[Perioprocedural mortality after ICD implantation]

Do Implantable Cardioverter-Defibrillators Prevent Sudden Cardiac Death in End-Stage Renal Disease Patients on Dialysis?

Chronic kidney disease patients appear to be predisposed to heart rhythm disorders, including atrial fibrillation/atrial flutter, ventricular arrhythmias, and supraventricular tachycardias, which increase the risk of sudden cardiac death. The pathophysiological factors underlying arrhythmia and sudden cardiac death in patients with end-stage renal disease are unique and include timing and frequency of dialysis and dialysate composition, vulnerable myocardium, and acute proarrhythmic factors triggering asystole. The high incidence of sudden cardiac deaths suggests that this population could benefit from implantable cardioverter-defibrillator therapy. The introduction of implantable cardioverter-defibrillators significantly decreased the rate of all-cause mortality; however, the benefits of this therapy among patients with chronic kidney disease remain controversial since the studies provide conflicting results. Electrolyte imbalances in haemodialysis patients may result in ineffective shock therapy or the appearance of non-shockable underlying arrhythmic sudden cardiac death. Moreover, the implantation of such devices is associated with a risk of infections and central venous stenosis. Therefore, in the population of patients with heart failure and severe renal impairment, periprocedural risk and life expectancy must be considered when deciding on potential device implantation. Harmonised management of rhythm disorders and renal disease can potentially minimise risks and improve patients' outcomes and prognosis.

Big Data in electrophysiology

The quantity of data produced and captured in medicine today is unprecedented. Technological improvements and automation have expanded the traditional statistical methods and enabled the analysis of Big Data. This has permitted the discovery of new associations with a granularity that was previously hidden to human eyes. In the first part of this review, the authors would like to provide an overview of basic Machine Learning (ML) principles and techniques in order to better understand their application in recent publications about cardiac arrhythmias. In the second part, ML-enabled advances in disease detection and diagnosis, outcome prediction, and novel disease characterization in topics like electrocardiography, atrial fibrillation, ventricular arrhythmias, and cardiac devices are presented. Finally, the limitations and challenges of applying ML in clinical practice, such as validation, replication, generalizability, and regulatory issues, are discussed. More carefully designed studies and collaborations are needed for ML to become feasible, trustworthy, accurate, and reproducible and to reach its full potential for patient-oriented precision medicine.