State-of-the-Art in Echocardiographic Strain Imaging of Arrhythmogenic Cardiomyopathy.

BACKGROUND

Arrhythmogenic cardiomyopathy (ACM) is a genetic disorder marked by fibrofatty myocardial replacement, leading to ventricular dysfunction and life-threatening arrhythmias. Historically identified as a right ventricular disease, advances in cardiac magnetic resonance imaging (CMR) have revealed left ventricular involvement in some cases, underscoring the need for refined diagnostic criteria. Conventional echocardiographic methods lack sensitivity, particularly for early disease detection, while strain imaging holds promise for identifying subclinical dysfunction.

OBJECTIVE

This review explores the advancements in echocardiographic strain imaging techniques for ACM diagnosis and prognosis, focusing on recent developments in right and left ventricular assessment, differentiation from athletic remodeling, and integration with CMR findings.

METHODS

An extensive review of contemporary literature was conducted that analyzed diagnostic thresholds, prognostic significance, and the utility of echocardiographic strain imaging compared to standard measures.

FINDINGS: Echocardiographic right ventricular strain assessment offers enhanced diagnostic sensitivity, identifying ACM in cases that fail to meet traditional echocardiographic criteria. Strain provides superior prognostic discrimination, correlating with the risk of ventricular arrhythmias and disease progression. Left ventricular strain, particularly global longitudinal strain, detects subclinical dysfunction and predicts adverse outcomes, emphasizing its importance in left-dominant ACM phenotypes. Strain-based mechanical dispersion metrics also facilitate early disease detection and risk stratification.

CONCLUSION

Echocardiographic strain imaging is a valuable, noninvasive tool for ACM, addressing gaps in traditional echocardiographic diagnostic criteria. Standardization of thresholds and broader integration into clinical practice are required to maximize its utility. Future directions include leveraging machine learning and multimodal imaging for comprehensive ACM characterization.