Background: Cardiac magnetic resonance (CMR) is a component of the revised Task Force Criteria (rTFC) for the diagnosis of arrhythmogenic right ventricular cardiomyopathy (ARVC). However, its diagnostic value in a pediatric population is unknown.
Objectives: This study examined the contribution of CMR to diagnosing ARVC using the rTFC in a pediatric population.
Methods: Clinical CMR studies of 142 pediatric patients evaluated for ARVC between 2005 and 2009 were reviewed. Patients were categorized into "definitive," "borderline," "possible," or "no" ARVC diagnostic groups based on the rTFC. The extent to which each element of the rTFC contributed to diagnosing ARVC was determined using a c-statistics model.
Results: A total of 23 (16%), 32 (23%), 37 (26%), and 50 (35%) patients had definite, borderline, possible, and no ARVC, respectively, applying the rTFC. The prevalence of regional wall motion abnormalities in these groups was 83%, 53%, 22%, and 16%, respectively (p < 0.001). By CMR, right ventricular end-diastolic volumes were 118 ± 31 cc/m², 108 ± 22 cc/m², 94 ± 14 cc/m², and 92 ± 18 cc/m², respectively (p < 0.001). Right ventricular fatty infiltration and fibrosis were detected in only 1 and 3 patients, respectively, all of whom had definitive ARVC. Of all rTFC major criteria, CMR had the largest c-statistic decline (c = -0.163). Eleven of the 23 patients (48%) with definite ARVC would not have been in this group if CMR had not been performed.
Conclusions: CMR parameters are important contributors to a diagnosis of ARVC in children, using the rTFC. Fatty infiltration and myocardial fibrosis provide limited value in children and adolescents.
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http://dx.doi.org/10.1016/j.jacc.2014.12.041 | DOI Listing |
J Cardiovasc Dev Dis
December 2024
Institute of Cardiovascular Sciences, University of Birmingham, Birmingham B15 2TT, UK.
Background Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare genetic disorder associated with an elevated risk of life-threatening arrhythmias and progressive ventricular impairment. Risk stratification is essential to prevent major adverse cardiac events (MACE). Our study aimed to investigate the incremental value of strain measured by two-dimensional speckle-tracking echocardiography in predicting MACE in ARVC patients compared to conventional echocardiographic parameters.
View Article and Find Full Text PDFJACC Case Rep
November 2024
SingHealth Duke-NUS Genomic Medicine Centre, Duke NUS Medical School, Singapore.
Hypertrophic cardiomyopathy (HCM) and arrhythmogenic right ventricular cardiomyopathy (ARVC) are phenotypically distinct inherited cardiac diseases. This case report presents a woman aged 51 years with coinheritance of pathogenic/likely pathogenic variants of the β-myosin heavy chain ( p.Glu924Lys) and plakophilin 2 ( p.
View Article and Find Full Text PDFJ Clin Med
November 2024
Collegium Medicum-Faculty of Medicine, WSB University, 41-300 Dabrowa Gornicza, Poland.
Cardiomyopathies represent a diverse group of heart muscle diseases marked by structural and functional abnormalities that are not primarily caused by coronary artery disease. Recent advances in non-invasive imaging techniques, such as echocardiography, cardiac magnetic resonance, and computed tomography, have transformed diagnostic accuracy and risk stratification, reemphasizing the role of cardiac imaging in diagnosis, phenotyping, and management of these conditions. Genetic testing complements imaging by clarifying inheritance patterns, assessing sudden cardiac death risk, and informing therapeutic choices.
View Article and Find Full Text PDFCirc Arrhythm Electrophysiol
December 2024
Department of Cardiology, University Medical Center Utrecht, the Netherlands. (S.A.M., M.I.F.J.O., A.S.J.M.t.R.).
Curr Heart Fail Rep
December 2024
Department of Cardiology, Division Heart & Lungs, University Medical Centre Utrecht, University Utrecht, Utrecht, the Netherlands.
Purpose 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.
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