546 Machine-learning based exploration of echocardiographic patterns and clinical parameters to understand their relation to death or transplant in pediatric dilated cardiomyopathy

Background Pediatric dilated cardiomyopathy (DCM) affects left ventricular (LV) function and carries a high risk of death or heart transplantation. However, the relation of LV regional function and inefficiency to clinical outcomes is underexplored. Purpose The aim of this study was to understand the relationship of regional LV mechanics, global LV function and clinical characteristics to the outcomes of death or heart transplant in children with DCM; through the integration of a vast amount of information enabled by unsupervised machine learning techniques. Methods DCM was defined by a LV end-diastolic dimension z-score > 2 and LV ejection fraction (EF) Results 50 children with DCM (age 0 to 18 years) were analyzed. Clustering on the two first dimensions of the low-dimensional space resulted in three clusters (Figure A), with significantly different proportions of the composite outcome of death or heart transplant (Cl1 = 79%, Cl2 = 50%, Cl3 = 20%; p = 0.01). The group with the highest proportion of death or transplant (cluster 1) comprised the oldest and most frequently medicated subjects, with impaired LVEF and GLS, and with the widest QRS duration (p Conclusion Our results serve as a proof-of-concept that machine-learning based approaches can be useful to explore and understand which regional and global echo parameters in combination with clinical parameters are associated with a higher risk of death or transplant in pediatric DCM. Abstract 546 Figure