View clinical trials related to Arrhythmias, Cardiac.
Filter by:Demonstrate the safety and effectiveness of the Ablacathâ„¢ Mapping Catheter and Ablamap® System in patients with all types of atrial fibrillation (AF) including paroxysmal or persistent or long-standing persistent, undergoing and De Novo or Redo procedures. Phenotype patients and demonstrate the prognostication power of Electrographic Flow (EGF®) maps among all subjects using 12-month follow-up outcomes following EGF-guided mapping and ablation.
Single center prospective study to analyze the impact of diabetes mellitus on patients' outcome following radiofrequency-guided catheter ablation for atrial fibrillation.
LIST OF PLANNED ORIGINAL PUBLICATIONS 1. T wave inversion detection with machine learning to prevent sudden death in professional football players. 2. Machine learning applied to biological parameters for control and advisory in professional football players (Machine learning applied to biological parameters for control and advisory in professional football players.) 3. Machine learning applied to sport geolocation systems for injury prevention in professional football players.
The goal of this clinical trial is to demonstrate that the OPTIMIZER® Integra CCM-D System (the "CCM-D System") can safely and effective convert induced ventricular fibrillation (VF) and spontaneous ventricular tachycardia and/or ventricular fibrillation (VT/VF) episodes in subjects with Stage C or D heart failure who remain symptomatic despite being on guideline-directed medical therapy (GDMT), are not indicated for cardiac resynchronization therapy (CRT), and have heart failure with reduced left ventricular ejection fraction (LVEF ≤40%). Eligible subjects will be implanted with the CCM-D System. A subset of subjects will be induced into ventricular fibrillation "on the table" in the implant procedure room. During the follow-up period, inappropriate shock rate and device-related complications will be evaluated. The follow-up period is expected to last at least two years.
Atrial fibrillation (AF) is a major public health issue: it is increasingly common, incurs substantial healthcare expenditure, and is associated with a range of adverse outcomes. There is rationale for the early diagnosis of AF, before the first complication occurs. Previous AF screening research is limited by low yields of new cases and strokes prevented in the screened populations. For AF screening to be clinically and cost-effective, the efficiency of identification of newly diagnosed AF needs to be improved and the intervention offered may have to extend beyond oral anticoagulation for stroke prophylaxis. Previous prediction models for incident AF have been limited by their data sources and methodologies. An accurate model that utilises existing routinely-collected data is needed to inform clinicians of patient-level risk of AF, inform national screening policy and highlight opportunities to improve patient outcomes from AF screening beyond that of only stroke prevention. The investigators will use routinely-collected hospital-linked primary care data to develop and validate a model for prediction of incident AF within a short prediction horizon, incorporating both a machine learning and traditional regression method. They will also investigate how atrial fibrillation risk is associated with other diseases and death. Using only clinical factors readily accessible in the community, the investigators will provide a method for the identification of individuals in the community who are at risk of AF, thus accelerating research assessing whether atrial fibrillation screening is clinically effective when targeted to high-risk individuals.
The objective of this study is to prospectively validate in real life cohorts from various departments of the APHP our artificial intelligence (deep-learning) models allowing for : 1. automatic measurement of various ECG quantitative features, 2. identification and typing of LQT and risk of TdP.
The goal of this study is to compare the heart signals that are received by a new heart monitoring device called the HeartWatch to heart signals that are recorded by existing monitors used in cardiology clinics today. The main question[s] it aims to answer are: 1. Does the HeartWatch recording allow characterization of tagged and abnormal arrhythmia events that are equivalent to the existing Event Recorder and Holter devices? 2. Does the HeartWatch recording allow for detection of QRS complexes that are equivalent to the predicate Holter lead I reference signal? Participants who have been recommended to receive a heart monitor for heart rhythm assessment will asked to wear the HeartWatch armband monitor at the same time as the standard heart monitor. They will be asked to flag symptoms (if they occur) on either the HeartWatch or on the Helpwear Smartphone. When their monitoring session for the standard heart monitor is complete, then the monitoring session for the HeartWatch armband is also complete.
The investigators will examine the effects of dynamic light therapy on circadian rhythms in intensive care unit (ICU) patients. In a randomized controlled trial (RCT), they will investigate the effects of a specific light algorithm on rhythms of serum melatonin, clock gene expression, the proteome, and metabolome, compared to standard hospital lighting, supported by the data science algorithms to improve vital-based algorithms with light interventions.
There is some limited evidence that reduced size of electrical complexes/traces of the heart on the electrocardiogram (ECG) may be associated with scarring in the heart muscle, which may predispose to serious life-threatening electrical abnormalities and sudden cardiac death (SCD). There is no current guidance on how young individuals and athletes with reduced ECG traces should be managed. Therefore, correct interpretation of this ECG finding is crucial for identifying athletes with disease and at risk of SCD. Some athletes experience SCD despite normal standard cardiac tests. The investigators, therefore, propose to study young healthy individuals and young athletes using cardiovascular MRI, cardiopulmonary exercise testing, 24 hour ECG monitoring and genetic analysis to determine the significance of reduced ECG traces and possibly revise current international sports recommendations.
The primary goal of the study is to compare different methods for pulse wave measurement and analysis which are cuffless and contactless. The novel measurement tools will be compared to cuff-based reference methods.