5 Clinical Trials for Various Conditions
Cardiac resynchronization therapy with pacemaker alone, or in combination with a cardioverter-defibrillator, prolongs life and decreases risk of heart failure exacerbation in patients with low ejection fraction and wide QRS. Some patients achieve decrease in QRS duration 6 months after cardiac resynchronization therapy. Such phenomenon is called reverse electrical remodeling of native conduction. Retrospective analysis showed that reverse electrical remodeling of the native conduction after at least 6 months of CRT is associated with decreased rate of ventricular arrhythmias and better survival. This study is designed to study reverse electrical remodeling prospectively.
A consequence of chronic RV pacing is a process of electrical remodeling that alters myocardial repolarization to reflect the altered depolarization induced by the RV lead. When RV pacing is discontinued, the altered repolarization persists for several weeks. This phenomena is traditionally described as "T-wave memory" based upon the 12-lead ECG appearance of inverted T-Waves. The investigators are using the ECGI technique to produce three dimensional electroanatomical images of this phenomena in patients with dual chamber pacemakers. Echocardiography will also be used to image the mechanical effects of RV pacing and T-wave memory. The images will show the spatial distribution of altered repolarization and allow us to correlate any mechanical consequences of this phenomena that may exist.
Heart failure with reduced left ventricular ejection fraction (HFrEF) is the most common form of chronic heart failure in subjects ≤ 75 years of age. Beta-blocker therapy greatly reduces mortality and improves ventricular function in HFrEF patients, but 30-40% of patients do not show improvement in ventricular function with beta blockade. An extensive gene signaling network downstream from the beta1-adrenergic receptor, the primary target of beta-blocker therapy is likely important for development and progression HFrEF. Pathologic changes in this gene signaling network are only reversed towards normal levels when ventricular function improves. One potential mechanism for failure to improve ventricular function in HFrEF patients unresponsive to beta blocker therapy is a lack of heart rate reduction. Ivabradine is an FDA-approved medication believed to have therapeutic benefit in HFrEF patients through reduction in heart rate independent of beta-blockade. Ivabradine has been shown to reduce the risk of hospitalization for worsening HF in patients with stable, symptomatic chronic heart failure with reduced EF (≤ 35%)in sinus rhythm with resting heart rate ≥ 70 bpm and who are on maximally tolerated doses of beta blockers or who have a contraindication to beta blockers. Given the high rate of mortality and hospitalization of HFrEF patients even with current therapies, there is a large unmet need for improving HFrEF therapy. The goals of this study are to test the hypothesis that heart rate reduction is an important antecedent for improvement in ventricular function, and to identify components of the beta1-adrenergic receptor gene signaling network responsible for improvement in ventricular function caused by heart rate reduction.
The COR-INSIGHT trial aims to evaluate the effectiveness of Peerbridge COR advanced ambulatory ECG wearables (COR 1.0 and COR 2.0) in accurately and non-invasively detecting cardiovascular and cardiopulmonary conditions using AI-based software (CardioMIND and CardioQSync). The study devices offer non-invasive, multiplexed, AI-enabled direct-from-ECG detection as a novel alternative to traditional diagnostic methods, including imaging, hemodynamic monitoring systems, catheter-based devices, and biochemical assays. Continuous COR ECG data collected in hospital, outpatient clinic, or home settings will be analyzed to evaluate the predictive accuracy, sensitivity, specificity, and performance of these devices in differentiating between screen-positive and screen-negative subjects. The panel of screened indications encompasses a broad spectrum of clinically relevant cardiovascular, cardiopulmonary, and sleep-related diagnostic parameters, which are critical for advanced patient assessment and management. In the cardiovascular domain, the protocol emphasizes the detection and classification of heart failure, assessment of ejection fraction severity, and identification of myocardial infarction, including pathological Q-waves and STEMI. It further addresses diagnostic markers for arrhythmogenic conditions such as QT interval prolongation, T-wave alternans, and ventricular tachycardia, as well as insights into ischemia, atrial enlargement, ventricular activation time, and heart rate turbulence. Additional parameters, such as heart rate variability, pacing efficacy, electrolyte imbalances, and structural abnormalities, including left ventricular hypertrophy, contribute to comprehensive cardiovascular risk stratification. In the non-invasive cardiopulmonary context, the protocol incorporates metrics like respiratory sinus arrhythmia, cardiac output, stroke volume, and stroke volume variability, providing critical insights into hemodynamic and autonomic function. The inclusion of direct-from-ECG metrics for sleep-related disorders, such as the apnea-hypopnea index, respiratory disturbance index, and oxygen saturation variability, underscores the protocol's utility in addressing the intersection of cardiopulmonary and sleep medicine. This multifaceted approach establishes a robust framework for precision diagnostics and holistic patient management. The COR 1.0 and COR 2.0 wearables provide multi-lead ECG recordings, with COR 2.0 offering extended capabilities for cardiopulmonary metrics and longer battery life (up to 14 days). COR 2.0 supports tri-modal operations: (i) Extended Holter Mode: Outputs Leads II and III, mirroring the functionality of COR 1.0 for broader ECG monitoring applications. (ii) Cardiopulmonary Mode: Adds real-time recording of Lead I, V2, respiratory impedance, and triaxial accelerometer outputs, providing advanced cardiopulmonary insights. (iii) Real-Time Streaming Mode: Streams data directly to mobile devices or computers via Bluetooth Low Energy (BLE), enabling real-time waveform rendering and analysis. The COR 2.0 units are experimental and not yet FDA-cleared. Primary endpoints include sensitivity (true positive rate) \> 80%, specificity (true negative rate) \> 90%, and statistical agreement with reference devices for cardiovascular, cardiopulmonary, and sleep metrics. Secondary endpoints focus on predictive values (PPV and NPV) and overall diagnostic performance. The study employs eight distinct sub-protocols (A through H) to address a variety of cardiovascular, cardiopulmonary, and sleep-related diagnostic goals. These sub-protocols are tailored to specific clinical endpoints, varying in duration (30 minutes to 14 days) and type of data collection. Up to 15,000 participants will be enrolled across multiple sub-protocols. Screening ensures eligibility, and subjects must provide informed consent before participation. Dropouts and non-compliant subjects will be excluded from final analyses.
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.