98%
921
2 minutes
20
Background: Atrial fibrillation is the most common cardiac arrhythmia, affecting 33.5 million patients globally. It is associated with increased morbidity, leading to significant clinical and economic burden. There exist only limited data in the Middle Eastern region from the existing registries. The goal of the FLOW-AF (atrial FibriLlatiOn real World management registry in the Middle East and Africa) registry is to evaluate the characteristics, treatment patterns, and clinical and economic outcomes associated with anticoagulation among patients newly diagnosed with nonvalvular atrial fibrillation in Egypt, Lebanon, the Kingdom of Saudi Arabia, and the United Arab Emirates.
Methods: This study will be a multicountry, multicenter, prospective observational registry aiming to enroll 1446 newly diagnosed nonvalvular atrial fibrillation patients at more than 20 sites across the four countries. During the recruitment period, patients will be included if they were newly diagnosed with nonvalvular atrial fibrillation and had initiated treatment for the prevention of stroke/systemic embolism. Patient data will be assessed prospectively at 6 and 12 months from their enrollment date. Demographics, clinical characteristics, antithrombotic treatments received, clinical outcomes, adverse events, healthcare resource utilization, and direct costs associated with management of nonvalvular atrial fibrillation will be collected and analyzed overall, by country, and by groups created based on treatment, demographics, and clinical characteristics, medical history and risk factors.
Conclusion: The FLOW-AF registry will provide information on the uptake of oral anticoagulants, treatment patterns, clinical outcomes, and healthcare utilization and costs among newly diagnosed nonvalvular atrial fibrillation patients in the Middle Eastern region.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.2459/JCM.0000000000001007 | DOI Listing |
Am J Med Sci
September 2025
The Ruth and Bruce Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, Israel; Department of Internal Medicine, Lady Davis Carmel Medical Center, Haifa, Israel.
Objective: Multifocal atrial tachycardia (MAT), characterized by an irregularly irregular rhythm, is often regarded as a clinical imitator of atrial fibrillation (AF). We aimed to evaluate the prevalence of MAT misclassification as AF in the emergency department (ED) setting.
Methods: A retrospective analysis of 1,828 ECGs from patients discharged with AF diagnoses over five years.
Environ Res
September 2025
Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
Background: Fine particulate matter (PM) has been previously linked to cardiovascular diseases (CVDs). PM is a mixture of components, each of which has its own toxicity profile which are not yet well understood. This study explores the relationship between long-term exposure to PM components and hospital admissions with CVDs in the Medicare population.
View Article and Find Full Text PDFClin Neurol Neurosurg
September 2025
Department of Neurology, UPMC, Pittsburgh, PA, USA. Electronic address:
Background: Final infarct volume (FIV) is a strong predictor of stroke outcomes. Although smaller FIV are associated with better outcomes, many patients fail to achieve functional independence. We aimed to identify poor outcome predictors in patients with anterior large vessel occlusion stroke (LVOS) who underwent mechanical thrombectomy (MT) and had small FIV.
View Article and Find Full Text PDFJ Physiol
September 2025
Undergraduate Medical Education, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
J Am Coll Cardiol
August 2025
Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, California, USA. Electronic address:
Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.
Objectives: The purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.