Abstract
Objective:
The aim of this study was to perform patient-specific hemodynamic simulations of thoracoabdominal aortic aneurysm (TAAA) models and evaluate the potential for intraluminal thrombus (ILT) formation.
Methods:
Computed Tomography Angiography data from 12 TAAA patients were reconstructed into patient-specific models, which were grouped according to the presence or absence of ILT accumulation. Old blood volume fraction (OBVF) and convergence time were proposed to reveal the association between thrombotic potential and blood stasis in TAAA.
Results:
The aneurysm exhibited high blood stasis compared with other parts. In the ILT group, normalized OBVF and convergence time (a passive-tracer washout time, not a physiological thrombosis timescale) were 76.66% ± 12.38% and 42.58 ± 13.29 s, respectively, whereas in the non-ILT group, they were 21.42% ± 3.39% and 10.93 ± 5.96 s. The OBVF and convergence time were significantly greater in the ILT group than in the non-ILT group (P-values were <0.0001, <0.01), indicating pronounced blood stasis.
Conclusion:
The blood stasis model provides a new and intuitive approach for clinicians to diagnose and treat ILT in TAAA, offering clearer insights than parameters related to WSS. This model can provide useful guidance for operative planning and postoperative evaluation.
1 Introduction
Thoracoabdominal aortic aneurysm (TAAA) is a prevalent cardiovascular disorder attributed to multifactorial etiologies, including tobacco exposure, genetic predisposition, and atherosclerotic pathogenesis (). This clinically silent condition demonstrates asymptomatic progression in most patients, yet carries catastrophic consequences upon rupture, with associated mortality rates surging to approximately 50% (, ). Current clinical guidelines recommend prophylactic intervention when aneurysmal dilation exceeds 5 cm in maximal diameter to mitigate rupture risk and optimize therapeutic outcomes (, ). Emerging evidence delineates a complex pathophysiological cascade driving aneurysmal degeneration, characterized by phenotypic modulation of vascular smooth muscle cells (VSMCs), leukocytic infiltration, extracellular matrix degradation, and progressive intraluminal thrombus (ILT) formation (, ). Notably, ILT has garnered increasing scientific interest due to its dual role in influencing hemodynamic wall stress distribution () and perpetuating localized inflammatory responses through cytokine-mediated pathways.
The progression of TAAA is closely linked to complex blood flow changes, primarily influenced by aneurysmal volumetric parameters and morphological characteristics (). Comparative analyses with normal aortic architecture reveal that aneurysmal lumens exhibit significantly amplified retrograde flow components, characterized by substantially diminished flow velocities and pathologically oscillatory wall shear stress (WSS) patterns (). ILT significantly impacts aneurysm blood flow patterns by reducing internal circulation and altering nearby vessel structure, which further modifies flow characteristics (–). Biomechanical analyses of TAAA rupture mechanisms consistently identify predilection sites exhibiting triad characteristics: critically reduced WSS magnitudes, elevated oscillatory shear index (OSI) values, and substantial ILT accumulation (, ). Areas of critical low average wall stress (TAWSS) show accelerated growth (>9 mm/year) and ILT development before rupture (). These findings emphasize the importance of tracking blood flow patterns and ILT formation to better understand aneurysm progression and improve rupture prediction.
Several studies have confirmed that low TAWSS in aneurysms () is associated with the accumulation of ILT (, ) and vascular hypoxia (, ), leading to localized vascular damage and even rupture. In contrast, other studies have shown that vessels with high WSS levels (>30 dynes/cm2) in the AAA are more inclined to rupture ().
It is generally believed that low TAWSS combined with high OSI promotes ILT formation, which may lead to enlargement or rupture of the AAA; however, it has also been reported that in small-diameter AAA, low OSI levels promote ILT formation in the aneurysm sac, and that when the diameter of the AAA increases, the OSI value does not play a role in ILT deposition (). Thus, the assessment of ILT using traditional hemodynamic parameters may yield conflicting results. Endothelial Cell Activation Potential (ECAP) (), defined as the ratio of OSI to TAWSS, characterizes the susceptibility of the arterial wall to thrombosis. Relative Residence Time (RRT) has recently been developed as a new hemodynamic component to characterize the relatively slow blood flow in the vicinity of the aneurysm wall (, ). Both novel parameters demonstrate significant predictive potential for thrombogenesis.
