172 Clinical Trials for Various Conditions
Evaluation of the mobiCARE™ ECG Monitoring System Electrode Placement Positioning Limits in a Prospective, Non-Randomized, Single-Center U.S. Study
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.
The study is a prospective single-arm multicenter clinical trial. Adult patients will be consented and enrolled in an outpatient cardiology office or Arrhythmia Clinics.
CSF-3 is a wrist-worn medical device similar to a watch. It is designed to monitor a user's heart rate based on ECG (electrocardiogram) and PPG (Photoplethysmograph) readings, using off-the-shelf ECG and unique PPG chipsets with proprietary algorithms. ECG and PPG are the two primary technologies for measuring heart rate. ECG measures the bio-potential generated by electrical signals that control the expansion and contraction of heart chambers, while PPG uses light-based technology to sense the rate of blood flow as controlled by the heart's pumping action.
This study is a cross-sectional observational study conducted in patients referred for routine ECG, to compare output from the everbeat finger-worn ECG device to standard of care (SOC) 12-lead electrocardiography. No intervention is involved in this study. Patients visiting the study site during the study period will be assessed for eligibility on a convenience basis, and observational data will be recorded on those eligible.
The purpose of this study is two-fold. First we would like to confirm that non-contact ECG provides equivalency to current contact methods of obtaining ECG data. Second we would like to investigate whether non-contact ECG can detect ECG changes prior to the onset symptoms from COVID19.
The purpose of this study is to determine, among a large cohort of 300 consecutive patients with hypertrophic cardiomyopathy, if extended ambulatory monitoring using the iRhythm Technologies, Inc. Zio XT device results in identifying a greater burden of nonsustained ventricular tachyarrhythmia (nsVT) compared to current ACCF/AHA guideline recommended 48-hour monitoring.
This is a feasibility study for a new application for capturing fetal cardiac activity. The objective of this study is to determine if it is feasible to capture a fetal ECG signal using a Holter ECG device. As comparison we will use a standard Doppler Fetal Heart Rate (FHR) device.
This study will validate the recording accuracy of a specific electrical interval of the heart, the QT interval, between an iPhone rhythm strip recording and a traditional 12-lead electrocardiogram (ECG). These measurements will occur in hospitalized patients that are starting either sotalol or dofetilide, since both of these medications can prolong the QT interval.
The purpose of this study is to evaluate the ECG effects of 10, 40, and 80 mcg/hr buprenorphine delivered by BTDS alone, or by BTDS dosed with naltrexone, relative to placebo in healthy male and female subjects.
This is a prospective, unblinded, non-randomized, descriptive study designed to collect ECG data.
To determine if there is a difference between wireless ECG and wired ECG findings.
Cardiac resynchronization therapy with a defibrillator (CRT-D) in heart failure (HF) patients without left bundle branch block (non-LBBB) has been less beneficial to improve outcomes despite being a guideline indicated therapy, posing a significant treatment challenge. However, non-LBBB patients with echocardiography response to CRT-D have better outcomes, and pre-implant variables could predict response, identifying patients who benefit the most. In this study, we plan to enroll 270 HF patients with non-LBBB and guideline-indicated CRT-D implantation to validate our prior echocardiography predictor score, and to identify novel ECG and echocardiography predictors using conventional statistics and machine learning analysis. We will also assess the applicability of such a score for clinical outcomes of HF, ventricular arrhythmias, or death.
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.
The purpose of this study is to demonstrate the safety and effectiveness of the VDI UHF-ECG System in the diagnosis of ventricular dyssynchrony when compared to the 12-lead ECG in patients with bradycardia and heart failure indicated for pacemaker implantation.
The purpose of the study is to evaluate the performance of ECG classification algorithms and their ability to classify heart rhythms into multiple categories of rhythms and heart rates.
This is a prospective, open-label, multi-center workflow pilot. Viz HCM will be implemented at each participating site and all conforming incoming ECG recordings within a 6-month enrollment period will be analyzed using Viz HCM. This workflow pilot provides an opportunity to understand how Viz HCM will be utilized and adopted post-market and to learn the pre- versus post-implementation impact of Viz HCM on HCM clinical workflow. This pilot is designed to evaluate the implementation of Viz HCM for use in traditional HCM clinical workflow. Findings from this pilot will help inform the following: * The impact of Viz HCM on HCM clinical workflow * How Viz HCM will be utilized and adopted post-market * The diversity in the HCM patient population and the variation in HCM clinical workflow
The objective of this clinical study was to evaluate the impact of Multipoint Pacing (MPP) and SyncAV programming on ventricular electrical activation time and activation sequence using noninvasive electrocardiographic imaging (ECGi) in patients receiving cardiac resynchronization therapy (CRT).
When a newborn is diagnosed with tachyarrhythmia, they are generally started on medical therapies, most commonly a beta-blocker, while being observed in an inpatient setting. In most academic institutions, current practice is to provide parental teaching on use of a stethoscope to auscultate their child when there is suspicion for distress, in addition to requiring cardiopulmonary resuscitation (CPR) classes. Fortunately, newer technologies have emerged that allow for capture of cardiac rhythm that may provide a buffer between the infant and the emergency room.
The aim of the study is to evaluate the performance of Withings SCT02 with embedded Withings ECG-app in the automatic detection of atrial fibrillation
The purpose of this study is to evaluate the AI-ECG algorithm for HCM in detecting HCM and in differentiating it from athlete's heart using not only the standard 12-lead ECG, but also ECGs obtained with the Apple Watch and Alivecor KardiaMobile devices.
