# Computational Flow Dynamic Analysis in Left Atrial Appendage Thrombus Formation Risk: A Review
## 2 CFD Simulation for Thrombs Formation Risk
### Works Selection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & \begin{tabular}{c} **Number of** \\ **Participants/Cases** \\ \end{tabular} & \begin{tabular}{c} **Population** \\ **Characteristics** \\ \end{tabular} & \
\begin{tabular}{c} **Summary** \\ \end{tabular} &
\begin{tabular}{c} **Outcomes Measured** \\ \end{tabular} \\ \hline
[43] & 1 & \begin{tabular}{c} Patients with left \\ ventricular assist \\ devices \\ \end{tabular} & \begin{tabular}{c} Using patient-specific simulations, the study \\ investigated the effect of left atrial appendage occlusion \\ \end{tabular} & \
\begin{tabular}{c} \(\bullet\) \\ (LAAO) on thrombolysis-related parameters. It found \\ that LAAG significantly decreases the atrial stagnation \\ volume and the time blood remains in the LAA \\ \end{tabular} &
\begin{tabular}{c} Atrial velocity \\ Atrial stagnation volume \\ Blood residence time in the LAA \\ \end{tabular} \\ \hline
[44] & 2 & \begin{tabular}{c} Patients with different \\ shapes and volumes \\ of LAA \\ \end{tabular} & \
\begin{tabular}{c} CFD simulations are used to study the blood flow in \\ the LAA. The study found that the morphology of the \\ appendage can affect the risk of thrombosis formation. \\ \end{tabular} &
\begin{tabular}{c} Velocity \\ Vorticity \\ Blood flow rate \\ \end{tabular} \\ \hline
[45] & 2 & \
\begin{tabular}{c} A novel model was presented to elucidate relations \\ between catheter temperature, patient-specific atrial \\ anatomy, and blood velocity and predict how they \\ change from SR to AF. The models can quantify blood flow in critical regions, including residence times and temperature distribution for different catheter \\ positions, providing a basis for quantifying stroke risks. \\ \end{tabular} &
\begin{tabular}{c} Average blood velocity \\ The percentage of blood remaining in \\ the LA for longer than one cycle \\ The maximum temperatures \\ \end{tabular} \\ \hline
[46] & -- & AF & \
\begin{tabular}{c} A mathematical model to quantify the LAA dead \\ volume is discussed. A novel methodology was \\ presented to study blood stasis risk within the left \\ atrium appendage in the absence of atrial contraction \\ due to fibrillation. \\ \end{tabular} &
\begin{tabular}{c} The proposed index is an effective way \\ of quantifying the LAA dead volume. \\ Sagnation can be detected in the LAA dead volume. \\ Head volume. \\ Head volume is independent of the rest \\ \end{tabular} \\ \hline
[47] & 6 & \begin{tabular}{c} Six people with a wide \\ range of conditions, \\ including normal LA \\ function and a variety \\ of LA dysfunctions, \\ well as subjects in \\ sinus rhythm and \\ with AF \\ \end{tabular} & \
\begin{tabular}{c} The effect of non-Newtonian blood rheology on LA \\ stasis in patient-specific simulations is investigated. \\ The study found that slow flow in the LAA increases \\ blood viscosity, altering secondary swirling flows and \\ intensifying blood stasis. \\ \end{tabular} &
\begin{tabular}{c} Effects of hematocrit and rouleaux \\ formation kinetics on blood viscosity, \\ altering secondary swirling flows and \\ intensifying blood stasis. \\ \end{tabular} \\ \hline
[22] & 3 & \begin{tabular}{c} One healthy patient \\ and two patients with \\ AF \\ \end{tabular} & \begin{tabular}{c} The effects of AF on blood flow and hemodynamic \\ construct models of a healthy left atrium and two atria \\ with fibrillation. The results show that each \\ characteristic phenomenon of AF influences \\ hemodynamics and that high-frequency fibrillation has \\ a significant impact on the stagnation of blood flow. \\ \end{tabular} & \
\begin{tabular}{c} The effect of AF on thrombosis in the left atrium is \\ explored. The study uses a patient-specific anatomical \\ shape of the left atrium and considers the \\ non-Newtonian property of the blood. The results \\ indicate that AF can aggravate thrombosis, which \\ mainly occurs in the LAA. \\ \end{tabular} &
\begin{tabular}{c} Thrombosis risk in the left atrium under atrial \\ fibrillation \\ \end{tabular} \\ \hline
[49] & 6 & LAA Chicken wing & \begin{tabular}{c} To investigate the risk of thrombosis in the LA and LA of AF patients, CT imaging and CFD analysis \\ were utilized. The results revealed that the LAA \\ exhibited lower blood flow velocity and time average \\ wall shear stress values (TAWS), indicating an \\ increased propensity for thrombus formation. \\ Moreover, AF patients exhibited elevated relative \\ residence time (RRT) values in the LAA, highlighting \\ the elevated risk of thrombosis in this population. \\ \end{tabular} & \
\begin{tabular}{c} A sensitivity analysis is performed to identify the most \\ relevant LA and LAA morphological parameters in \\ atrial blood flow dynamics. Simulations were run on \\ synthetic ellipsoidal left atria models where different \\ parameters were individually studied. Additionally, the authors observed that pulmonary vein \\ configuration critically influenced LAA blood \\ flow patterns. \\ \end{tabular} &
\begin{tabular}{c} SENSitivity analysis of different boundary \\ conditions scenarios available in the literature \\ to identify the optimal modeling choices for \\ predicting DRT. \\ \end{tabular} \\ \hline
[50] & 36 & \
\begin{tabular}{c} A sensitivity analysis is performed to identify the most \\ relevant LA and LAA morphological parameters in \\ atrial blood flow dynamics. Simulations were run on \\ synthetic ellipsoidal left atria models where different \\ parameters were individually studied. Additionally, the authors observed that pulmonary vein \\ configuration critically influenced LAA blood \\ flow patterns. \\ \end{tabular} &
\begin{tabular}{c} SENSitivity analysis of different boundary \\ conditions scenarios available in the literature \\ to identify the optimal modeling choices for \\ predicting DRT. \\ \end{tabular} \\ \hline
[51] & 6 & Non-valvular AF & \
\begin{tabular}{c} Simulations of fluid dynamics after LAA occlusion \\ were performed in order to predict thrombus formation \\ after LAA occlusion. The results of the simulation \\ showed that thrombus formation was sensitive to \\ changes in the parameters of the simulation. The \\ authors concluded that the simulation could be used to \\ predict thrombus formation in clinical settings. \\ \end{tabular} &
\begin{tabular}{c} Sensitivity analysis of different boundary \\ conditions scenarios available in the literature \\ to identify the optimal modeling choices for \\ predicting DRT. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}