Blood stasis is a critical determinant of intra-aneurysmal thrombosis, where reduced flow velocities, impaired metabolite clearance, and hypoxic conditions collectively promote thrombus formation (, ). It is well known that Virchow's triad includes three categories of factors that may contribute to thrombosis: hypercoagulability, hemodynamic changes (turbulence or stasis), and endothelial injury/dysfunction. Blood stasis, as an important component, is different from conventional hemodynamic parameters, which only describe the changes in blood flow within the vessel walls. Instead, blood stasis represents a volumetric fluid dynamic state and may have a closer correlation with thrombosis. This investigation implemented a blood stasis model to quantify global flow stagnation, utilizing two-phase flow computational modeling to predict ILT development patterns in TAAAs.
In this study, 12 patients diagnosed with thoracoabdominal aortic aneurysms at Nanjing Drum Tower Hospital were enrolled. Patients were divided into Group A (NILT group, which means without ILT) and Group B (ILT group). Patient-specific models were constructed by collecting CTAs for both groups. Numerical hemodynamic simulations were carried out to simulate blood flushing and stagnation within the aneurysm. The blood stasis model was applied with conventional WSS-related hemodynamic parameters to compare the hemodynamic differences between the two groups, with the aim of exploring the link between ILT formation and blood stasis within thoracoabdominal aortic aneurysms.
2 Methods
2.1 Patient-specific models
A total of 12 patient-specific models were reconstructed from CTA images using Mimics 20.0 (Materialise, Plymouth, MI, USA). Surface smoothing was performed in Geomagic 20.0 (3D Systems, Inc., Rock Hill, SC, USA) to enhance the robustness of simulations. Notably, the ILT component in the aneurysm was removed and considered part of the fluid domain. By predicting blood stasis in this region, the risk of thrombosis was assessed, as shown in Figure 1. Patient-specific models and CTA images at the site of maximal aneurysm diameter are shown in Figure 2.
Figure 1
Figure 2
Patient-specific information is summarized in Table 1, including thrombus volume and the ratio of thrombus in the thrombus group. The mean ages of patients in Group A and Group B were 69.17 ± 10.83 years and 67.50 ± 9.65 years, respectively. There was no significant difference in age between the two groups (P = 0.784). Differences in gender and aneurysm morphology were also avoided to minimize interference with the results. Notably, aneurysm size remains a key factor affecting rupture and ILT formation. Therefore, the maximum diameters of aneurysms in the thrombus group were larger than those in the non-thrombus group.
Table 1
| Group | Case | Gender | Age (years) | Aneurysm maximum diameter (mm) | Aneurysm volume (cm3) | Aneurysm topology | Thrombus volume (cm3) | Ratio of thrombus |
|---|---|---|---|---|---|---|---|---|
| Group A (NILT) | Case A1 | Female | 50 | 40.3 | 35 | Fusiform | — | — |
| Case A2 | Male | 65 | 59.2 | 118 | Saccular | — | — | |
| Case A3 | Male | 77 | 76.9 | 163 | Saccular | — | — | |
| Case A4 | Male | 77 | 53.5 | 255 | Fusiform | — | — | |
| Case A5 | Male | 68 | 41 | 42 | Fusiform | — | — | |
| Case A6 | Female | 78 | 70.8 | 169 | Saccular | — | — | |
| Group A (ILT) | Case B1 | Male | 68 | 72.8 | 427 | Saccular | 329 | 77.05% |
| Case B2 | Male | 62 | 83 | 337 | Saccular | 249 | 73.89% | |
| Case B3 | Male | 81 | 76.4 | 306 | Saccular | 160 | 52.29% | |
| Case B4 | Male | 53 | 91 | 694 | Fusiform | 398 | 57.35% | |
| Case B5 | Female | 67 | 76.2 | 236 | Saccular | 180 | 76.27% | |
| Case B6 | Male | 74 | 62.6 | 306 | Fusiform | 204 | 58.96% | |
| P value | — | — | P = 0.784 | P < 0.05 | P < 0.01 | — | — | — |
Patient specifications.
