AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 2,000
- 试验地点
- 16
- 主要终点
- Agreement of CorEFS Software EF Severity Categories Using Peerbridge COR™ ECG Data with ASE EF Severity Categories Established by Ultrasound Echocardiography
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Age ≥ 18 years
- •Able and eligible to wear a Holter monitor
排除标准
- •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
结局指标
主要结局
Agreement of CorEFS Software EF Severity Categories Using Peerbridge COR™ ECG Data with ASE EF Severity Categories Established by Ultrasound Echocardiography
时间窗: 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.
次要结局
- 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(Through study completion, average of 9 months.)