Strain Analysis for Early Detection of Fibrosis in Arrhythmogenic Cardiomyopathy: Insights from a Preliminary Study

<b>Background:</b> Arrhythmogenic cardiomyopathy (ACM) is a genetic disorder characterized by fibrofatty replacement of myocardial tissue, predominantly affecting the right ventricle (RV), but often involving the left ventricle (LV) as well. The early detection of fibrosis, crucial for risk stratification, has been enhanced by advanced imaging techniques. Global longitudinal strain (GLS) has shown promise as a surrogate marker for late enhancement (LE) in identifying myocardial fibrosis, yet precise cut-off values for strain are lacking. The aim of the study is to evaluate LV strain as a predictor of LE in ACM and to define strain cut-offs for early fibrosis detection, enhancing non-invasive diagnostic accuracy. <b>Methods:</b> This retrospective single-center study included 64 patients diagnosed with ACM. Echocardiographic analysis using speckle-tracking echocardiography was performed to assess LV strain. LE was evaluated through cardiac magnetic resonance (CMR) or via cardiac computed tomography (CCT) in cases with CMR contraindications. The study aimed to correlate regional LV strain values with the presence of LE, identifying cut-off values predictive of fibrosis. <b>Results:</b> The study found significant correlations between reduced LV strain values and the presence of LE, particularly in the anterolateral and inferolateral segments (<i>p</i> < 0.05). Specific strain thresholds, such as those for segment 12 (<i>p</i> = 0.02) and segment 17 (<i>p</i> = 0.03), were identified as predictive markers for LE. These findings suggest that strain imaging could serve as a non-invasive tool for the early detection of myocardial fibrosis in ACM patients. <b>Conclusions:</b> LV strain analysis offers potential as a non-invasive surrogate marker for myocardial fibrosis in ACM. Incorporating strain imaging into routine echocardiographic evaluations could improve early diagnosis and risk stratification, guiding patient management.