Christos Kaskoutis

Improving clinical prediction of sudden cardiac death is a crucial step in the management of patients with hypertrophic cardiomyopathy. However, finding the optimal method for risk evaluation has been challenging, given the complexity and the wide variation in clinical phenotypes. This is particularly important, as these patients are often of younger age and defibrillator implantation is associated with a low but tangible long-term risk of adverse events. A number of risk factors, including degree of hypertrophy, presence of syncope and family history of sudden cardiac death, have typically been considered to indicate a higher risk. The European risk score for prediction of sudden cardiac death is widely used; however, it may not apply well in patients with specific forms of the condition, such as those with extreme hypertrophy. Increasing evidence suggests that the presence and extent of myocardial fibrosis assessed with cardiac magnetic resonance imaging should be considered in clinical decision-making. Some research suggests that integrating electrophysiological studies into traditional risk assessment models may further optimize risk prediction and significantly improve accuracy in detecting high risk patients. Novel cardiac imaging techniques, better understanding of the genetic substrate and artificial intelligence-based algorithms may prove promising for risk refinement. The present review article provides an updated and in-depth viewpoint.