2.2 Mesh and boundary condition
The aneurysm were divided into separate fluid domains to simulate blood stasis. The inlet was positioned at the aortic root. All inlets and outlets were extended to suppress computational instability, which may arise due to backflow. For mesh sensitivity analysis, three grids of 8.24, 4.57, and 2.18 million elements were generated for Case A1. The grid of 4.57 million elements was confirmed as the appropriate mesh for simulation (see Section 3). Grids of 4.35–6.84 million elements were generated for the remaining 11 models using commercial software Ansys Meshing (Ansys, Inc., Canonsburg, PA, USA), with a similar setup as the 4.57-million grid. Five grid layers were added to all the arterial walls to correctly resolve the boundary layer and guarantee accuracy in simulation, as shown in Figure 3. A time-varying volumetric flow rate, extracted from the literature, was applied at the inlet of each model for a period of 1 s (). Windkessel Proximal Resistance, Capacitance and Distal Resistance (RCR) boundary conditions were applied at the outlets. Three Element Windkessel Model (EWM) parameters were calculated through patient-specific iteration, with values provided in Supplementary Table S1. All walls were assumed to be rigid with no slip conditions.
Figure 3
2.3 Numerical simulation
All single-phase flow simulations in this study were transient and conducted using commercial software Ansys Fluent 22R1 (Ansys, Inc., Canonsburg, PA, USA). Blood was regarded as an incompressible Newtonian fluid, with a density of 1,055 kg/m3 and dynamic viscosity of 3.5 × 10−3 Pa s. Since the potential presence of turbulence within the aorta (, ), the flow was assumed to be turbulent using the k-ω Shear-Stress Transport (SST) model. The peak Reynolds numbers ranged from 362 to 1,805, while the mean Reynolds numbers ranged from 55 to 323. The y+ values were all close to 1. A second-order implicit backward Euler scheme was chosen for temporal discretization, with a fixed time step of 10 ms, so that each cardiac cycle was resolved using 100 time steps. A maximum of 50 sub-iterations were used for each physical time step, and the maximum RMS residual was set to 10−5 as a convergence criterion. Unsteady simulations were carried out for approximately 10 cardiac cycles to obtain statistically converged flow fields, followed by another 10 cardiac cycles to achieve hemodynamic parameters such as TAWSS and RRT.
A two-fluid model was employed to simulate the process of blood stasis, continuing from the converged single-fluid flow field, in accordance with prior studies (, ). The computational setup was identical to the single-fluid runs, except that the VOF method was employed to solve the two-fluid flow field. New blood would gradually replace old blood, blood stasis would be tracked and monitored over time. Considering computational resources and time costs, convergence criteria were set that the old blood volume fractions (OBVFs) dropped within 5% in the past 10 cardiac cycles for all cases, and the corresponding time is the convergence time. Differences in blood stasis were evaluated, by comparing convergent OBVF values and convergence times of the two groups, and then correlated with ILT deposition. All computations were performed on a 192-core cluster equipped with 16 Intel Xeon E5-2680 v3 CPUs. Single-fluid simulations normally converged within 4 h, while two-phase flow simulations took less than 2 days.
3 Results
3.1 Grid sensitivity analysis
Grid sensitivity analysis was conducted for Case A1. The OBVF within the aneurysm at the fifth cardiac cycle was compared to the results predicted with the fine mesh. OBVFs are presented in Table 2. The differences between the results of the “Middle” and “Fine” mesh were almost negligible when compared with the “Coarse” mesh. Therefore, the middle grid was employed for the analyses, and similar setups were employed when generating grids for the remaining cases.
Table 2
| Mesh | Cells (×106) | OBVF (%) | Error of OBVF (%) |
|---|---|---|---|
| Coarse | 2.18 | 20.3 | 5.2 |
| Middle | 4.57 | 19.6 | 1.6 |
| Fine | 8.24 | 19.3 | / |
Results of grid sensitivity analysis.
Error of OBVF (%), defined as |OBVF-OBVF0|/OBVF0, where OBVF0 is the OBVF of the aneurysm at the fifth cardiac cycle predicted with the fine mesh.
3.2 Flow patterns
Blood flow within the aneurysm was slow and dominated by whirlpools, particularly near the entrance, where the flow rate decreased due to dilation of aorta, resulting in reflux and secondary flow. The flow streamlines at the systolic peak of the two groups are shown in Figure 4. By comparing the flow rate of aneurysms in Group A and Group B, it was noted that the velocity in the non-thrombotic group was significantly higher than that in the thrombotic group. Notably, the streamlines of aneurysm in Group B were dark blue, indicating low-velocity conditions, generally less than 0.1 m/s. By observing the flow patterns of these two groups, it was noted that the streamlines in Group A appeared more chaotic than those in Group B, especially in Case A3 and Case A6. In Group A, the main flow in the aneurysm presented a spiral state, and the entire flow field was full of disordered and chaotic multiple eddies. In contrast, the flow pattern in the thrombus group was mainly non-vortex, with only small eddies at the inlet and outlet of the aneurysm, with the flow field being relatively stable.
