Machine-learning phenotypic classification of bicuspid aortopathy

Bicuspid aortic valves (BAV) are associated with incompletely characterized aortopathy. Our objectives were to identify distinct patterns of aortopathy using machine-learning methods and characterize their association with valve morphology and patient characteristics. We analyzed preoperative 3-dimensional computed tomography reconstructions for 656 patients with BAV undergoing ascending aorta surgery between January 2002 and January 2014. Unsupervised partitioning around medoids was used to cluster aortic dimensions. Group differences were identified using polytomous random forest analysis. Three distinct aneurysm phenotypes were identified: root (n = 83; 13%), with predominant dilatation at sinuses of Valsalva; ascending (n = 364; 55%), with supracoronary enlargement rarely extending past the brachiocephalic artery; and arch (n = 209; 32%), with aortic arch dilatation. The arch phenotype had the greatest association with right–noncoronary cusp fusion: 29%, versus 13% for ascending and 15% for root phenotypes (P