Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement

BackgroundCerebrovascular events (CVE) are one of the most feared complications of transcatheter aortic valve replacement (TAVR). CVE appear difficult to predict due to their multifactorial origin incompletely explained by clinical predictors. We aimed to build a deep learning-based predictive tool for TAVR-related CVE.MethodsIntegrated clinical and imaging characteristics from consecutive patients enrolled into a prospective TAVR registry were analysed. CVE comprised any strokes and transient ischemic attacks. Predictive variables were selected by recursive feature reduction to train an autoencoder predictive model. Area under the curve (AUC) represented the model’s performance to predict 30-day CVE.ResultsAmong 2,279 patients included between 2007 and 2019, both clinical and imaging data were available in 1,492 patients. Median age was 83 years and STS score was 4.6%. Acute (ConclusionsTAVR-related CVE can be estimated using a deep learning-based predictive algorithm. The model was implemented online for broad usage. (https://www.welcome.alviss.ai/#/cvecalculator).