scholarly journals Survey of class Ic antiarrhythmic agents for incurable supraventricular arrhythmias. Efficacy for drug resistent atrial fibrillation and atrial flutter.

1995 ◽  
Vol 15 (4) ◽  
pp. 290-296
Author(s):  
Kaoru Sugi ◽  
Yoshihisa Enjoji ◽  
Takanori Ikeda ◽  
Masashi Kasao ◽  
Seishiro Matsukawa ◽  
...  
ESC CardioMed ◽  
2018 ◽  
pp. 2050-2050
Author(s):  
Gregory Y. H Lip

The precise description of the epidemiology of supraventricular tachycardias is difficult as the published data often has poor differentiation between atrial fibrillation, atrial flutter, and other supraventricular arrhythmias. In contrast to the extensive epidemiology on atrial fibrillation, a specific focus on supraventricular tachycardia population epidemiology is sparse, especially in the general population (rather than observational cohorts from specialized centres).


2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
T S Kovalchuk ◽  
E V Yakovleva ◽  
S G Fetisova ◽  
T L Vershinina ◽  
T M Pervunina ◽  
...  

Abstract Introduction Emery-Dreifuss muscular dystrophy (EDMD) is an inherited muscle dystrophy often accompanied by cardiac abnormalities in the form of supraventricular arrhythmias, conduction defects, sinus node dysfunction. Cardiac phenotype typically arises years after skeletal muscle presentations, though, can be severe and life-threatening. The disease usually manifests during the third decade of life with elbow joint contractions and progressive muscle weakness and atrophy. Objective To present our clinical experience of diagnosis and treatment of arrhythmias in children with Emery-Dreifuss muscular dystrophy Materials and methods We enrolled 5 patients with different forms of EDMD (X-linked and autosomal dominant) linked to the mutations in EMD and LMNA genes, presented with early onset of cardiac abnormalities and no leading skeletal muscle phenotype. The predominant forms of cardiac pathology were atrial flutter, atrial fibrillation and conduction disturbances that progress over time. Clinical examination included physical examination, 12-lead electrocardiography, Holter ECG monitoring (HM), transthoracic echocardiography, neurological examination and biochemical and hormone tests. Also we performed CMR, electrophysiological study (EPS), treadmill test of some patients. One patient underwent an endomyocardial biopsy to exclude inflammatory heart disease. Target sequencing was performed using a panel of 108 or 172 genes Results We observed five patients with EDMD and cardiac debut during first-second decades of life: 3 with 1st subtype (variants in EMD gene) and 2 with 2nd subtype (variants in LMNA gene). All patients were males. The mean age of cardiac manifestation was 13,2±3,11 (from 9 to 16 y.o.). The mean follow-up period was 7,4±2,6 years. All patients presented with sinus node dysfunction and four out of five with AV conduction abnormalities. The leading arrhythmic phenotypes included various types of supraventricular arrhythmias: multifocal atrial tachycardia (AT) (n=4), premature atrial captures (PACs) (n=4), atrial flutter, (AF) (n=3), atrial fibrillation (AFib) (n=3) and AV nodal recurrent tachycardia (AVRNT). Heart rhythm disorders were the first manifestation in all three patients with 1st EDMD subtype. Radiofrequency ablation was performed in 2 patients, one of them received permanent pacemaker implantation. Conclusions In conclusion, while being the rare cases, heart rhythm disorders can represent the first and for a long time, the only clinical symptom of EDMD even in the pediatric group of patients. Therefore, thorough laboratory and neurological screening along with genetic studies, are of importance in each pediatric patient presenting with complex heart rhythm disorders of primary supraventricular origin to exclude EDMD or other neuromuscular disorders. FUNDunding Acknowledgement Type of funding sources: None.


2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Solis Cancino ◽  
A.D Pacheco Bouthillier ◽  
L.A Moreno Ruiz

Abstract Background Supraventricular arrhythmias represent a diagnostic challenge. Its prevalence and causes are not well established, given the impossibility to differentiate between all different types of supraventricular tachycardias (SVT). There are several supraventricular arrhythmias, but we focus on: 1) Atrial tachycardia (AT) 2) Junctional tachycardia (JT) 3) Atrial fibrillation (AF) 4) Atrial flutter (AA) 5) Atrioventricular nodal reentrant tachycardia (AVNRT), and 6) Atrioventricular reentrant tachycardia (AVRT). The electrocardiographic diagnosis is based on the presence of P-waves, its morphology and relationship with the QRS complex, and the relationship between the atrial and ventricular frequency. Purpose The purpose of this study was to create a helpful clinical tool that could serve the physician as a guide to determine a diagnosis and initial treatment. Additionally, we wanted to establish the sensitivity and specificity of the algorithm. Methods It is a diagnostic test study. We include 190 electrocardiograms of different SVT of patients undergoing electrophysiological studies. The data consists of 760 observations from two different readings of the electrocardiograms. Results 104 of 112 AF, were correctly identified using the algorithm, with a sensitivity and specificity of 92.9% and 99.1%, respectively (95% CI: 0.86–0.96). 76 of 760 were AA, and 62 were correctly diagnosed, with a sensitivity and specificity of 81.6% and 95.5%, respectively (95% CI 0.71–0.88). 50 of the 72 AT were correctly classified, with a sensitivity of 69.4% and specificity of 97.4% (95% CI 0.58–0.78). 99 of 152 AVNRT were identified with a sensitivity and specificity of 64.5% and 87%, respectively (95% CI 0.84–0.89). 254 of 344 AVRT were diagnosed correctly with a sensitivity of 73.8% and specificity of 88.2% (95% CI 0.68–0.78). Finally, 1 of 4 JT were identified, with a sensitivity and specificity of 25% and 99.1% respectively (95% CI 0.04–0.69). Conclusion The algorithm is an excellent diagnostic tool to identify atrial flutter, atrial fibrillation and atrioventricular reentrant tachycardia. SVT algorithm Funding Acknowledgement Type of funding source: None


2005 ◽  
Vol 10 (2) ◽  
pp. 311-322 ◽  
Author(s):  
Redi Pecini ◽  
Hanne Elming ◽  
Ole Dyg Pedersen ◽  
Christian Torp-Pedersen

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