Assessment of the left atrial appendage morphology in patients after ischemic stroke - The ASSAM study

Cardiac imaging correlates and predictors of stroke in patients with atrial fibrillation: a meta-analysis

BACKGROUND

New nonclinical parameters are needed to improve the current stroke risk stratification schemes for patients with atrial fibrillation. This study aimed to summarize data on potential cardiac imaging correlates and predictors of stroke or systemic embolism in patients with atrial fibrillation.

METHODS

MEDLINE, EMBASE, and Web of Science were searched to identify all published studies providing relevant data through 16 November 2022. Random effects meta-analysis method was used to pool estimates.

RESULTS

We included 64 studies reporting data from a pooled population of 56 639 patients. Left atrial spontaneous echo-contrast [adjusted odds ratio (aOR) 3.32, 95% confidence interval (CI) 1.98-5.49], nonchicken wing left atrial appendage (LAA) morphology (aOR 2.15, 95% CI 1.11-4.18), left atrial enlargement (aOR 2.12, 95% CI 1.45-3.08), and higher LAA orifice diameter (aOR 1.56, 95% CI 1.18-2.05) were highly associated with stroke. Other parameters associated with stroke included higher left atrial sphericity (aOR 1.14, 95% CI 1.01-1.29), higher left atrial volume (aOR 1.03, 95% CI 1.01-1.04), higher left atrial volume index (aOR 1.014, 95% CI 1.004-1.023), lower left atrial reservoir strain [adjusted hazard ratio (aHR) 0.86, 95% CI 0.76-0.98], higher left ventricular mass index (aOR 1.010, 95% CI 1.005-1.015) and E / e' ratio (aOR 1.12, 95% CI 1.07-1.16). There was no association between LAA volume (aOR 1.37, 95% CI 0.85-2.21) and stroke.

CONCLUSION

These cardiac imaging parameters identified as potential predictors of thromboembolism may improve the accuracy of stroke risk stratification schemes in patients with atrial fibrillation. Further studies should evaluate the performance of holistic risk scores including clinical factors, biomarkers, and cardiac imaging.

Evaluation of atrial anatomical remodeling in atrial fibrillation with machine-learned morphological features

PURPOSE

To elucidate the role of atrial anatomical remodeling in atrial fibrillation (AF), we proposed an automatic method to extract and analyze morphological characteristics in left atrium (LA), left atrial appendage (LAA) and pulmonary veins (PVs) and constructed classifiers to evaluate the importance of identified features.

METHODS

The LA, LAA and PVs were segmented from contrast computed tomography images using either a commercial software or a self-adaptive algorithm proposed by us. From these segments, geometric and fractal features were calculated automatically. To reduce the model complexity, a feature selection procedure is adopted, with the important features identified via univariable analysis and ensemble feature selection. The effectiveness of this approach is well illustrated by the high accuracy of our models.

RESULTS

Morphological features, such as LAA ostium dimensions and LA volume and surface area, statistically distinguished ([Formula: see text]) AF patients or AF with LAA filling defects (AF(def+)) patients among all patients. On the test set, the best model to predict AF among all patients had an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI, 0.8-1) and the best model to predict AF(def+) among all patients had an AUC of 0.92 (95% CI, 0.81-1).

CONCLUSION

This study automatically extracted and analyzed atrial morphology in AF and identified atrial anatomical remodeling that statistically distinguished AF or AF(def+). The importance of identified atrial morphological features in characterizing AF or AF(def+) was validated by corresponding classifiers. This work provides a good foundation for a complete computer-assisted diagnostic workflow of predicting the occurrence of AF or AF(def+).

Does left atrial appendage morphology and dimension differ amongst etiological stroke subtypes in patients without known atrial fibrillation? Results from the left atrial appendage morphology and dimension assessment by TEE in patients with stroke without known atrial fibrillation (LAMDA-STROKE) study

CONTEXT

Complex left atrial appendage (LAA) morphology is increasingly associated with cryptogenic ischemic stroke as compared to cardioembolic stroke due to atrial fibrillation (AF). However, data on such an association in patients with other etiological stroke subtypes in the absence of AF is limited.

AIM

The study aimed to assess the LAA morphology, dimension and other echocardiographic parameters by transesophageal echocardiography (TEE) in patients with embolic stroke of undetermined source (ESUS) and compare it with other etiological stroke subtypes without known AF.

