Association between Left Atrial Appendage Fibrosis and Thrombus Formation: A Histological Approach

The correlation between ultrasonographic morphology and structure of the left atrial appendage, blood flow velocity, and plasma galectin-3 levels with thrombus formation in the left atrial appendage of patients with atrial fibrillation

Background

To explore the correlation between left atrial appendage morphology, blood flow velocity and plasma galectin-3 and thrombosis in patients with atrial fibrillation.

Methods

Patients with atrial fibrillation who received treatment and completed ultrasound examination in hospital from 2022 to December 2023 were enrolled. According to whether there was left atrial appendage thrombosis, the patients were divided into a control group (no left atrial appendage thrombosis was found) and a study group (left atrial appendage thrombosis was found). The morphology and structure of the left atrial appendage, blood flow velocity and plasma galectin-3 level were recorded exploring its correlation with left atrium thrombosis.

Results

A total of 330 patients with atrial fibrillation were enrolled, including 278 in the control group and 52 in the study group. Left group and the control group of morphological structure differences (P < 0.05). The main lobe length, ostial area, longest diameter, shortest diameter, left atrial appendage volume and left atrial volume in the study group were higher than those in the control group (P < 0.05). The left atrial appendage emptying velocity, filling velocity and left ventricular ejection fraction of the study group were lower than those of the control group, and the left ventricular end-diastolic diameter was higher than that of the control group (P < 0.05). Group of white blood cell count, neutrophils/lymphocyte ratio, plasma galactose lectin-3 levels were higher than control group (P < 0.05). ROC curve analysis of left atrial appendage emptying velocity, left atrial appendage filling velocity, left atrial enddiastolic diameter and left atrial ejection fraction had higher diagnostic value (P < 0.05).

Conclusions

Left atrial appendage morphology, blood flow velocity and plasma galectin-3 level are important factors to evaluate the risk of left atrial appendage thrombosis in patients with atrial fibrillation. This study improves the understanding of thrombosis, further elucidates the risk factors for thrombosis, and improves patient prognosis.

Relationship Between Fibrosis, Endocardial Endothelial Damage, and Thrombosis of Left Atrial Appendage in Atrial Fibrillation

BACKGROUND

Left atrial appendage (LAA) thrombus (LAAT) and ischemic stroke are considered important in atrial cardiomyopathy with progressive atrial fibrosis and endocardial endothelial damage.

OBJECTIVES

This study aimed to obtain histological evidence to clarify the association between LAA fibrosis and endocardial endothelial damage with LAAT, ischemic stroke, and clinical risk factors.

METHODS

Ninety-six patients with atrial fibrillation (AF) scheduled to undergo LAA excision during surgery were enrolled. They underwent transesophageal echocardiography before the surgery to validate the LAA function/morphology and LAAT presence or absence. The resected LAAs were subjected to Azan-Mallory staining and CD31 immunohistochemistry to quantify the degree of fibrosis and endocardial endothelial damage staged as F1-F4 and E1-E4 per the quantiles.

RESULTS

Patients with an LAAT and/or ischemic stroke history had higher fibrosis degrees (18.4% ± 9.9% vs 10.4% ± 7.0%, P < 0.0001) and lower CD31 expressions (0.27 [IQR: 0.05-0.57] vs 1.02 [IQR: 0.49-1.65]; P < 0.0001). Also, higher CHADS₂ was associated with a higher degree of fibrosis and lower CD31 expression. Multivariate logistic regression analysis revealed that endothelial damage (E4) was associated with an LAAT and/or ischemic stroke history independent of AF type (paroxysmal or nonparoxysmal) with an OR of 3.47. Among patients with nonparoxysmal AF, fibrosis (F4, OR: 3.66), endothelial damage (E4, OR: 4.62), and LAA morphology (non-chicken-wing, OR: 3.79) were independently associated with LAAT and/or stroke. The degree of fibrosis correlated significantly with endothelial damage (R = -0.38, P = 0.0001).

CONCLUSIONS

These histological findings may be essential in considering the pathophysiology of LAAT and stroke within the atrial cardiomyopathy context.

Personalized biomechanical insights in atrial fibrillation: opportunities & challenges

INTRODUCTION

Atrial fibrillation (AF) is an increasingly prevalent and significant worldwide health problem. Manifested as an irregular atrial electrophysiological activation, it is associated with many serious health complications. AF affects the biomechanical function of the heart as contraction follows the electrical activation, subsequently leading to reduced blood flow. The underlying mechanisms behind AF are not fully understood, but it is known that AF is highly correlated with the presence of atrial fibrosis, and with a manifold increase in risk of stroke.

AREAS COVERED

In this review, we focus on biomechanical aspects in atrial fibrillation, current and emerging use of clinical images, and personalized computational models. We also discuss how these can be used to provide patient-specific care.

EXPERT OPINION

Understanding the connection betweenatrial fibrillation and atrial remodeling might lead to valuable understanding of stroke and heart failure pathophysiology. Established and emerging imaging modalities can bring us closer to this understanding, especially with continued advancements in processing accuracy, reproducibility, and clinical relevance of the associated technologies. Computational models of cardiac electromechanics can be used to glean additional insights on the roles of AF and remodeling in heart function.

Machine Learning and the Conundrum of Stroke Risk Prediction

Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.

Left Atrial Appendage Depth and Tachycardia Bradycardia Syndrome as Important Predictors of Left Atrial Appendage Thrombus in Patients with Nonvalvular Atrial Fibrillation

Background

Atrial fibrillation (AF) is the most common heart rhythm disorder that has been shown to be associated with a significant increase in stroke and systemic embolism risk. The left atrial appendage (LAA) is a finger-like extension originating from the left atrium; the formation of thrombus in LAA is the main reason of stroke and systemic embolism in patients with nonvalvular atrial fibrillation (NVAF). This study is aimed at finding out the risk of left atrial appendage thrombus (LAAT) in patients with nonvalvular atrial fibrillation (NVAF).

Method

We retrospectively examined the clinic and left atrial computer tomography angiography (CTA) features of patients assessed in Zhengzhou No. 7 People's Hospital between January 2020 and January 2021 derivation. Student's t-test, chi-square test, receiver operating characteristics (ROC) curves, and logistic regression analysis were used to identify predictors of LAAT.

Result

Of 480 patients included in the analysis, LAAT was found in approximately 9.2% of all patients. Univariate demographic predictors of LAAT included left atrium top and bottom diameter (LTD), left atrial appendage depth (LAAD), CHA2DS2-VASc, tachycardia bradycardia syndrome (TBS), and nonparoxysmal atrial fibrillation (PAF). In a multiple logistic regression analysis, the independent predictors of thrombus were LAAD > 23.45 mm (odds ratio: 4.216, 95% CI: 1.869-9.510, P = 0.001), TBS (odds ratio: 4.076, 95% CI: 1.655-10.038, P = 0.002), and non-PAF (odds ratio: 2.896, 95% CI: 1.183-7.094, P = 0.02).

Conclusion

In NVAF patients with LAAT, evidence suggested that larger LAAD, non-PAF, and TBS present a high risk of LAAT. This is the first report demonstrating that the LAAD and TBS are associated with LAAT in patients with NVAF.