Role Of Left Atrial Morphology And In-silico Haemodynamics In Thrombus Formation

  • Mill, Jordi (Universitat Pompeu Fabra)
  • Albors, Carlos (Universitat Pompeu Fabra)
  • Saiz, Marta (Universitat Pompeu Fabra)
  • Olivares, Andy Luis (Universitat Pompeu Fabra)
  • Camara, Oscar (Universitat Pompeu Fabra)

Please login to view abstract download link

Atrial fibrillation (AF) is the most common of the arrythmias and being an important factor for stroke. The lack of rhythmicity favours flow stagnation in a cavity found in the left atrium (LA) between the left superior pulmonary vein and the mitral valve, named left atrial appendage (LAA). In fact, 99% of the thrombi in non-valvular AF patients are found in within this cavity. Studies suggest that abnormal LAA haemodynamics and the subsequently stagnated flow are the factors triggering clot formation in AF patients. However, the relation between LAA morphology, the blood pattern and the triggering is not fully understood. Moreover, the impact of structures such as the pulmonary veins (PVs) on LA haemodynamics has not been thoroughly studied due to the difficulties of acquiring appropriate data. Current imaging techniques (e.g., 4D Flow magnetic resonance imaging) are promising but they cannot reach the required resolution to capture local flow at low velocities, which are key factors in thrombus formation process as the Virchow’s triad states [1] in patients who suffer from atrial fibrillation (AF). On the other hand, in-silico studies and simulations allow a thorough analysis of haemodynamics, analysing the 4D nature of blood ow patterns under different boundary conditions [2]. However, the reduced number of cases reported on the literature of these studies has been a limitation. The main goal of this work was to study the influence of PVs on LA and LAA haemodynamics. Computational fluid dynamics simulations were run on 131 patients, the largest cohort so far in the literature, where different parameters were individually studied: pulmonary veins orientation and configuration; LAA and LA volumes and its ratio; and flow patterns. Our computational and machine learning joint analysis showed how the cases with the highest risk of thrombus formation was characterised by high values of LAA height, tortuosity and ostium perimeter, as well as total number of flow particles in the LAA and low angle between the LAA and the left superior pulmonary vein, proving the usefulness nique to extract knowledge from the data, and early identify AF patients at higher risk of thrombus formation.