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Catheter ablation is the first-line treatment for atrial fibrillation. Although the efficacy and safety of this procedure have been reported in older patients, they might diminish with age. Therefore, this study aimed to determine the safety and effectiveness of atrial fibrillation ablation in patients aged ≥80 years. We retrospectively analyzed the features of the catheter ablation and the subsequent clinical course and outcomes of 100 patients with atrial fibrillation aged ≥80 years who underwent ablation between July 2019 and December 2021 at Tosei General Hospital, Seto, Aichi, Japan. The average duration of atrial fibrillation was 6.0 ± 9.5 months, and 83% of the patients were symptomatic. Approximately 30% of patients developed heart failure, with 15% requiring hospitalization within one year before ablation. After ablation, 93% of patients were atrial fibrillation-free, and none required postoperative hospitalization due to heart failure. However, several complications have been observed, including cardiac tamponade, hematoma at the access site, and postoperative bradycardia. Notably, an enlarged left atrial diameter before ablation is a predictor of complications. In patients aged ≥80 years, atrial fibrillation ablation therapy demonstrated a high non-recurrence rate and may alter the progression of heart failure. Although the incidence of complications was relatively low, caution should be exercised when older patients with enlarged left atrial diameters undergo atrial fibrillation ablation.
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http://dx.doi.org/10.18999/nagjms.87.1.37 | 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.