Key result
AI and advanced imaging may personalize SCD risk stratification in HCM beyond established models.
Why the study?
Sudden cardiac death risk stratification in hypertrophic cardiomyopathy remains challenging in specific subgroups, requiring evaluation of additional indicators and modern methodologies for more personalized assessment.
This review highlights current approaches, challenges in specific subgroups, and future directions including artificial intelligence for sudden cardiac death risk stratification in patients with hypertrophic cardiomyopathy.
Current SCD risk models should guide HCM decisions; leaves open whether AI and advanced imaging will enable personalization.
Over the past decade, knowledge about the risk of sudden cardiac death (SCD) in patients with hypertrophic cardiomyopathy (HCM) has advanced significantly. A standard well-recognized approach to risk stratification is based on the fundamental risk factors and SCD risk models that should be incorporated into the shared decision-making process. More detailed analysis including additional indicators, such as reduced left ventricular systolic function, the presence of late gadolinium enhancement, or in some cases genetic variants, may provide valuable insights for intermediate-risk patients, enabling more personalized diagnosis and treatment. Risk stratification remains challenging in specific groups, such as patients who have undergone septal reduction therapy, those taking mavacamten, or those with phenocopies of HCM. The advancement of modern methodologies, including multifactorial approaches supported by artificial intelligence algorithms, offers hope for more precise and individualized SCD risk assessment in HCM patients.
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Norrish et al. (2025) conducted a review in Hypertrophic cardiomyopathy. Sudden cardiac death risk stratification was evaluated. Risk stratification for sudden cardiac death in hypertrophic cardiomyopathy relies on established risk models, but future approaches incorporating artificial intelligence and advanced imaging may enable more personalized assessment.
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