AliveCor (www.alivecor.com) has developed several electrocardiogram (ECG) devices that interface with iOS and Android smartphones and tablets via various Kardia apps. The current Kardia family of devices can measure single lead and six limb-lead ECGs, depending on the device. KardiaMobile, KardiaMobile 6L, and KardiaMobile Card have FDA clearance for ECG rhythm recording. A modified single-lead Kardia smartphone 12-lead ECG was previously validated in the multicenter ST LEUIS study for the diagnosis of ST-Segment Elevation Myocardial Infarction (STEMI) and Non-ST-Elevation Myocardial Infarction (NSTEMI). Recently, AliveCor developed a new device: AliveCor (AC) 12-lead (12L) ECG System to record simultaneously 4 leads of ECG and then generate complete 12-lead ECGs. A previous protocol at the University of Oklahoma involved 200 subjects with early prototypes of the AC 12L device with the specific aim to validate that it accurately generated 12-lead ECGs as compared to simultaneously acquired FDA-cleared 12-lead ECGs. The prototype version of the AliveCor 12L ECG System simultaneously measured four channels of ECG (leads I, II, V2, V4), calculated the remaining limb leads as is standard for 12-lead ECGs (Leads III, aVR, aVL, aVF) and synthesized the remaining 4 precordial ECG leads (V1, V3, V5, V6). This protocol will serve to validate the production version of the system against standard 12-Lead ECGs for the diagnosis of STEMI and NSTEMI in patients admitted to the Emergency Department or directly to the Cardiac Cath Lab for the evaluation of chest pain. It is anticipated that the waveforms for each of the 12 leads from the AC 12L ECG System will be highly correlated with the corresponding leads from the comparator commercially available 12-lead ECG devices used at participating sites. The purpose of this study is to clinically validate that the four-channel AC 12L ECG device can enable the diagnosis of STEMI and NSTEMI in a non-inferior manner to existing 12-lead ECG devices.
The main aim of this study is to find out how more than one dose of TAK-279 changes the heart-rate corrected QT (QTc) interval of healthy adults. A QTc interval is a measurement on an electrocardiogram (ECG) and shows the time when the heart contracts until it finishes relaxing. Other aims are to learn about the effect of several doses of TAK-279 on other ECG measurements of healthy adults, how the body of a healthy adult processes TAK-279 and moxifloxacin (pharmacokinetics), and to learn about the side effects of TAK-279 and how well it is tolerated when given to healthy adults. The participants will be given TAK-279 and moxifloxacin or a placebo for 7 days. During the study, participants will need to stay at the clinic for 10 days.
The objective of this study is to validate the performance of the electrocardiogram (ECG) function of the Masimo INVSENSOR00057 against contemporaneous measurements
This study is designed to evaluate the performance of the Verily Watch Cardio for recording electrocardiogram (ECG) and photoplethysmography (PPG) signals and detecting suspected atrial fibrillation (AF) episodes, in a free-living environment, in participants at risk for having an AF event.
The proposed research is to address the accessibility and affordability of technology to capture symptomatic and asymptomatic cardiac events via Long-Term Continuous Electrocardiogram Monitoring (LTCM), and to provide physicians with full access to their patients' recorded data in a timely manner. We adopt an FDA cleared single- lead OEM patch Holter made of flexible material to make long-term wearing more comfortable and more patient compliant. The patch transmits the recorded data to our cloud-based platform: ZBPro™, where our innovative technologies reside. Our proprietary AI algorithms with innovative human interaction tools, which were developed under the National Science Foundation (NSF) Small Business Innovation Research (SBIR) Phase I (Award #: 2025951) Award analyze and interpret the recorded data to detect and annotate arrhythmia/cardiac events, and generates daily reports for physician review. The feasibility of our algorithms has been verified using ZBeats' proprietary ECG database based on the standard ANSI/AAMI EC57. Data collection and transmission has been verified in the office environment. This proposed observational study will utilize a multidisciplinary collaboration of ZBeats Inc. (ZBTS), Stony Brook University (SBU), and Lankenau Medical Center (LMC). The study will enroll patients undergoing ECG monitoring. The primary outcome measure will be the ability to capture cardiac arrhythmias and events from participants. The prescribed FDA-cleared device will serve as the ground truth (GT) for our analyses. The detected arrhythmias and events from our solution will be compared with the findings from the ground truth to obtain our system's detection rate. The goal is to achieve a detection rate of \>80% to be deemed successful.
The primary objective of this study is to collect CT scan data without ECG-leads attached to the human subject. This data is intended to represent a typical range of clinical scenarios in which Cardiac CT imaging is used.
This is a single-center, non-randomized study to collect validation data for submission to the Food and Drug Administration (FDA) to support a 510(k) device clearance for a smartwatch ECG.
A prospective, cluster-randomized, care-as-usual controlled trial to evaluate the impact of an ECG-based artificial intelligence (ECG-AI) algorithm to detect low left ventricular ejection fraction (LVEF) on diagnosis rates of LVEF ≤ 40% in the outpatient setting. The objective of this study is to evaluate the impacts of an ECG-AI algorithm to detect low LVEF and an associated Medical Device Data System when used during routine outpatient care. The study will be conducted in 2 phases: feasibility assessment phase and clinical impact phase.
The purpose of this study is to evaluate electrocardiogram (ECG) changes in 100 patients undergoing transcatheter aortic valve replacement (TAVR) to assess new-onset conduction abnormalities, such as atrioventricular nodal block (AVB) (1st, 2nd, or 3rd degree), or new-onset left bundle branch block (LBBB) that may occur during the procedure. Eligible patients enrolled in this study will be monitored with an FDA-approved ECG Holter system during TAVR, to assess intra-procedural changes. This will be a small-scale, early feasibility study performed to inform a future, larger-scale prospective investigation.