Herzschrittmachertherapie und Elektrophysiologie最新文献

The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes a comprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context.

New smart devices that have the potential to support the health and well-being of their owners have become available. In particular, smart watches are able to identify a fall by the person who is wearing the watch and report it to pre-defined contacts and the local emergency control center. Falls in older people are common and only rarely caused by malignant cardiac arrhythmia. The case of an elderly male whose smart watch automatically reported his fall due to ventricular fibrillation to the local emergency control center is described. Through the intervention of the wearer's device, the activated dispatcher called the patient's wife, who found her husband lying unresponsive on the floor. Emergency medical services responded immediately and were able to successfully resuscitate the patient. A hospital discharge without any long-term complications was achieved.

A 2:1 atrioventricular (AV) block can occur anywhere within the conduction system, and noninvasive measurements may not always predict the exact site of the block. Although localization of the block is critical for deciding any treatment, patients should also be carefully questioned about symptoms both at rest and during exertion. A case of 2:1 AV block that was symptomatic only during exertion, appeared infranodal by noninvasive diagnostic methods, but was found to be intranodal on electrophysiological study is reported.

Background: The digital transformation in medicine, particularly in technology-orientated areas such as rhythmology, is leading to a rapid change in diagnostic and therapeutic options. Digital skills are helpful and need to keep up with this pace of change.

Research question: Which digital technologies and resources with rhythmological relevance play a role today and in the future?

Methods: Review of the various digital technologies for rhythm detection and monitoring, as well as current digital resources for training and education.

Results: Rhythm detection and monitoring can be optimized with smart devices and telemedicine, while digital platforms such as social media and virtual reality offer new perspectives in the training of rhythmology specialists.

Conclusion: Acquiring digital skills will be the basis for future work in rhythmology.

The implantation of electrodes for cardiac implantable electronic devices (CIED) requires profound technical understanding and precise execution. The positioning of electrodes in the right ventricle and atrium has significant implications for patient safety and the effectiveness of CIED therapy. Particular focus is given to the distinction between apical and septal stimulation in ventricular positioning. Based on current data, this article provides a practice-oriented guide that leads implanters through the individual steps of electrode positioning. The implantation of electrodes for physiological stimulation (cardiac resynchronization therapy, CRT, and conduction system pacing, CSP) is not addressed in this article.

A case of successful catheter ablation of paroxysmal atrial fibrillation and atrial tachycardia is reported. After pulmonary vein isolation, atrial tachycardia was induced by the use of isoproterenol and burst pacing from the catheter in the right atrium. An attempt was made to create a three-dimensional (3D) map of the atrial tachycardia, but the atrial tachycardia was terminated in the middle of the mapping. The 3D map was insufficient but indicated that the superior vena cava was involved in the circuit. When the intracardiac electrograms were reviewed, it was found that the atrial tachycardia was initiated with orthodromic capture of superior vena cava potentials and it was considered that the atrial tachycardia involved the superior vena cava-right atrium junction. Accordingly, superior vena cava isolation was performed. After that, atrial fibrillation and atrial tachycardias were not induced by the use of isoproterenol and burst pacing. In this case, an intracardiac electrogram at the time of induction of the tachycardia was helpful for understanding the circuit of the tachycardia.

Cardiac arrhythmias remain a major cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as a promising strategy to realize the vision of precision cardiology. While there is no uniform definition of a digital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides a short overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable a significant precision medicine-based advancement in cardiac arrhythmia management.