Abstract 4120577: Discrimination of Fabry Disease and Cardiac Amyloidosis Using Cardiac MRI and Echocardiography: A Comprehensive Analysis

Background: Discriminating Fabry disease from cardiac amyloidosis based on conventional imaging techniques can be challenging due to overlapping features. We aimed to investigate the discriminatory ability of cardiac MRI (CMR) and echocardiography imaging, to differentiate between these conditions. Methods: Cardiac MRI and echocardiography with SR imaging were performed on patients with suspected Fabry disease (n=39) or cardiac amyloidosis (n=79). Various imaging parameters, including late gadolinium enhancement (LGE) patterns and T1 mapping values derived from CMR, E/e’ and global longitudinal strain (GLS) were analyzed. Discrimination scores were calculated using multivariate analysis, with sensitivity and specificity determined for selected parameters. Results: In Fabry patients, LGE patterns predominantly exhibited a subendocardial distribution (66%), whereas amyloidosis patients showed transmural LGE (93%). LV mass index and T1 mapping values were higher (150±20 vs. 90±15 g/m 2 , 1208±104 vs. 822± 210ms both p p<0.001) in cardiac amyloidosis compared to Fabry disease. Echocardiographic parameters demonstrated significantly reduced GLS (-12±2 vs -18±3%, p<0.001), larger LA size (50 ± 5 mm vs. 40 ± 5 mm, p<0.001) and higher E/e' ratio (15 ± 3 vs. 8 ± 2, p<0.001) in cardiac amyloidosis compared to Fabry disease. Multivariate analysis identified LGE patterns and GLS as significant discriminators, and combining both parameters can discriminate Fabry and Amyloidosis with a sensitivity of 80.0%, specificity of 90.0%, and predictive accuracy of 85.0%. Incorporating LA size, LV mass index and E/e’ further improve the predictive accuracy to 92.0%. Conclusions: Cardiac MRI and echocardiography demonstrate promising discriminatory ability in distinguishing Fabry disease from cardiac amyloidosis. GLS emerges as a valuable parameter, reflecting myocardial function and aiding in accurate diagnosis. Integration of these imaging modalities into clinical practice could enhance diagnostic accuracy and guide appropriate management strategies.