Contemporary tools for detection and prediction of cardiac arrhythmias

Abstract

Cardiac arrhythmias are a common cause of morbidity and mortality, leading to frequent hospital admissions and a high burden on healthcare. Timely detection and treatment is essential to prevent further progression of the disease, complications, impaired quality of life, or death. In this thesis we explore promising contemporary tools for the detection and prediction of atrial and ventricular arrhythmias and their value in current treatment strategies in detail. The first part of this thesis mainly focuses on the detection and treatment of atrial and ventricular arrhythmias in patients with implantable cardioverter defibrillators (ICDs), including the subcutaneous ICD. Contemporary technologies to detect and predict atrial arrhythmias are explored in the second part of this thesis. For all these studies we used data of patients undergoing thoracoscopic surgery for atrial fibrillation in our tertiary referral center. In a number of studies we explored the use of artificial intelligence. In the final part we provide a general discussion of the studies in this thesis, including interpretation of the results and future perspectives