METHODS

This was a single-Centre, observational study involving comparison of echocardiographic parameters including LAA morphology and dimension in ESUS patients (group A; n = 30) with other etiological stroke subtypes i.e., TOAST (Trial of Org 10172 in Acute Stroke Treatment) class I-IV without AF (group B; n = 30).

RESULTS

Complex LAA morphology was predominant in group A (18 patients in group A versus 5 patients in group B, p-Value = 0.001). Mean LAA orifice diameter (15.3 + 3.5 mm in group A versus 17 + 2.0 mm in group B, p-Value = 0.027) and LAA depth were significantly lower in group A (28.4 + 6.6 mm in group A versus 31.7 + 4.3 mm in group B, p-Value = 0.026). Out of these three parameters only complex LAA morphology was found to be independently associated with ESUS [OR = 6.003, 95% CI {1.225-29.417}, p = 0.027].

CONCLUSION

Complex LAA morphology is a predominant feature in ischemic stroke patients with ESUS and may contribute to an increased risk of stroke in these patients.

Left atrial appendage filling defects restricted to the early phase of cardiac computed tomography is significantly associated with ischemic stroke

PURPOSE

To investigate the relationship between filling defects in the left atrial appendage restricted to the early phase of cardiac computed tomography (CCT), and ischemic stroke in patients with atrial fibrillation (AF).

MATERIALS AND METHODS

A total of 152 patients with non-valvular AF were retrospectively enrolled and divided into two groups according to the stroke history, as confirmed by brain computed tomography (CT) or magnetic resonance imaging (MRI), as the non-stroke group (n = 89) and stroke group (n = 63), respectively. The numbers of patients with filling defects in the early phase of CCT images without thrombi were recorded. Morphological parameters of the LAA were measured for all participants. All patients with early-phase filling defects (n = 44) were assigned to two groups according to ischemic stroke history: the filling defects with stroke group (n = 28) and the filling defects without stroke group (n = 16). The clinical characteristics and LAA morphological parameters were compared.

RESULTS

Univariate analysis showed that compared with the non-stroke group，LAA volume index and age were higher in the stroke group, and the ratio of early phase filling defect in LAA, hypertension and diabetes were also higher, in the meanwhile the LVEF and BMI were lower (P < 0.05).After adjusting confounding factors by the multivariate logistic regression analysis, filling defect was significantly related with stroke [odds ratio (OR): 4.339, 95% confidence interval (CI): 1.951-9.653, P = 0.000]. LAA morphological parameters were not significantly different between the filling defects with stroke group and the group without stroke.

CONCLUSION

AF patients with LAA non-thrombotic filling defects in the early-phase of CCT had an increased risk of ischemic stroke compared to those without filling defects. This finding may help to optimize stroke risk stratification in patients with AF.

Finding low CHA2DS2-VASc scores unreliable? Why not give morphological and hemodynamic methods a try?

Patients with atrial fibrillation (AF) suffer from a high risk of thrombosis. Currently, the CHA2DS2-VASc score is the most widely used tool for risk stratification in patients with AF, but it has disappointing accuracy and limited predictive value, especially in those with low scores. Thrombi in patients with AF mostly grow in their left atrial appendages (LAA), which is directly related to the abnormal morphology of the LAA or the left atrium and the unusual hemodynamic state around LAA, which may sensitively evaluate the risk of thrombosis complications in patients with AF and bring bases to clinical plans of medication and operation. Therefore, we investigated the research progress of hemodynamic and morphological studies about the predictive value of thrombosis risk in patients with AF, intending to discuss the prediction potential of morphological and hemodynamic indexes when compared with the presently used CHA2DS2-VASc system and how to build a more precise thromboembolic event prediction model for patients with AF.

Stroke risk evaluation for patients with atrial fibrillation: Insights from left atrial appendage

Left atrial appendage (LAA) is believed to be a common site of thrombus formation in patients with atrial fibrillation (AF). However, the commonly-applied stroke risk stratification model (such as. CHA2DS2-VASc score) does not include any structural or hemodynamic features of LAA. Recent studies have suggested that it is important to incorporate LAA geometrical and hemodynamic features to evaluate the risk of thrombus formation in LAA, which may better delineate the AF patients for anticoagulant administration and prevent strokes. This review focuses on the LAA-related factors that may be associated with thrombus formation and cardioembolic events.