Figure 4
3.3 TAWSS and OSI
TAWSS and OSI are commonly used hemodynamic parameters to assess the risk of aneurysm rupture and thrombosis. It is generally accepted that low TAWSS and high OSI predispose one to thrombosis (31, 32). The distribution of TAWSS and OSI in the two groups is shown in Figures 5a,b, respectively. In the non-thrombotic group, the distribution of TAWSS in aneurysmal regions was not significantly different from that in other regions, and only low-TAWSS areas appeared locally in the aneurysm. Similarly, the overall OSI values across aneurysms in six patients were not significantly increased; in particular, Case A1, Case A3, and Case A6 exhibited low OSI states. Different from Group A, patients in Group B showed a significant decrease of TAWSS across aneurysmal regions, with nearly the entire aneurysm wall of six cases in a state of low TAWSS. On the contrary, large areas of high OSI were observed on the walls of Case B4, Case B5, and Case B6, and the OSI values of the aneurysmal region in the thrombus group were generally higher than those in the non-thrombus group.
Figure 5
Wall-averaged TAWSS and OSI values at the aneurysm were compared between groups, as shown in Figures 5c,d. In this study, an unpaired t-test was employed to compare differences between the two datasets. The average TAWSS value of patients in Group A was 6.78 ± 4.41 dynes/cm2 and that in group B was 0.92 ± 0.47 dynes/cm2. The average TAWSS value in the thrombus group was significantly lower than that in the non-thrombus group (P < 0.01). In contrast, the average OSI value in Group A was 0.139 ± 0.062 and that in Group B was 0.293 ± 0.049. The average OSI value in the thrombus group was significantly higher than that in the non-thrombus group (P < 0.001).
3.4 ECAP and RRT
ECAP and RRT are hemodynamic parameters related to WSS, and it is generally believed that high ECAP and RRT values suggest higher risk of thrombosis (–). As shown in Figures 6a,b, the ECAP and RRT values did not increase significantly in the non-thrombotic group, with only ECAP value of Case A6 being higher in the middle of the aneurysm and ECAP and RRT values of Case A1 and Case A4 being reduced in the aneurysms. In contrast to the non-thrombotic group, the RRT and ECAP values in Group B increased significantly, with entire aneurysms being covered by high RRT and ECAP values.
Figure 6
Wall-averaged ECAP and RRT values in the aneurysmal regions in the two groups are shown in Figures 6c,d. The average ECAP value in the non-thrombotic group (NILT) was 0.28 ± 0.21 Pa−1 and that of the thrombus group (ILT) was 3.92 ± 1.58 Pa−1. The average ECAP on the aneurysm wall of Group A was significantly lower than that of Group B (P < 0.001). Similar to ECAP, the average RRT value in aneurysmal regions of Group A was 3.49 ± 2.48 s and that in Group B was 64.11 ± 34.72 s. The average RRT value of the wall in Group A was also significantly lower than that in Group B (P < 0.01). As mentioned previously, the simulation results suggested that the discrimination of ILT by ECAP and RRT values was consistent with the clinical manifestations.
3.5 Blood stasis
Using the two-fluid model, the distribution of old blood was predicted and to characterize blood stasis, as shown in Figure 7a. In both groups, red regions remained in the aneurysms; the thrombus group exhibited a significantly deeper color than the non-thrombus group, particularly in Case B1, Case B2, and Case B5, which meant that blood stasis was more obvious than that in the non-thrombus group.
Figure 7
The OBVF and convergence time for aneurysms in the two groups are shown in Figures 7b,c. To minimize the influence of aneurysm size and maximum diameter, OBVF and convergence time were normalized by diameter and volume, respectively. As shown in Figure 7b, the convergence OBVF of aneurysms in Group A (NILT) was 21.42% ± 3.39% and that in Group B (ILT) was 76.66% ± 12.38%. The OBVF in the non-thrombotic group was significantly lower than that in the thrombotic group (P < 0.0001). As shown in Figure 7c, the convergence time of aneurysms in the non-thrombotic group was 10.93 ± 5.96 s and that in the thrombotic group was 42.58 ± 13.29 s. The convergence time in Group A was significantly lower than that in Group B (P < 0.001). This simulation demonstrated that the degree of blood stasis in the thrombotic group was significantly higher than that in the non-thrombotic group, which was better associated with the formation of ILT. It can be indicated that the degree and location of thrombosis can be associated well by using blood stasis model.
