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Exploring the Current Status of Risk Stratification in Hypertrophic Cardiomyopathy: From Risk Models to Promising
Alexandros Kasiakogias1, Christos Kaskoutis1, Christos-Konstantinos Antoniou1
1First Department of Cardiology, School of Medicine, National and Kapodistrian University of Athens, Hippokration General Hospital, 11527 Athens, Greece.
Insights
Predicting sudden cardiac death in hypertrophic cardiomyopathy is challenging. New methods like cardiac MRI and AI may improve risk evaluation for these patients.
Area of Science:
- Cardiology
- Medical Imaging
- Genetics
Background:
- Sudden cardiac death (SCD) prediction in hypertrophic cardiomyopathy (HCM) is critical.
- Current risk models may not suit all HCM phenotypes, especially those with extreme hypertrophy.
- Defibrillator implantation carries long-term risks, necessitating accurate risk stratification.
Purpose of the Study:
- To review current and emerging strategies for improving SCD risk prediction in HCM.
- To highlight the limitations of existing risk assessment tools.
- To explore novel approaches for more accurate risk stratification.
Main Methods:
- Review of existing literature on SCD risk factors in HCM.
- Analysis of the role of cardiac magnetic resonance imaging (CMR) in assessing myocardial fibrosis.
- Exploration of electrophysiological studies (EPS) and artificial intelligence (AI) in risk prediction.
Main Results:
- Traditional risk factors (hypertrophy, syncope, family history) have limitations.
- Myocardial fibrosis on CMR is an increasingly recognized predictor of SCD.
- Integrating advanced imaging, genetics, and AI shows promise for refined risk assessment.
Conclusions:
- Optimizing SCD risk prediction in HCM requires moving beyond traditional factors.
- Cardiac MRI and electrophysiological studies offer valuable insights.
- Future research integrating novel imaging, genetics, and AI algorithms is essential for improved patient management.
Abstract:
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.
