Left bundle branch block may be due to conduction system degeneration or a reflection of myocardial pathology. Left bundle branch block may also develop following aortic valve disease or cardiac procedures. Patients with heart failure with reduced ejection fraction and left bundle branch block may respond positively to cardiac resynchronization therapy. Lead placement via the coronary sinus is the mainstay approach of cardiac resynchronization therapy. However, other options, including physiological pacing, are being explored. In this review, we summarize the salient pathophysiologic and clinical aspects of left bundle branch block, as well as current and future strategies for management.
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http://dx.doi.org/10.1161/CIRCEP.119.008239 | DOI Listing |
JACC Clin Electrophysiol
January 2025
Cardiac Pacing Unit, Department of Cardiology, University Hospital of Geneva, Geneva, Switzerland. Electronic address:
Front Cardiovasc Med
January 2025
Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
Background: Painful left bundle branch block (LBBB) syndrome is an uncommon disease that is defined as intermittent episodes of angina associated with simultaneous LBBB changes on an electrocardiogram (ECG) with the absence of flow-limiting coronary artery disease or ischemia on functional testing. Vasovagal syncope (VVS) is the most common cause of syncope and can be provoked by sublingual nitroglycerin (NTG). Herein, we report a case of painful LBBB syndrome complicated with VVS, which was misdiagnosed as acute coronary syndrome and cardiogenic shock.
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January 2025
Department of Cardiology and Nephrology, Mie University Graduate School of Medicine.
Circ Arrhythm Electrophysiol
January 2025
Department of Cardiovascular Medicine (S.H., T.W., N.Z., J.W.).
Heliyon
January 2025
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, Ancona, 60131, Italy.
Background: Deep-learning applications in cardiology typically perform trivial binary classification and are able to discriminate between subjects affected or not affected by a specific cardiac disease. However, this working scenario is very different from the real one, where clinicians are required to recognize the occurrence of one cardiac disease among the several possible ones, performing a multiclass classification. The present work aims to create a new interpretable deep-learning tool able to perform a multiclass classification and, thus, discriminate among several different cardiac diseases.
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