Aims: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by ventricular dysfunction and ventricular arrhythmias (VA). Adequate arrhythmic risk assessment is important to prevent sudden cardiac death. We aimed to study the incremental value of strain by feature-tracking cardiac magnetic resonance imaging (FT-CMR) in predicting sustained VA in ARVC patients.
Methods And Results: CMR images of 132 ARVC patients (43% male, 40.6 ± 16.0 years) without prior VA were analysed for global and regional right and left ventricular (RV, LV) strain. Primary outcome was sustained VA during follow-up. We performed multivariable regression assessing strain, in combination with (i) RV ejection fraction (EF); (ii) LVEF; and (iii) the ARVC risk calculator. False discovery rate adjusted P-values were given to correct for multiple comparisons and c-statistics were calculated for each model. During 4.3 (2.0-7.9) years of follow-up, 19% of patients experienced sustained VA. Compared to patients without VA, those with VA had significantly reduced RV longitudinal (P ≤ 0.03) and LV circumferential (P ≤ 0.04) strain. In addition, patients with VA had significantly reduced biventricular EF (P ≤ 0.02). After correcting for RVEF, LVEF, and the ARVC risk calculator separately in multivariable analysis, both RV and LV strain lost their significance [hazard ratio 1.03-1.18, P > 0.05]. Likewise, while strain improved the c-statistic in combination with RVEF, LVEF, and the ARVC risk calculator separately, this did not reach statistical significance (P ≥ 0.18).
Conclusion: Both RV longitudinal and LV circumferential strain are reduced in ARVC patients with sustained VA during follow-up. However, strain does not have incremental value over RVEF, LVEF, and the ARVC VA risk calculator.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762936 | PMC |
http://dx.doi.org/10.1093/ehjci/jeac030 | DOI Listing |
Heart Rhythm
December 2024
Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Background: Desmoplakin (DSP) variants are associated with left-predominant or biventricular arrhythmogenic cardiomyopathy. Exercise promotes penetrance and sustained ventricular arrhythmias (VA) in right-sided arrhythmogenic right ventricular cardiomyopathy, but its effect is unknown in DSP variant carriers.
Objectives: To assess whether exercise is associated with clinical outcomes among individuals with a pathogenic or likely pathogenic (P/LP) DSP variant.
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 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.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!