Why the study?
Cardiomyopathy classification remains challenging due to extraordinary clinical, morphological, and genetic heterogeneity, necessitating updated frameworks as diagnostic technologies evolve.
Cardiomyopathy classification serves as an initial step for precision diagnosis but must evolve into dynamic models integrating genetics, imaging, and molecular research.
Supports evolution beyond rigid phenotypes in cardiomyopathy classification; leaves open validation of multiparametric pathways in prospective studies.
Background Cardiomyopathy classification remains challenging due to their extraordinary clinical, morphological, and genetic heterogeneity. As diagnostic technologies evolve, so too must the frameworks by which we conceptualize and communicate these diseases. Content Since the 2008 ESC morpho-functional classification and the genotype–phenotype integrated MOGE(S) system proposed in 2013, substantial advances in imaging and genetics have prompted a revised 2023 ESC phenotype-first model. The five current phenotypes—dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy (RCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and non-dilated left ventricular cardiomyopathy (NDLVC)—capture major morphological expressions but display extensive overlap, especially among DCM, ARVC, and NDLVC. This overlap underscores the need for dynamic, multiparametric diagnostic pathways and individualized interpretation. Summary Cardiomyopathy classification should serve as a starting point rather than an endpoint, guiding precision diagnosis and informing clinical and therapeutic decision-making. The future of cardiomyopathy taxonomy will require flexible models capable of integrating continuous advances in genetics, imaging, and molecular research.
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Perotto et al. (2026) conducted a review in Cardiomyopathies. Cardiomyopathy classification frameworks was evaluated.
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