4 Discussion
Blood stasis plays an important role in the thrombosis of thoracic and abdominal aortic aneurysms. Slow flow leads to platelet aggregation and promotes the hypercoagulability of blood, causing a lack of oxygen in the arterial wall. In this study, blood stasis was predicted using a two-fluid model applied to thoracic and abdominal aortic aneurysms. Through hemodynamic simulation of six patients in each group (thrombus versus non-thrombus), it was found that the blood flow in aneurysms of the thrombus group was slow, while that in the non-thrombus group was fast with complex flow patterns. Under such circumstances, the OBVF and convergence time indicated that blood stasis was more severe in the thrombus group.
In previous studies, conventional hemodynamic parameters such as TAWSS and OSI were often used to evaluate the potential of ILT, and it was generally believed that regions with low TAWSS, high OSI, high ECAP, and high RRT were prone to the formation of ILT (–). In this study, simulations confirmed that aneurysm walls in the thrombus group had low TAWSS, high OSI, high ECAP, and high RRT. On the contrary, aneurysmal regions in the non-thrombotic group were not significantly different from other areas, and some cases even exhibited high TAWSS and low OSI. The difference in conventional hemodynamic parameters between the two groups demonstrated that the wall-averaged TAWSS in the thrombus group was significantly lower than that in the non-thrombus group (P < 0.01), while the wall-averaged OSI, ECAP, and RRT were significantly higher than those in the non-thrombotic group (POSI < 0.001, PECAP < 0.001, PRRT < 0.01), consistent with previous research results.
In most studies, the combination of low TAWSS and high OSI has been associated with aneurysm growth and rupture (33–36). In the present study, regions with low TAWSS (<4 dynes/cm2) and high OSI (>0.3) were more extensive in the thrombus group than the non-thrombus group, consistent with previous findings. However, some research has reported contrasting results. For example, Arzani and Shadden (37) found that thrombosis commonly formed in regions where TAWSS ranged between 2 and 3 dynes/cm2, while OSI was negatively correlated with thrombosis accumulation. Notably, ILT accumulation was not observed in regions with high OSI (>0.4) and low TAWSS (<1 dynes/cm2). Other studies have shown that ILT formation can occur in either high or low OSI regions, indicating that OSI may not be directly related to ILT (, 38). As there are discrepancies in assessing ILT formation using TAWSS and OSI, new metrics are needed to assess ILT formation. In this research, convergence OBVF and convergence time were considered hemodynamic indicators in the blood stasis model. Unlike WSS-related parameters, these two parameters, describing the spatial and temporal characteristics of blood stasis, offered more advantageous associations with ILT formation in aneurysms. It can be observed that OBVF and convergence time were significantly greater in the thrombus group than in the non-thrombus group (POBVF < 0.0001, Pconvergence time < 0.001). More importantly, among the six patients in Group B, there was a high degree of thrombosis (defined as the ratio of volume of ILT and aneurysm) in Case B1, Case B2, and Case B5 (with the degree value above 70%), while the degree of thrombosis in the other three cases was below 60%. The OBVF of the first three patients was above 82%, with values less than 69% for the remaining three patients. It was observed that the difference between OBVF and the degree of thrombosis in each case was less than 15%, while the relevant parameters of WSS could not accurately distinguish variations in the degree of thrombosis. Notably, given the limited sample size, this finding was purely observational and did not reach statistical significance. Nevertheless, this phenomenon still merits further investigation into the correlation between OBVF and the degree of thrombosis.
Blood stasis increases the possibility of platelet adhesion and deposition, leading to thrombosis. In fact, thrombosis is a very complex process, including a series of cascade reactions involving endothelial injury and platelet activation. Nevertheless, this study provided a highly efficient and rapid alternative for evaluating ILT accumulation in TAAA. The blood stasis model can be used to predict the influence of morphological parameters on thrombosis, offering clinicians valuable insights to predict the possibility of ILT formation in TAAA. While RRT describes the relative time of blood retention on the wall of the blood vessel—with high RRT regions prone to thrombosis—blood stasis occurs not only in the wall but also in the overall blood flow field of aneurysms. Therefore, OBVF, as a parameter to describe the volume of blood stasis, is more intuitive than RRT in associating with thrombosis potential. Although RRT can indicate the location of ILT, it cannot distinguish the degree of thrombus formation across different patients. However, the OBVF values of individual patients within the group exhibited a strong correlation with the thrombosis ratios. In addition, recent studies have proposed various ways to describe blood stasis. Rayz et al. (39) employed washout time within basilar aneurysms as a metric to assess blood stasis, which was subsequently compared against measurements derived from 4D flow, and the convergence time represents a continuation and extension of that concept. In the field of imaging, researchers analyzed the radiomics of CT to describe blood stasis and thus predicted the formation of left atrial appendage thrombosis (40, 41). Mathews et al. (42) described blood stasis in deep veins of lower extremities through platelet count, which was associated with deep venous thrombosis. These studies can provide references for the improvement and optimization of the blood stasis model.
