The post-stroke aftermath of patients suffering from atrial fibrillation (AF) is investigated. A total of 104 consecutive patients (80 with AF and 24 without AF) were referred to a rehabilitation centre and enrolled. They underwent a rehabilitative programme. Disability was assessed by using the Functional Independence Measure (FIM), at the time of admission, discharge and after 1 year. Death was more frequent over the 1-year interval in AF group. Ten patients died (42%), whereas in the non-AF group death occurred in 15 cases (19%). By using chi-square test, the difference was significant (P < 0.02). The logistic regression analysis indicated that the unique variable affecting the FIM at follow-up was the FIM at discharge (P < 0.0001). AF was not a significant variable in the equation. The result can be attributed to subject attrition in the AF group, as the most severe patients died and survivors could benefit from a better functional status.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1046/j.1468-1331.2003.00514.x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Cardiotoxic Effects Following CAR-T Cell Therapy: A Literature Review.
Curr Oncol Rep
January 2025
Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 111 East 210Th Street, Bronx, NY, 10461, USA.
Purpose Of Review: This paper reviewed the current literature on incidence, clinical manifestations, and risk factors of Chimeric Antigen Receptor T-cell (CAR-T) cardiotoxicity.
Recent Findings: CAR-T therapy has emerged as a groundbreaking treatment for hematological malignancies since FDA approval in 2017. CAR-T therapy is however associated with a few side effects, among which cardiotoxicity is of significant concern.
Myths and challenges around anticoagulation in atrial fibrillation: A practicing clinician's perspective.
Eur J Clin Invest
January 2025
Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
Factors Associated With the Development of Heart Failure Following Acute Coronary Syndrome: A Systematic Review and Meta-Analysis.
Cureus
December 2024
Internal Medicine, University of Health Sciences, Lahore, PAK.
Acute coronary syndrome (ACS) remains a major global health burden, encompassing a spectrum of conditions from unstable angina to acute myocardial infarction. Despite advancements in early detection and management, ACS is often complicated by the development of heart failure. This systematic review and meta-analysis aimed to identify factors associated with the development of heart failure following acute coronary syndrome.
View Article and Find Full Text PDFEnhancing Thromboembolic Risk Prediction in Non-valvular Atrial Fibrillation: The Critical Role of Transesophageal Echocardiography (TEE).
Cureus
December 2024
Cardiology, Avicenna Military Hospital, Marrakesh, MAR.
Introduction Atrial fibrillation (AF), the most common cardiac arrhythmia, poses challenges in predicting thromboembolic risk. While the CHADS-VASc (congestive heart failure, hypertension, age ≥ 75 years (doubled), type 2 diabetes mellitus, previous stroke, transient ischemic attack, or thromboembolism (doubled), vascular disease, age 65-74 years, and sex category) score remains essential, its limitations include failure to identify left atrial (LA) thrombus in some patients. Transesophageal echocardiography (TEE) provides superior detection of LA thrombi and thrombogenic factors compared to transthoracic echocardiography (TTE), improving risk stratification, especially in intermediate-risk groups.
View Article and Find Full Text PDFCardioattentionnet: advancing ECG beat characterization with a high-accuracy and portable deep learning model.
Front Cardiovasc Med
January 2025
Department of Cardiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
Introduction: The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.
Methods: The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!