Abstract P139: Clinical Classifiers To Identify Prevalent Aortopathy In Patients With Bicuspid Versus Tricuspid Aortic Valves

Background and Aims: The mechanisms underlying bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV) ascending aortic aneurysm are still unknown. We sought to identify predictors of aortopathy in BAV and TAV patients, respectively, and determine the genetic contribution to the valve phenotype. Methods: This study included BAV (n=545) and TAV (n=496) patients with aortic valve disease and/or ascending aorta dilatation but devoid of coronary artery disease. We applied machine learning algorithms and classic logistic regression models using multiple variable selection methodologies to predict individuals of high risk of aneurysm. Analyses included comprehensive multidimensional data (i.e., valve morphology, plasma analyses, genetic- and clinical data, family history of cardiovascular diseases, prevalent diseases, demographic, lifestyle and medication). The genetic impact on phenotype was estimated in a genome-wide complex trait analysis using a variance components model. Results: BAV patients were younger (60.4±12.3 years) than TAV patients (70.2±9.5 years), and had a higher frequency of aortic dilatation (45.1% and 29% for BAV and TAV, respectively. P Conclusions: The predictive classifier of TAV patients is clinically relevant and potentially offers important implications for better targeting TAV individual at high risk of developing aneurysm. Cardiovascular risk profiles appear to be more predictive of aortopathy than valve morphology and genetic data in TAV patients, whereas in BAV patients, the genetic contribution exceeds environmental factors.