Prevalence of Primary Aldosteronism in Atrial Fibrillation
Atrial FibrillationPrimary AldosteronismThis is an observational prospective cross-sectional study, investigating the prevalence of primary aldosteronism in patients with atrial fibrillation.
Study in Atrial Fibrillation (AF) Patients at High Risk of Stroke
Atrial FibrillationThe GARDENIA registry will collect real-world clinical data on the anticoagulant strategies in patients with AF at elevated risk of stroke but also elevated risk of bleeding.
A Prospective Single Center Pilot Study Using the ECGenius System to Collect Electrogram Data to...
Persistent Atrial FibrillationCardiac ArrhythmiaTo 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.
WHOOP Abnormal Rhythm Notification
Atrial FibrillationThe purpose of this protocol is to assess the sensitivity and specificity of a photoplethysmography (PPG)-based algorithm for the detection of atrial fibrillation as compared to a gold-standard assessment (wearable ECG patch) among a population of individuals with known atrial fibrillation and without known atrial fibrillation over a 7-day study period.
Clinical Cohorts for Validation of New Digital Biomarkers
Atrial FibrillationThe 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.
Middle East African Registry Women CardioVascular Disease
Heart FailureAtrial Fibrillation2 moreThe 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.
Ergospirometry in Paroxysmal Atrial Fibrillation Prognosis
Atrial FibrillationAtrial ArrhythmiaAn 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.
Implantable Cardiac Monitor to Detect Atrial Fibrillation in Patients With MINOCA
MINOCAAtrial FibrillationMyocardial 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 Dynamics of Human Atrial Fibrillation
Atrial FibrillationArrhythmias1 moreAtrial 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.
Machine Learning in Atrial Fibrillation
Atrial FibrillationArrhythmias1 moreAtrial 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).