Non-Newtonian blood rheology impacts left atrial stasis in patient-specific simulations.
Alejandro GonzaloManuel García-VillalbaLorenzo RossiniEduardo DuránDavis M VigneaultPablo Martínez-LegazpiOscar FloresJavier BermejoElliot McVeighAndrew M KahnJuan C Del AlamoPublished in: International journal for numerical methods in biomedical engineering (2022)
The lack of mechanically effective contraction of the left atrium (LA) during atrial fibrillation (AF) disturbs blood flow, increasing the risk of thrombosis and ischemic stroke. Thrombosis is most likely in the left atrial appendage (LAA), a small narrow sac where blood is prone to stagnate. Slow flow promotes the formation of erythrocyte aggregates in the LAA, also known as rouleaux, causing viscosity gradients that are usually disregarded in patient-specific simulations. To evaluate these non-Newtonian effects, we built atrial models derived from 4D computed tomography scans of patients and carried out computational fluid dynamics simulations using the Carreau-Yasuda constitutive relation. We examined six patients, three of whom had AF and LAA thrombosis or a history of transient ischemic attacks (TIAs). We modeled the effects of hematocrit and rouleaux formation kinetics by varying the parameterization of the Carreau-Yasuda relation and modulating non-Newtonian viscosity changes based on residence time. Comparing non-Newtonian and Newtonian simulations indicates that slow flow in the LAA increases blood viscosity, altering secondary swirling flows and intensifying blood stasis. While some of these effects are subtle when examined using instantaneous metrics like shear rate or kinetic energy, they are manifested in the blood residence time, which accumulates over multiple heartbeats. Our data also reveal that LAA blood stasis worsens when hematocrit increases, offering a potential new mechanism for the clinically reported correlation between hematocrit and stroke incidence. In summary, we submit that hematocrit-dependent non-Newtonian blood rheology should be considered when calculating patient-specific blood stasis indices by computational fluid dynamics.
Keyphrases
- atrial fibrillation
- left atrial
- left atrial appendage
- catheter ablation
- computed tomography
- end stage renal disease
- oral anticoagulants
- blood flow
- pulmonary embolism
- chronic kidney disease
- molecular dynamics
- heart failure
- gene expression
- magnetic resonance
- positron emission tomography
- direct oral anticoagulants
- mitral valve
- newly diagnosed
- monte carlo
- deep learning
- prognostic factors
- risk factors
- pulmonary hypertension
- acute coronary syndrome
- coronary artery
- coronary artery disease
- machine learning
- patient reported outcomes
- smooth muscle
- pulmonary artery
- subarachnoid hemorrhage
- data analysis