Clinical Research Directory

COR-INSIGHT: Optimizing Cardiovascular and Cardiopulmonary Outcomes with AI-Driven Multiplexed Indications Using COR ECG Wearable

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.

Peripheral Venous Pressure Variation, Pulse Pressure Variation and Pleth Variability Index for Fluid Responsiveness

Pulse pressure variation (PPV) and pleth variability index (PVI) are widely used in clinical practice as indicators of the responsiveness to fluid therapy in patients receiving mechanical ventilation. PPV, which measures changes in arterial pressure, requires arterial puncture, which is invasive, and PVI, which detects subtle changes in oxygen saturation, requires an expensive, commercial monitoring equipment. In this study, we aimed to measure peripheral venous pressure variation using less invasive waveform variation in peripheral veins and to determine whether this indicator can be clinically used to predict the responsiveness to fluid therapy. In addition, the investigators aimed to confirm the superiority of the indicators by comparing them with the responsiveness to fluid therapy of the PPV and PVI.

Deep Learning Model for Predicting a Peripheral Venous Waveform-based Pulse Pressure Variation

Pulse pressure variation is a monitoring index that indicates the response to fluid therapy in patients receiving mechanical ventilation, and is used as a reference for patients with unstable hemodynamic conditions. However, it is invasive because it requires arterial puncture to collect it. In a previous study by the investigators, the investigators developed and verified an artificial intelligence model that predicts stroke volume variation, in real time using only the central venous pressure waveform. However, since a large vein such as the jugular vein must be punctured to collect the central venous pressure waveform, it is still invasive, and its clinical utility is low. Therefore, in this study, the investigators collected waveforms from peripheral veins that are less invasive and can be a wide range of applications because all surgical patients have them. The investigators aimed to develop and verify an artificial intelligence model that predicts pulse pressure variation obtained from peripheral venous waveforms .