This study has several limitations. First, this research predicted blood stasis rather than directly simulating the process of ILT. Therefore, the convergence time only reflected the time scale of old blood being replaced by new blood. Precise thrombosis simulation is needed to consider the various factors leading to thrombosis and predict the growth of thrombosis on a longer time scale, such as 6 months or 1 year. Therefore, the blood stasis model is currently unable to predict the specific time required for thrombosis formation, and more accurate results are needed, with more patients and longer follow-up observations. Furthermore, an expanded sample size would facilitate validation of the model's sensitivity and its correlation with thrombosis. Second, thrombosis is a very complex process, and this study only considered one element of Virchow's triad, namely, slow and stagnant blood flow, without considering platelet changes and the activation process caused by biochemical factors. The time scale for thrombosis was different from the time scale considered in the present study, but this model was nevertheless sufficient to distinguish the risk of thrombosis in different patients. Third, the non-Newtonian properties of blood are equally non-negligible; shear-thinning effects elevate viscosity in low-shear regions and exacerbate blood stasis—particularly in large-volume aneurysms with low WSS. In future research, non-Newtonian models such as Carreau-Yasuda or Casson should be incorporated into two-phase flow simulations to improve the precision of the blood stasis model. Finally, all simulations were based on CTA images from one period. By collecting images at more time points for clinical follow-up, the process of thrombosis can also be better tracked, offering a more precise reference for thrombosis. Moreover, as a retrospective study, patient-specific waveforms were not available. Future work should include patient-specific measured flow rates and sensitivity analyses for boundary conditions.
In summary, through the hemodynamic simulations of thoracoabdominal aortic aneurysms in the thrombotic and non-thrombotic groups, this study demonstrated that the blood stasis model can provide better associations with ILT formation in TAAA compared with WSS-related parameters, and can directly reflect both the temporal and spatial distribution of ILT. By directly associating thrombosis potential with blood stasis, this model provides a new approach for clinicians to diagnose and treat thrombus in TAAA. It can be used for preoperative evaluation and prognosis assessment of more diseases with complex hemodynamic changes, providing valuable guidance for operative planning and postoperative evaluation.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
Ethics statement
This study involving humans was approved by the Ethics Committee of Nanjing Drum Tower Hospital, affiliated with Nanjing University Medical College. The study was conducted in accordance with local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with national legislation and institutional requirements.
Author contributions
GZ: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft. NH: Conceptualization, Data curation, Formal analysis, Methodology, Validation, Writing – review & editing. PW: Conceptualization, Formal analysis, Funding acquisition, Methodology, Software, Validation, Visualization, Writing – review & editing. ZD: Data curation, Investigation, Resources, Validation, Writing – review & editing. KH: Data curation, Formal analysis, Investigation, Validation, Visualization, Writing – review & editing. XL: Conceptualization, Funding acquisition, Methodology, Project administration, Writing – review & editing. RT: Conceptualization, Data curation, Resources, Writing – review & editing. XJ: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This study was primarily supported by the Natural Science Foundation of China (NSFC, Grant No. 12072216, 12472330, 82370519, and 82070496), the International Technological Joint Project of Jiangsu Province (BZ2024016), and the Mobility Programme of the Sino-German Center (Grant No. M-0231).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2026.1815923/full#supplementary-material
References
1.
KuivaniemiHRyerEJElmoreJRTrompG. Understanding the pathogenesis of abdominal aortic aneurysms. Expert Rev Cardiovasc Ther. (2015) 13:975–87. 10.1586/14779072.2015.1074861
2.
MollFLPowellJTFraedrichGVerziniFHaulonSWalthamMet al. Management of abdominal aortic aneurysms clinical practice guidelines of the European Society for Vascular Surgery. Eur J Vasc Endovasc Surg. (2011) 41(Suppl 1):S1–58. 10.1016/j.ejvs.2010.09.011
3.
