Regional heterogeneity in left atrial stiffness impacts passive deformation in a cohort of patient-specific models

TL;DR

It is shown that regional heterogeneity in stiffness contributes to regional LA biomechanics, while anatomical features appeared less important, and a significant correlation between regionally calibrated stiffness and CT-derived LA biomechanics is found.

Abstract

The deformation of the left atrium (LA), or its biomechanical function, is closely linked to the health of this cardiac chamber. In atrial fibrillation (AF), atrial biomechanics are significantly altered but the underlying cause of this change is not always clear. Patient-specific models of the LA that replicate patient atrial motion can allow us to understand how factors such as atrial anatomy, myocardial stiffness and physiological constraints are linked to atrial biomechanics. We created patient-specific LA models from CT images. We fitted regional model stiffness to peak CT-derived deformation during the LA reservoir phase ($\pm0.90$ mm) and used the CT deformation transients through the reservoir and conduit phase for model validation (deformation transients fell within $\pm0.38$ mm per unit time of targets). We found that myocardial stiffness varies regionally across the LA. The regional stiffness values were significant factors contributing to regional physiological LA deformation ($p=0.023$) while features of LA anatomy, including regional wall thickness and adipose volume, were less important. These findings provide insight into the underlying causes of altered LA biomechanics in AF.

Figures (12)

• Figure 1: Summary of Methods. A A cohort of 10 LA meshes with epicardial adipose tissue (EAT) was generated from the end-diastolic (ED) frame of a gated contrast-enhanced CT image-set. B Using image registration, the ED LA mesh was deformed over cardiac cycle, with maximum deformation at end-systolic (ES). Plots show the endocardial volume and surface displacement transients derived from feature tracking motion models. C The loading conditions applied in our LA modelling framework and the simulation set-up with boundary conditions applied. A estimated pressure profile was applied to LA endocardium. Patient-specific image-derived mitral valve (MV) displacement was applied to the MV annulus in simulation model. D Description of how Gaussian process emulators (GPEs) were used to speed up computation time. E-G Summary of the fitting methods used in this study.
• Figure 2: Left atrial mesh cohort. Images show the anterior view of the cohort of 10 meshes generated using the pipeline described in Methods. * indicates patients with AF.
• Figure 3: Regional heterogeneity in LA displacement. Plots show regional endocardial surface displacements transients derived from the feature tracking motion models for a representative case (A) and the distribution of the regional ES displacements across the cohort (B). Each marker symbol corresponds to one of the 10 patients. Significance was determined using a paired t-test with the Bonferroni correction for multiple comparisons applied. *** indicates a p-value $<$ 0.001. **** indicates a p-value $<$ 0.0001.
• Figure 4: Effect of anatomical features on observed LA displacement. Plots show how regional averages of end-systolic (ES) displacement vary with regionally averaged LA wall thickness (A) and regional EAT volume (B). Each marker symbol corresponds to one of the 10 patients.
• Figure 5: Global sensitivity analysis results. A Heatmap of the total effect of the parameters (x-axis) on the outputs (y-axis). B Barplot of the maximum total effect of each parameter over all outputs. The parameters are ranked from most to least important. The coloured bars represent the total sensitivity up to 90%.