Atrial fibrillation (AF) carries out a 5-fold increase in stroke risk, related to embolization of thrombi clotting in left atrium (LA). Left atrial appendage (LAA) is the site with the highest blood stasis which causes thrombus formation. About 90 % of the intracardiac thrombi in patients with cardioembolic events originally develop in the LAA. Recent studies have been focused on the association between LAA anatomical features and stroke risk and provided conflicting results. Haemodynamic and fluid dynamic information on the LA and mostly on the LAA may improve stroke risk stratification. Therefore, the aim of this study was the design and development of a workflow to quantitatively define the influence of the LAA morphology on LA hemodynamics. Five 3D LA anatomical models, obtained from real clinical data, which were clearly different as regard to LAA morphology were used. For each LAA we identified and computed several parameters describing its geometry. Then, one LA chamber model was chosen and a framework was developed to connect the different LAAs belonging to the other four patients to this model. These new anatomical models represented the computational domain for the computational fluid dynamics (CFD) study; simulations of the hemodynamics within the LA and LAA were performed in order to evaluate the interplay of the LAA shape on the blood flow characteristics in AF condition. CFD simulations were carried out for five cardiac cycles. Blood velocity, vorticity, LAA orifice velocity, residence time computed in the five models were compared and correlated with LAA morphologies. Results showed that not only complex morphologies were characterized by low velocities, low vorticity and consequently could carry a higher thrombogenic risk; even qualitatively simple morphologies showed a thrombogenic risk equal, or even higher, than more complex auricles. CFD results supported the hypothesis that LAA geometric characteristics plays a key-role in defining thromboembolic risk. LAA geometric parameters could be considered, coupled with the morphological characteristics, for a comprehensive evaluation of the regional blood stasis. The proposed procedure might address the development of a tool for patient-specific stroke risk assessment and preventive treatment in AF patients, relying on morpho-functional defintion of each LAA type.
Background: In patients (pts) with sick sinus syndrome (SSS), right ventricular apical (RVA) pacing increased the risk of developing atrial fibrillation (AF). However, the mechanism of proarrhythmic effect of RVA pacing remains unclear. Methods: We performed detailed echocardiograhic examination with Tissue Doppler Imaging in 60 pts with SSS (mean age 73A9 years, 42 F) who implanted with DDD pacemakers during atrial and ventricular pacing with atrioventricular interval programmed at 120-150 mesc (ApVp mode) and AAI mode with (ApVs mode) at 70 bpm. Echo measurements were taken after 15 mins of pacing in each mode. The myocardial atrial contraction velocity was measured at annulus of right free wall (Ra), septal (Sa) and lateral free wall (La) respectively. Results: As expected, the AV interval was significantly shorter (118A25 vs.163A45 ms, P=0.002), and QRS duration was longer (146A33 vs.97A26 ms, P,0.001) during ApVp mode as compared with ApVs mode. Although there was no significant difference in left ventricular ejection fraction, left atrial (LA) ejection fraction (50A14 vs.55A14%, P=0.005), LA active emptying fraction (32A17 vs.37A16%, P=0.018) and LA filling fraction (43A13 vs. 48A13%, P=0.007) were all significant improved by 18%, 54% and 18%, respectively during ApVs mode as compared with ApVp mode. Furthermore, atrial myocardial contraction velocities among Ra (14.0A3.8 vs.15.2A4.6cm/s, P=0.026), Sa (7.8A2.6 vs. 8.8A2.8cm/s, P=0.001), and La (8.9A3.2 vs.9.7A2.7cm/s, P=0.020) were also significantly increased during ApVs mode by 12%, 19% and 21%, respectively as compared with ApVp mode (Figure). Conclusions: In pts with SSS, avoidance of RVA pacing during ApVs mode improves LA haemodynamic and mechanical function, which might contribute to a lower risk of development of AF after pacemaker implantation. P773Qualitative and quantitative assessment of 3 novel post-processing methods for enhancing echocardiographic images. Echocardiography, while a prevalent tool for assessing cardiac morphology and function, suffers from a range of artefacts that reduce its diagnostic value. This work qualitatively and quantitatively evaluates 3 novel post-processing methods for enhancing echocardiographic images. Data enhancement is achieved by utilising multiple partially decorrelated instances of a cardiac cycle acquired through a single acoustic window. Such information has until now been largely disregarded during data post-processing. Moreover, unlike past approaches, data enhancement is achieved without filtering out information based on static or adaptive selection criteria. Qualitative assessment using 32 clinical datasets demonstrated (i) suppression of cavity noise, (ii) increase in tissue/cavity contrast, and (iii) visual enhancement of tissue structures previously masked-out by various artefacts (Figure 1). The effect of each post-processing method on the diagnostic value of cardiac ultrasound data was quantitatively assessed by examining the repeatability coefficient variations (via Bland-Altman plots) in clini...
Atrial fibrillation is associated with a five-fold increase of the stroke risk. Left atrial appendage (LAA) is the atrial site with the highest blood stasis risk, increasing thrombus formation and stroke. Recent studies have been focused on the association between the left atrial appendage anatomical features and the stroke risk. However, conflicting results have been published. In this context, clinical studies suggested the stroke risk stratification could be improved by using haemodynamics information on the left atrium and mainly on the left atrial appendage. Therefore, the aim of this study was the design and development of a method which enabled to reconstruct and generate several LA anatomical models, where each one was characterized by a different LAA morphology. These anatomical models represent the computational domain for the computational fluid dynamics simulations of the haemodynamics within the left atrium and LAA.
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