Reduced-order models of wall shear stress patterns in the left atrial appendage from a data-augmented atrial database

TL;DR

This study develops reduced-order, data-driven models of left atrial appendage flow patterns in atrial fibrillation, revealing flow disturbances linked to thrombosis risk and aiding clinical decision-making.

Contribution

It introduces a novel combination of geometric data augmentation, ULAAC projection, and POD analysis to characterize LAA flow patterns during atrial remodeling.

Findings

ULAAC effectively visualizes LAA data distributions.

Reduced-order models distinguish flow patterns in remodeled atria.

Flow disturbances correlate with increased thrombosis risk.

Abstract

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: To develop reduced-order data-driven models of LAA flow patterns during atrial remodeling in order to uncover flow disturbances concurrent with LAA stasis that could add granularity to clinical decision criteria. Methods: We combined a geometric data augmentation process with projection of results from 180 CFD atrial simulations on a 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 anatomies. 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 for atrial geometries that underwent adverse atrial remodeling and experienced elevated blood stasis. Together with anatomical morphing-based patient-specific data augmentation, this approach could facilitate data-driven analyses to identify flow features associated with thrombosis risk due to atrial remodeling.