AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor

Официальное название

AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor

Обзор

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.

Подробное описание

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.

Вмешательства

• Устройство 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 maintai

Первичные конечные точки

• Agreement of CorEFS Software EF Severity Categories Using Peerbridge COR™ ECG Data with ASE EF Severity Categories Established by Ultrasound Echocardiography [Срок оценки: Through study completion, average of 9 months.]

Вторичные конечные точки (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 [Срок оценки: Through study completion, average of 9 months.]

Критерии участия

Критерии включения

• Age ≥ 18 years
• Able and eligible to wear a Holter monitor

Критерии исключения

• 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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

США · 8 центров

• Orange County Heart Institute — Orange
• Peerbridge Health — Pasadena
• Henry Ford Hospital — Detroit
• Hackensack University Medical Center — Hackensack
• Mount Sinai Hospital — New York
• Moses H. Cone Memorial Hospital — Greensboro
• Texas Cardiac Arrhythmia Research Foundation — Austin
• South Heart Clinic — Weslaco

Публикации

• 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 22416086
• 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 Ima PMID 25559473
• 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 32554161
• 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 28546456
• 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 35330455
• 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 202 PMID 32986471
• 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 M PMID 33958795
• 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 37489538

Идентификаторы

NCT: NCT06699056 · PBH-COREFS-1-A · PBH-COREFS-1-A

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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