Active clinical trials for "Atrial Fibrillation"

The MAESTRIA study is an international, multi-centre, non-interventional, observational registry. The main goal is to enrol a representative group of European patients diagnosed with Atrial Fibrillation (AF) to analyse clinical and relevant parameters (digitalised ECG, echocardiograms, cardiac CTs, MRIs and blood biomarkers) that could be used during clinical practise for the diagnosis of atrial cardiomyopathy. The AF patients will be distributed in 3 groups according to the different manifestation of AF: paroxysmal, persistent and permanent AF.

The MEA cardiology societies have joined forces to tackle the issue by establishing a tangible real-world data registry in every MEA country. This endeavor has resulted in the development of a multicenter registry called MEA-WCVD, which is being sponsored by each national cardiology society from participating countries. All data gathered will be consolidated into a singular registry for thorough analysis. Country specific analysis will be performed.

To acquire, amplify, digitize, and record atrial intracardiac electrophysiology signals during cardiac electrophysiology studies for the treatment of persistent atrial fibrillation and to use the recorded data to test the performance of an signal complexity visualization algorithm.

Collect electrophysiological data during atrial fibrillation (AF) ablation procedures to assess the performance of a Signal Complexity Visualization algorithm designed to be integrated into the CathVision ECGenius® System.

The ARIES study is an observational study in which patients with a recent acute ischemic stroke of cryptogenic aetiology are consecutively enrolled in order to perform a extensive cardiologic work-up. The main objective is to study parameters that could predict arrythmias on prolonged monitoring and also echocardiographic parameters of left atrial disfunction that could predict the presence of a hidden atrial fibrilation and recurrent ischemic events in patients with cryptogenic stroke.

Myocardial infarction with non-obstructive coronary arteries (MINOCA) (i.e.<50% stenoses) on coronary angiography) is an underappreciated clinical entity concerning 5-6% of patients with acute myocardial infarction. Approximately 50% of these patients remain without appropriate diagnosis and treatment. The MINOCA study aims at systematically assessing the frequency of underlying pathologies of MINOCA and outcomes with a multidisciplinary etiologic work-up and follow-up of 5 years including, for the first time, an implantable cardiac monitor (ICM) to assess the frequency of atrial fibrillation as underlying cause for MINOCA.

The purpose of this study is to provide real-world data useful to address the factors associated to the administration of oral anticoagulants in the elderly population affected by non-valvular atrial fibrillation (NVAF), in Italy, and it's persistence rate after one year.

An observational, prospective, cohort study aiming to assess the potential predictive role of cardiopulmonary exercise testing in the prognosis of paroxysmal atrial fibrillation, in combination with echocardiographic indices and plasma biomarker values.

Atrial fibrillation (AF) is an enormous public health problem in the United States, affecting 2-5 million Americans and causing rapid heart beats, stroke, heart failure or death. In this project, the applicant will develop a novel framework to better understand human AF that builds on agreement between several concepts for the disease. The applicant will develop strategies to identify AF patients who will best respond to each of several therapies, to guide personalized therapy.

Atrial fibrillation is a serious public health issue that affects over 5 million Americans (Miyazaka, Circulation 2006) in whom it may cause skipped beats, dizziness, stroke and even death. Therapy for AF is currently suboptimal, in part because AF represents several disease states of which few have been delineated or used to successfully guide management. This study seeks to clarify this delineation of AF types using machine learning (ML).