ReviewReviews in cardiovascular medicine2024
Arrhythmogenic Cardiomyopathy: Current Updates and Future Challenges.
Review in Reviews in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Arrhythmogenic cardiomyopathy (ACM) epitomises a genetic anomaly hallmarked by a relentless fibro-fatty transmogrification of cardiac myocytes. Initially typified as a right ventricular-centric disease, contemporary observations elucidate a frequent occurrence of biventricular and left-dominant presentations. The diagnostic labyrinth of ACM emerges from its clinical and imaging properties, often indistinguishable from other cardiomyopathies. Precision in diagnosis, however, is paramount and unlocks the potential for early therapeutic interventions and vital cascade screening for at-risk individuals. Adherence to the criteria established by the 2010 task force remains the cornerstone of ACM diagnosis, demanding a multifaceted assessment incorporating electrophysiological, imaging, genetic, and histological data. Reflecting the evolution of our understanding, these criteria have undergone several revisions to encapsulate the expanding spectrum of ACM phenotypes. This review seeks to crystallise the genetic foundation of ACM, delineate its clinical and radiographic manifestations, and offer an analytical perspective on the current diagnostic criteria. By synthesising these elements, we aim to furnish practitioners with a strategic, evidence-based algorithm to accurately diagnose ACM, thereby optimising patient management and mitigating the intricate challenges of this multifaceted disorder.
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