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Left Ventricular Trabeculations in Athletes: Epiphenomenon or Phenotype of Disease?

PURPOSE OF REVIEW: Excessive trabeculation attracting a diagnosis of left ventricular noncompaction cardiomyopathy (LVNC) has been reported in ostensibly healthy athletes. This review aims to explain why this occurs and whether this represents a spectrum of athletic physiological remodelling or unma...

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Detalles Bibliográficos
Autores principales: Abela, Mark, D’Silva, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209014/
https://www.ncbi.nlm.nih.gov/pubmed/30367273
http://dx.doi.org/10.1007/s11936-018-0698-8
Descripción
Sumario:PURPOSE OF REVIEW: Excessive trabeculation attracting a diagnosis of left ventricular noncompaction cardiomyopathy (LVNC) has been reported in ostensibly healthy athletes. This review aims to explain why this occurs and whether this represents a spectrum of athletic physiological remodelling or unmasking of occult cardiomyopathy. RECENT FINDINGS: Genetic studies have yet to identify a dominant mutation associated with the LVNC phenotype and reported gene mutations overlap with many distinct cardiomyopathies and ion channel disorders, implying that the phenotype is shared across different genetic conditions. Large contemporary cohort studies indicate that current LVNC imaging criteria are oversensitive and not predictive of adverse clinical outcomes. SUMMARY: The majority of excessive LV trabeculation, as assessed by current quantification methods, is not due to cardiomyopathy but forms part of the normal continuum in health with potential contributions from cardiac remodelling processes. The study of rare, severe LVNC phenotypes may yield insights into an underlying molecular pathogenesis but in the absence of a universally accepted definition, contamination with aetiologically distinct conditions expressing a similar phenotype will remain an issue. Automated, objective quantification of trabeculation will help to define the normal distribution using big data without the constraint of wide interobserver variation.