Predictive machine learning models for ascending aortic dilatation in patients with bicuspid and tricuspid aortic...

The objective of this study was to develop clinical classifiers aiming to identify prevalent ascending aortic dilatation in patients with bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV).

A prospective, single-centre and observational cohort.

The study involved 543 BAV and 491 TAV patients with aortic valve disease and/or ascending aortic dilatation, excluding those with coronary artery disease, undergoing cardiothoracic surgery at the Karolinska University Hospital (Sweden).

Predictors of high risk of ascending aortic dilatation (defined as ascending aorta with a diameter above 40 mm) were identified through the application of machine learning algorithms and classic logistic regression models.

Comprehensive multidimensional data, including valve morphology, clinical information, family history of cardiovascular diseases, prevalent diseases, demographic details, lifestyle factors, and medication.

BAV patients, with an average age of 60.4±12.4 years, showed a higher frequency of aortic dilatation (45.3%) compared with TAV patients, who had an average age of 70.4±9.1 years (28.9% dilatation, p <0.001). Aneurysm prediction models for TAV patients exhibited mean area under the receiver-operating-characteristic curve (AUC) values above 0.8, with the absence of aortic stenosis being the primary predictor, followed by diabetes and high-sensitivity C reactive protein. Conversely, prediction models for BAV patients resulted in AUC values between 0.5 and 0.55, indicating low usefulness for predicting aortic dilatation. Classification results remained consistent across all machine learning algorithms and classic logistic regression models.

Cardiovascular risk profiles appear to be more predictive of aortopathy in TAV patients than in patients with BAV. This adds evidence to the fact that BAV-associated and TAV-associated aortopathy involves different pathways to aneurysm formation and highlights the need for specific aneurysm preventions in these patients. Further, our results highlight that machine learning approaches do not outperform classical prediction methods in addressing complex interactions and non-linear relations between variables.