BrewsterDCCronenwettJLHallettJWJohnstonKWKrupskiWCMatsumuraJS. Guidelines for the treatment of abdominal aortic aneurysms. Report of a subcommittee of the Joint Council of the American Association for Vascular Surgery and Society for Vascular Surgery. J Vasc Surg. (2003) 37:1106–17. 10.1067/mva.2003.363
4.
SakalihasanNLimetRDefaweO. Abdominal aortic aneurysm. Lancet. (2005) 365:1577–89. 10.1016/S0140-6736(05)66459-8
5.
ShimizuKMitchellRLibbyP. Inflammation and cellular immune responses in abdominal aortic aneurysms. Arterioscler Thromb Vasc Biol. (2006) 26:987–94. 10.1161/01.ATV.0000214999.12921.4f
6.
WangDHJMakarounMSWebsterMWVorpDA. Effect of intraluminal thrombus on wall stress in patient-specific models of abdominal aortic aneurysm. J Vasc Surg. (2002) 36:598–604. 10.1067/mva.2002.126087
7.
KaziMThybergJReligaPRoyJErikssonPHedinUet al. Influence of intraluminal thrombus on structural and cellular composition of abdominal aortic aneurysm wall. J Vasc Surg. (2003) 38:1283–92. 10.1016/S0741-5214(03)00791-2
8.
TanweerOWilsonTAMetaxaERiinaHAMengH. A comparative review of the hemodynamics and pathogenesis of cerebral and abdominal aortic aneurysms: lessons to learn from each other. J Cerebrovasc Endovasc Neurosurg. (2014) 16:335–49. 10.7461/jcen.2014.16.4.335
9.
RisslandPAlemuYEinavSRicottaJBluesteinD. Abdominal aortic aneurysm risk of rupture: patient-specific FSI simulations using anisotropic model. J Biomech Eng. (2009) 131:031001. 10.1115/1.3005200
10.
TaylorCHughesTZarinsC. Finite element modeling of three-dimensional pulsatile flow in the abdominal aorta: relevance to atherosclerosis. Ann Biomed Eng. (1998) 26:975–87. 10.1114/1.140
11.
TaylorCHughesTZarinsC. Effect of exercise on hemodynamic conditions in the abdominal aorta. J Vasc Surg. (1999) 29:1077–89. 10.1016/S0741-5214(99)70249-1
12.
TangBTChengCPDraneyMTWilsonNMTsaoPSHerfkensRJet al. Abdominal aortic hemodynamics in young healthy adults at rest and during lower limb exercise: quantification using image-based computer modeling. Am J Physiol Heart Circ Physiol. (2006) 291:H668–76. 10.1152/ajpheart.01301.2005
13.
KemmerlingEPeattieR. Abdominal aortic aneurysm pathomechanics: current understanding and future directions. Adv Exp Med Biol. (2018) 1097:157–79. 10.1007/978-3-319-96445-4_8
14.
BoydAJKuhnDCSLozowyRJKulbiskyGP. Low wall shear stress predominates at sites of abdominal aortic aneurysm rupture. J Vasc Surg. (2016) 63:1613–9. 10.1016/j.jvs.2015.01.040
15.
MartufiGLindquist LiljeqvistMSakalihasanNPanuccioGHultgrenRRoyJet al. Local diameter, wall stress, and thrombus thickness influence the local growth of abdominal aortic aneurysms. J Endovasc Ther. (2016) 23:957–66. 10.1177/1526602816657086
16.
DoyleBMcgloughlinTKavanaghEHoskinsPR. “From detection to rupture: a serial computational fluid dynamics case study of a rapidly expanding, patient-specific, ruptured abdominal aortic aneurysm”. In: Computational Biomechanics for Medicine. eds. Doyle B, Miller K, Wittek A, Nielsen P. New York: Springer (2014). p. 53–68. 10.1007/978-1-4939-0745-8_5
17.
McClartyDKuhnDCBoydA. Hemodynamic changes in an actively rupturing abdominal aortic aneurysm. J Vasc Res. (2021) 58:172–9. 10.1159/000514237
18.
QiuYWangYFanYPengLLiuRZhaoJet al. Role of intraluminal thrombus in abdominal aortic aneurysm ruptures: a hemodynamic point of view. Med Phys. (2019) 46:4263–75. 10.1002/mp.13658
19.
