Left atrial regional strain assessed by novel dedicated three-dimensional speckle tracking echocardiography

Left atrial stiffness index - an early marker of left ventricular diastolic dysfunction in patients with coronary heart disease

AIMS

To evaluate the correlation between left atrial stiffness index (LASI) and left ventricular diastolic function in patients with coronary heart disease (CHD) by Autostrain LA technique.

METHODS

This was a retrospective analysis that included a total of 82 CHD patients who had suitable image quality for left atrial strain measurement. According to the 2016 ASE/EACVI guidelines for the echocardiographic assessment of diastolic dysfunction, the patients were divided into three groups: normal left ventricular diastolic function group (n = 26), indeterminate left ventricular diastolic function (n = 36), and left ventricular diastolic dysfunction (LVDD) (n = 20). The left atrial conduit strain (LAScd), Left atrial contractile strain (LASct), left atrial reservoir strain (LASr) and its derived parameters, including LASI and left atrial filling index (LAFI), were compared among the three groups. Furthermore, we conduct a correlation analysis between LASI and left ventricular diastolic function in patients with CHD.

RESULTS

LASr and LAScd in normal group were higher than those in indeterminate group, LASr and LAScd in indeterminate group were higher than those in LVDD group, LASI in normal group was lower than that in indeterminate group, and LASI in indeterminate group was lower than that in LVDD group (P < 0.001). LASct in both normal and indeterminate groups was higher than that in LVDD group (P < 0.05). The LAFI of normal group was lower than that of indeterminate group and LVDD group (P < 0.001). LASI was positively correlated with E/e'(r = 0.822) (P < 0.001). LASr and E/e' were negatively correlated (r = -0.637) (P < 0.001).

CONCLUSION

LASI is closely related to the changes of left ventricular diastolic function in CHD patients.

Automatic 3D left atrial strain extraction framework on cardiac computed tomography

BACKGROUND AND OBJECTIVE

Strain analysis provides insights into myocardial function and cardiac condition evaluation. However, the anatomical characteristics of left atrium (LA) inherently limit LA strain analysis when using echocardiography. Cardiac computed tomography (CT) with its superior spatial resolution, has become critical for in-depth evaluation of LA function. Recent studies have explored the feasibility of CT-derived strain; however, they relied on manually selected regions of interest (ROIs) and mainly focused on left ventricle (LV). This study aimed to propose a first-of-its-kind fully automatic deep learning (DL)-based framework for three-dimensional (3D) LA strain extraction on cardiac CT.

METHODS

A total of 111 patients undergoing ECG-gated contrast-enhanced CT for evaluating subclinical atrial fibrillation (AF) were enrolled in this study. We developed a 3D strain extraction framework on cardiac CT images, containing a 2.5D GN-U-Net network for LA segmentation, axis-oriented 3D view extraction, and LA strain measure. The segmentation accuracy was evaluated using Dice similarity coefficient (DSC). The model-extracted LA volumes and emptying fraction (EF) were compared with ground-truth measurements using intraclass correlation coefficient (ICC), correlation coefficient (r), and Bland-Altman plot (B-A). The automatically extracted LA strains were evaluated against the LA strains measured from 2D echocardiograms. We utilized this framework to gauge the effect of AF burden on LA strain, employing the atrial high rate episode (AHRE) burden as the measurement parameter.

RESULTS

The GN-U-Net LA segmentation network achieved a DSC score of 0.9603 on the test set. The framework-extracted LA estimates demonstrated excellent ICCs of 0.949 (95 % CI: 0.93-0.97) for minimal LA volume, 0.904 (95 % CI: 0.86-0.93) for maximal LA volume, and 0.902 (95 % CI: 0.86-0.93) for EF, compared with expert measurements. The framework-extracted LA strains demonstrated moderate agreement with the LA strains based on 2D echocardiography (ICCs >0.703). Patients with AHRE > 6 min had significantly lower global strain and LAEF, as extracted by the framework than those with AHRE ≤ 6 min.

CONCLUSION

The promising results highlighted the feasibility and clinical usefulness of automatically extracting 3D LA strain from CT images using a DL-based framework. This tool could provide a 3D-based alternative to echocardiography for assessing LA function.

Left Atrial Cardiomyopathy - A Challenging Diagnosis

Left atrial cardiomyopathy (LACM) has been an ongoing focus of research for several years. There is evidence that LACM is responsible for atrial fibrillation and embolic strokes of undetermined sources. Therefore, the correct diagnosis of LACM is of clinical importance. Various techniques, including electrocardiography, echocardiography, cardiac magnetic resonance imaging, computed tomography, electroanatomic mapping, genetic testing, and biomarkers, can both identify and quantify structural, mechanical as well as electrical dysfunction in the atria. However, the question arises whether these techniques can reliably diagnose LACM. Because of its heterogeneity, clinical diagnosis is challenging. To date, there are no recommendations for standardized diagnosis of suspected LACM. However, standardization could help to classify LACM more precisely and derive therapeutic directions to improve individual patient management. In addition, uniform diagnostic criteria for LACM could be important for future studies. Combining several parameters and relating them seems beneficial to approach the diagnosis of LACM. This review provides an overview of the current evidence regarding the diagnosis of LACM, in which several potential parameters are discussed and, consequently, a proposal for a diagnostic algorithm is presented.