Atrial Appendage Transcriptional Profile in Patients with Atrial Fibrillation with Structural Heart Diseases

2006 ◽  
Vol 1091 (1) ◽  
pp. 205-217 ◽  
Author(s):  
MARIA S. KHARLAP ◽  
ANGELICA V. TIMOFEEVA ◽  
LUDMILA E. GORYUNOVA ◽  
GEORGE L. KHASPEKOV ◽  
SERGEY L. DZEMESHKEVICH ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Diseases ◽  
Transcriptional Profile ◽  
Atrial Appendage ◽  
Structural Heart Diseases

Inherited Structural Heart Diseases With Potential Atrial Fibrillation Occurrence

2015 ◽  
Vol 27 (2) ◽  
pp. 242-252 ◽  
Author(s):  
ROBERTA MANUGUERRA ◽  
SERGIO CALLEGARI ◽  
DOMENICO CORRADI
Keyword(s):  
Atrial Fibrillation ◽  
Heart Diseases ◽  
Structural Heart Diseases

scholarly journals Flecainide in Ventricular Arrhythmias: From Old Myths to New Perspectives

2021 ◽  
Vol 10 (16) ◽  
pp. 3696
Author(s):  
Carlo Lavalle ◽  
Sara Trivigno ◽  
Giampaolo Vetta ◽  
Michele Magnocavallo ◽  
Marco Valerio Mariani ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Disease ◽  
Ventricular Arrhythmias ◽  
Heart Diseases ◽  
Structural Heart Disease ◽  
Rhythm Control ◽  
Current Evidence ◽  
Structural Heart Diseases ◽  
Therapeutic Tools ◽  
Sinus Rhythm Maintenance

Flecainide is an IC antiarrhythmic drug (AAD) that received in 1984 Food and Drug Administration approval for the treatment of sustained ventricular tachycardia (VT) and subsequently for rhythm control of atrial fibrillation (AF). Currently, flecainide is mainly employed for sinus rhythm maintenance in AF and the treatment of idiopathic ventricular arrhythmias (IVA) in absence of ischaemic and structural heart disease on the basis of CAST data. Recent studies enrolling patients with different structural heart diseases demonstrated good effectiveness and safety profile of flecainide. The purpose of this review is to assess current evidence for appropriate and safe use of flecainide, 30 years after CAST data, in the light of new diagnostic and therapeutic tools in the field of ischaemic and non-ischaemic heart disease.

scholarly journals Distribution of First-Detected Atrial Fibrillation Patients Without Structural Heart Diseases in Symptom Classifications

2012 ◽  
Vol 76 (4) ◽  
pp. 1020-1023 ◽  
Author(s):  
Keitaro Senoo ◽  
Shinya Suzuki ◽  
Koichi Sagara ◽  
Takayuki Otsuka ◽  
Shunsuke Matsuno ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Diseases ◽  
Structural Heart Diseases

scholarly journals Identifying patients with atrial fibrillation during sinus rhythm on ECG: confirming the utility of artificial intelligence algorithm in a small-scale cohort without structural heart diseases

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
S Suzuki ◽  
J Motogi ◽  
W Matsuzawa ◽  
T Takayanagi ◽  
T Umemoto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Time Course ◽  
Heart Diseases ◽  
Validation Dataset ◽  
Small Scale ◽  
Internal Validation ◽  
Testing Dataset ◽  
Structural Heart Diseases

Abstract Background Detection of atrial fibrillation (AF) out of electrocardiograph (ECG) on sinus rhythm (SR) using artificial intelligence (AI) algorithm has been widely studied within recent couple of years. Generally, it is believed that a huge number of ECGs are necessary for developing an AI-enabled ECG to be adequate to correspond to a lot of minor variations of ECGs. For example, structural heart diseases have typical ECG characteristics, but they could be a noise for the purpose of detecting the small signs of electrocardiographic signature of AF. We hypothesized that when patients with structural heart diseases are excluded, AI-enabled ECG for identifying patients with AF can be developed with a small number of ECGs. Methods We developed an AI-enabled ECG using a convolutional neural network to detect the electrocardiographic signature of AF present during normal sinus rhythm (NSR) using a digital, standard 10-second, 12-lead ECGs. We included all patients who newly visited the Cardiovascular Institute with at least one NSR ECG between Feb 1, 2010, and March 31, 2018. We classified patients with at least one ECG with a rhythm of AF as positive for AF (AF label) and others as negative for AF (SR label). We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operating characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the sensitivity, specificity, F1 score, and accuracy with two-sided 95% confidence intervals (CIs). Results We totally included 19170 patients with 12-lead ECG. After excluding patients with structural heart diseases, 12825 patients with NSR ECGs at the initial visit were identified (1262 were clinically diagnosed as AF anytime during the time course and 11563 were never diagnosed as AF). Of 11563 non-AF patients, 1818 patients who were followed over 1095 days were selected for the analysis with the SR label, to secure the robustness for maintaining SR. Of 1262 AF patients, 251 patients were selected for the analysis with the AF label, of whom a NSR ECG within 31 days before or after the index AF ECG (the first AF ECG during the time course) could be obtained. In the patients with AF label, the NSR ECG of which the date was the nearest to the index AF ECG was selected for the analysis. The AI-enabled ECG showed an AUC of 0.88 (0.84–0.92) with sensitivity 81% (72–88), specificity 80% (77–83), F1 score 50% (43–57), and overall accuracy 80% (78–83). Conclusion An AI-enabled ECG acquired during NSR allowed identification of patients with AF in a small population without structural heart diseases. FUNDunding Acknowledgement Type of funding sources: None.

HEART COMPLICATIONS IN HYPERTHYROIDISM

2011 ◽  
pp. 7-17
Author(s):  
Hai Thuy Nguyen ◽  
Anh Vu Nguyen
Keyword(s):  
Atrial Fibrillation ◽  
Blood Pressure ◽  
Heart Rate ◽  
Heart Diseases ◽  
Systolic Dysfunction ◽  
Oxygen Demand ◽  
Left Ventricular ◽  
Cardiac Contraction ◽  
Cardiac Complications ◽  
Cardiac Symptoms

Thyroid hormone increases the force of the contraction and the amount of the heart muscle oxygen demand. It also increases the heart rate. Due to these reasons, the work of the heart is greatly increased in hyperthyroidism. Hyperthyroidism increases the amount of nitric oxide in the intima, lead them to be dilated and become less stiff. Cardiac symptoms can be seen in anybody with hyperthyroidism, but can be particularly dangerous in whom have underlying heart diseases. Common symptoms include: tachycardia and palpitations. Occult hyperthyroidism is a common cause of an increased heart rate at rest and with mild exertion. Hyperthyroidism can also produce a host of other arrhythmias such as PVCs, ventricular tachycardia and especially atrial fibrillation. Left ventricular diastolic dysfunction and systolic dysfunction, Mitral regurgitation and mitral valve prolapsed are heart complications of hyperthyroism could be detected by echocardiography. The forceful cardiac contraction increases the systolic blood pressure despite the increased relaxation in the blood vessels reduces the diastolic blood pressure. Atrial fibrillation, atrial enlargement and congestive heart failure are important cardiac complications of hyperthyroidism. An increased risks of stroke is common in patients with atrial fibrillation. Graves disease is linked to autoimmune complications, such as cardiac valve involvement, pulmonary arterial hypertension and specific cardiomyopathy. Worsening angina: Patients with coronary artery disease often experience a marked worsening in symptoms with hyperthyroidism. These can include an increase in chest pain (angina) or even a heart attack.

Sign in / Sign up

Export Citation Format

Share Document