Clinical trial NCT06699056: AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor
Official title: AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor
Study type: OBSERVATIONAL
Overall status: RECRUITING
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
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
Lead sponsor: Peerbridge Health, Inc
Sponsor class: INDUSTRY
First posted: 2024-11-21
Enrollment: 1500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
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.

Brief 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 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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* 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

Primary outcomes:
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.
Secondary outcomes:
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.
Other outcomes:
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.
Locations:
city: Orange
country: United States
facility: Orange County Heart Institute
state: California
city: Melbourne
country: United States
facility: Peerbridge Health
state: Florida
city: Detroit
country: United States
facility: Henry Ford Hospital
state: Michigan
city: Hackensack
country: United States
facility: Hackensack University Medical Center
state: New Jersey
city: New York
country: United States
facility: Mount Sinai Hospital
state: New York
city: Greensboro
country: United States
facility: Moses H. Cone Memorial Hospital
state: North Carolina
city: Austin
country: United States
facility: Texas Cardiac Arrhythmia Research Foundation
state: Texas
city: Weslaco
country: United States
facility: South Heart Clinic
state: Texas
Study officials:
affiliation: Texas Cardiac Arrhythmia Research Foundation
name: Andrea Natale, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: MOUNT SINAI HOSPITAL
name: Johanna P Contreras, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Henry Ford Hospital
name: Sachin Parikh, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Orange County Heart Institute
name: Brian Kolski, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Moses H. Cone Memorial Hospital
name: Daniel Bensimhon, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Peerbridge Health, Inc
name: Sandeep Gulati, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: South Heart Clinic
name: Frank Mazzola, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Hackensack Meridian Health
name: Sameer Jamal, MD
role: PRINCIPAL_INVESTIGATOR
References:
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