Association Between Left Atrial Appendage Morphology and Function and the Risk of Ischaemic Stroke in Patients with Atrial Fibrillation

AF is the most common cardiac arrhythmia and has been identified as an independent risk factor for stroke. The European Society of Cardiology guidelines recommend a thromboembolic event risk assessment based on the CHA₂DS₂-VASc score. However, stroke also occurs in some patients with a low CHA₂DS₂-VASc score. Therefore, it is necessary to find new factors to improve thromboembolic risk stratification in AF patients. Over 90% of embolic strokes are caused by thrombi originating from the left atrial appendage (LAA). Thus, certain anatomical or functional parameters of the LAA could potentially be used to predict cardioembolic stroke. Studies have suggested that some of these factors, such as LAA morphology, number of LAA lobes, LAA dimensions, LAA volume, distance from the LAA ostium to the first bend of LAA, LAA orifice diameter, extent of LAA trabeculations, LAA takeoff, LAA flow velocity and LAA strain rate, are independently associated with a higher risk of stroke in a population of patients with AF and improve the performance of the CHA₂DS₂-VASc score. However, the results are conflicting and, so far, no new parameter has been added to the CHA₂DS₂-VASc score.

Management and outcomes of patients with left atrial appendage thrombus prior to percutaneous closure

Objective

Left atrial appendage (LAA) thrombus has heretofore been considered a contraindication to percutaneous LAA closure (LAAC). Data regarding its management are very limited. The aim of this study was to analyse the medical and invasive treatment of patients referred for LAAC in the presence of LAA thrombus.

Methods

This multicentre observational registry included 126 consecutive patients referred for LAAC with LAA thrombus on preprocedural imaging. Treatment strategies included intensification of antithrombotic therapy (IAT) or direct LAAC. The primary and secondary endpoints were a composite of bleeding, stroke and death at 18 months, and procedural success, respectively.

Results

IAT was the preferred strategy in 57.9% of patients, with total thrombus resolution observed in 60.3% and 75.3% after initial and subsequent IAT, respectively. Bleeding complications and stroke during IAT occurred in 9.6% and 2.9%, respectively, compared with 3.8% bleeding and no embolic events in the direct LAAC group before the procedure. Procedural success was 90.5% (96.2% vs 86.3% in direct LAAC and IAT group, respectively, p=0.072), without cases of in-hospital thromboembolic complications. The primary endpoint occurred in 29.3% and device-related thrombosis was found in 12.8%, without significant difference according to treatment strategy. Bleeding complications at 18 months occurred in 22.5% vs 10.5% in the IAT and direct LAAC group, respectively (p=0.102).

Conclusion

In the presence of LAA thrombus, IAT was the initial management strategy in half of our cohort, with initial thrombus resolution in 60% of these, but with a relatively high bleeding rate (~10%). Direct LAAC was feasible, with high procedural success and absence of periprocedural embolic complications. However, a high rate of device-related thrombosis was detected during follow-up.

Deep Learning Framework for Real-Time Estimation of in-silico Thrombotic Risk Indices in the Left Atrial Appendage

Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and the formation of thrombi in atrial fibrillation (AF) patients. Nonetheless, CFD solvers are notoriously time-consuming and computationally demanding, which has sparked an ever-growing body of literature aiming to develop surrogate models of fluid simulations based on neural networks. The present study aims at developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), an in-silico index linked to the risk of thrombosis, typically derived from CFD simulations, solely from the patient-specific LAA morphology. To this end, a set of popular DL approaches were evaluated, including fully connected networks (FCN), convolutional neural networks (CNN), and geometric deep learning. While the latter directly operated over non-Euclidean domains, the FCN and CNN approaches required previous registration or 2D mapping of the input LAA mesh. First, the superior performance of the graph-based DL model was demonstrated in a dataset consisting of 256 synthetic and real LAA, where CFD simulations with simplified boundary conditions were run. Subsequently, the adaptability of the geometric DL model was further proven in a more realistic dataset of 114 cases, which included the complete patient-specific LA and CFD simulations with more complex boundary conditions. The resulting DL framework successfully predicted the overall distribution of the ECAP in both datasets, based solely on anatomical features, while reducing computational times by orders of magnitude compared to conventional CFD solvers.