LVEF, Holter, ECG Wearable, COR, Atrial Conduction, ECG Biomarker, LV Dysfunction
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Peerbridge Health, IncIndustry (pharma, biotech, devices)
- Study start
- 21 Nov 2024 (actual)
- Primary completion
- 15 Nov 2027 (estimated)
- Study completion
- 15 Nov 2027 (estimated)
- First posted
- 21 Nov 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
In 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.
Detailed description
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.
Background 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.
Echocardiography (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.
Successful 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.
Hypothesis Specific ECG changes may identify left ventricular dysfunction (LVSD) and predict EF severity, enabling low-burden, cost-effective EF monitoring in high-risk populations.
Study Design
Participant Enrollment and Setup
Participants will receive the Peerbridge Cor® wearable, with data collection occurring through:
In-clinic setup: Study staff apply and initiate device use. Patient Home Setup (PHS): Telehealth guidance for independent device application (20% of participants).
Subprotocols
A: 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.
Data Collection
Peerbridge Cor® ECG Data: 30 minutes recorded; 15 minutes analyzed in 5-minute segments.
Echo 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.
Endpoints 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.
This streamlined protocol validates CorEFS software for reliable, cost-effective EF monitoring and clinical decision support.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Cohort Breakdown to Power Accuracy Assessments | – | Device: 15-minutes of sitting during COR ECG AcquistionThe study will enroll up to 2000 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 3 hours 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. A 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- 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. Enrollment 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.
Key inclusion criteria (2 listed)
- Age ≥ 18 years
- Able and eligible to wear a Holter monitor
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 15-minutes of sitting during COR ECG AcquistionParticipants 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. To 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. | Device | Cohort Breakdown to Power Accuracy Assessments |
Primary outcome
Agreement of CorEFS Software EF Severity Categories Using Peerbridge COR™ ECG Data with ASE EF Severity Categories Established by Ultrasound Echocardiography
Time frame: Through study completion, average of 9 months.
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.
1 secondary outcome
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
Time frame: Through study completion, average of 9 months.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Jun 2026.
- Background: PubMed 22416086Murtagh 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.
- Background: PubMed 25559473Lang 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.
- Background: PubMed 32554161Alhamaydeh 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.
- Background: PubMed 28546456O'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.
- Background: PubMed 35330455Chen 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.
- Background: PubMed 32986471Adedinsewo 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.
- Background: PubMed 33958795Yao 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.
- Background: PubMed 37489538Sangha 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.
- Background: PubMed 34998740Bachtiger 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.
- Background: PubMed 38739639Al 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.
- Background: PubMed 35904538Garcia-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.
- Individual participant dataSharing planned: Yes. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Andrea Natale, MD principal investigator
Texas Cardiac Arrhythmia Research Foundation
Johanna P Contreras, MD principal investigator
MOUNT SINAI HOSPITAL
Sachin Parikh, MD principal investigator
Henry Ford Hospital
8 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ventricular Ejection Fraction and LVF, run by Peerbridge Health, Inc at 8 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 1 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 8 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Sandeep Gulati, PhD (8182162958, s.gulati@peerbridgehealth.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement of CorEFS Software EF Severity Categories Using Peerbridge COR™ ECG Data with ASE EF Severity Categories Established by Ultrasound Echocardiography (time frame: Through study completion, average of 9 months.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06699056, which also lists 11 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Jun 2026.