Reduced-order models of endocardial shear stress patterns in the left atrial appendage from a data-augmented patient-specific database.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting over 1% of the population. It is usually triggered by irregular electrical impulses that cause the atria to contract irregularly and ineffectively. It increases blood stasis and the risk of thrombus formation within the left atrial appendage (LAA), and aggravates adverse atrial remodeling. Despite recent efforts, LAA flow patterns representative of AF conditions and their association with LAA stasis remain poorly characterized.

AIM: Our goal was to develop reduced-order data-driven models of LAA flow patterns to discriminate between AF and non-AF flow phenotypes and discover explainable determinants of LAA blood stasis.

METHODS: We combined a geometric data augmentation process with projection of results from 180 CFD atrial simulations on the universal LAA coordinate (ULAAC) system. The projection approach enhances data visualization and facilitates direct comparison between different anatomical and functional states. ULAAC projections were used as input for a proper orthogonal decomposition (POD) algorithm to build reduced-order models of hemodynamic metrics, extracting flow characteristics associated with AF and non-AF patients.

RESULTS: We verified that the ULAAC system provides an adequate representation to visualize data distributions on the LAA surface and to build POD-based reduced-order models. These models revealed significant differences in LAA flow patterns between patients with and without AF, which correlated with blood stasis. Together with anatomical morphing-based patient-specific data augmentation, this approach could help evaluate each patient's current risk of LAA thrombosis and enable prediction of how atrial remodeling could modulate this future stroke risk. Such information could potentially be used to personalize anticoagulation therapy in AF patients.