Introduction: Current family screening approaches in dilated cardiomyopathy (DCM) depend on the presence or absence of a familial genetic variant, in which variant pathogenicity (i.e. benign or pathogenic) classification drives screening recommendations.
View Article and Find Full Text PDFBackground: founder variants cause hypertrophic cardiomyopathy leading to heart failure and malignant ventricular arrhythmias. Exercise is typically regarded as a risk factor for disease expression although evidence is conflicting. Stratifying by type of exercise may discriminate low- from high-risk activities in these patients.
View Article and Find Full Text PDFPurpose Of Review: This review aims to explore the emerging potential of artificial intelligence (AI) in refining risk prediction, clinical diagnosis, and treatment stratification for cardiomyopathies, with a specific emphasis on arrhythmogenic cardiomyopathy (ACM).
Recent Findings: Recent developments highlight the capacity of AI to construct sophisticated models that accurately distinguish affected from non-affected cardiomyopathy patients. These AI-driven approaches not only offer precision in risk prediction and diagnostics but also enable early identification of individuals at high risk of developing cardiomyopathy, even before symptoms occur.
J Cardiovasc Electrophysiol
December 2024
Arrhythmogenic cardiomyopathy (ACM) is a genetically heterogeneous inherited cardiomyopathy with an estimated prevalence of 1:5000-10 000 that predisposes patients to life-threatening ventricular arrhythmias (VA) and sudden cardiac death (SCD). ACM diagnostic criteria and risk prediction models, particularly for arrhythmogenic right ventricular cardiomyopathy (ARVC), the most common form of ACM, are typically genotype-agnostic, but numerous studies have established clinically meaningful genotype-phenotype associations. Early signs of ACM onset differ by genotype indicating the need for genotype-specific diagnostic criteria and family screening paradigms.
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