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Subject-specific factors affecting particle residence time distribution of left atrial appendage in atrial fibrillation: A computational model-based study

BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmia, that causes thrombus formation, ordinarily in the left atrial appendage (LAA). The conventional metric of stroke risk stratification, CHA(2)DS(2)-VASc score, does not account for LAA morphology or hemodynamics. We showed in our previous...

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Detalles Bibliográficos
Autores principales: Sanatkhani, Soroosh, Nedios, Sotirios, Menon, Prahlad G., Saba, Samir F., Jain, Sandeep K., Federspiel, William J., Shroff, Sanjeev G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040531/
https://www.ncbi.nlm.nih.gov/pubmed/36993996
http://dx.doi.org/10.3389/fcvm.2023.1070498
Descripción
Sumario:BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmia, that causes thrombus formation, ordinarily in the left atrial appendage (LAA). The conventional metric of stroke risk stratification, CHA(2)DS(2)-VASc score, does not account for LAA morphology or hemodynamics. We showed in our previous study that residence time distribution (RTD) of blood-borne particles in the LAA and its associated calculated variables (i.e., mean residence time, t(m), and asymptotic concentration, C(∞)) have the potential to improve CHA(2)DS(2)-VASc score. The purpose of this research was to investigate the effects of the following potential confounding factors on LAA t(m) and C(∞): (1) pulmonary vein flow waveform pulsatility, (2) non-Newtonian blood rheology and hematocrit level, and (3) length of the simulation. METHODS: Subject-Specific data including left atrial (LA) and LAA cardiac computed tomography, cardiac output (CO), heart rate, and hematocrit level were gathered from 25 AF subjects. We calculated LAA t(m) and C(∞) based on series of computational fluid dynamics (CFD) analyses. RESULTS: Both LAA t(m) and C(∞) are significantly affected by the CO, but not by temporal pattern of the inlet flow. Both LAA t(m) and C(∞) increase with increasing hematocrit level and both calculated indices are higher for non-Newtonian blood rheology for a given hematocrit level. Further, at least 20,000 s of CFD simulation is needed to calculate LAA t(m) and C(∞) values reliably. CONCLUSIONS: Subject-specific LA and LAA geometries, CO, and hematocrit level are essential to quantify the subject-specific proclivity of blood cell tarrying inside LAA in terms of the RTD function.