VorpDALeePCWangDHJMakarounMSNemotoEMOgawaSet al. Association of intraluminal thrombus in abdominal aortic aneurysm with local hypoxia and wall weakening. J Vasc Surg. (2001) 34:291–9. 10.1067/mva.2001.114813
20.
MetaxaETzirakisKKontopodisNIoannouCVPapaharilaouY. Correlation of intraluminal thrombus deposition, biomechanics, and hemodynamics with surface growth and rupture in abdominal aortic aneurysm—application in a clinical paradigm. Ann Vasc Surg. (2018) 46:357–66. 10.1016/j.avsg.2017.08.007
21.
DolanJKolegaJMengH. High wall shear stress and spatial gradients in vascular pathology: a review. Ann Biomed Eng. (2013) 41:1411–27. 10.1007/s10439-012-0695-0
22.
LozowyRJKuhnDCSDucasAABoydAJ. The relationship between pulsatile flow impingement and intraluminal thrombus deposition in abdominal aortic aneurysms. Cardiovasc Eng Technol. (2017) 8:57–69. 10.1007/s13239-016-0287-5
23.
Di AchillePTellidesGFigueroaCHumphreyJD. A haemodynamic predictor of intraluminal thrombus formation in abdominal aortic aneurysms. Proc R Soc A Math Phys Eng Sci. (2014) 470:20140163. 10.1098/rspa.2014.0163
24.
XiangJNatarajanSKTremmelMMaDMoccoJHopkinsLNet al. Hemodynamic-morphologic discriminants for intracranial aneurysm rupture. Stroke. (2011) 42:144–52. 10.1161/STROKEAHA.110.592923
25.
LeeSAntigaLSteinmanD. Correlations among indicators of disturbed flow at the normal carotid bifurcation. J Biomech Eng. (2009) 131:061013. 10.1115/1.3127252
26.
CaoHLiDLiYQiuYLiuJPuHet al. Role of occlusion position in coronary artery fistulas with terminal aneurysms: a hemodynamic perspective. Cardiovasc Eng Techn. (2020) 11:394–404. 10.1007/s13239-020-00468-w
27.
JiangXCaoHZhangZZhengTLiXWuP. A hemodynamic analysis of the thrombosis within occluded coronary arterial fistulas with terminal aneurysms using a blood stasis model. Front Physiol. (2022) 13:906502. 10.3389/fphys.2022.906502
28.
ChengZTanFPPRigaCVBicknellCDHamadyMSGibbsRGJet al. Analysis of flow patterns in a patient-specific aortic dissection model. J Biomech Eng. (2010) 132:051007. 10.1115/1.4000964
29.
MandellJGLokeY-HMassPNClevelandVDelaneyMOpfermannJet al. Altered hemodynamics by 4D flow cardiovascular magnetic resonance predict exercise intolerance in repaired coarctation of the aorta: an in vitro study. J Cardiovasc Magn Reson. (2021) 23:99. 10.1186/s12968-021-00796-3
30.
ManchesterELPirolaSSalmasiMYO’ReganDPAthanasiouTXuXY. Evaluation of computational methodologies for accurate prediction of wall shear stress and turbulence parameters in a patient-specific aorta. Front Bioeng Biotechnol. (2022) 10:836611. 10.3389/fbioe.2022.836611
31.
JiangPLiuQWuJChenXLiMLiZet al. A novel scoring system for rupture risk stratification of intracranial aneurysms: a hemodynamic and morphological study. Front Neurosci. (2018) 12:596. 10.3389/fnins.2018.00596
32.
AlgabriYARookkapanSGramignaVEspinoDMChatpunS. Computational study on hemodynamic changes in patient-specific proximal neck angulation of abdominal aortic aneurysm with time-varying velocity. Australas Phys Eng Sci Med. (2019) 42:181–90. 10.1007/s13246-019-00728-7
33.
TzirakisKKamarianakisYMetaxaEKontopodisNIoannouCVPapaharilaouY. A robust approach for exploring hemodynamics and thrombus growth associations in abdominal aortic aneurysms. Med Biol Eng Comput. (2017) 55:1493–506. 10.1007/s11517-016-1610-x
34.
MooreJEMaierSEKuDNBoesigerP. Hemodynamics in the abdominal aorta: a comparison of in vitro and in vivo measurements. J Appl Physiol (1985). (1994) 76:1520–7. 10.1152/jappl.1994.76.4.1520
35.
ZambranoBAGharahiHLimCJaberiFAChoiJLeeWet al. Association of intraluminal thrombus, hemodynamic forces, and abdominal aortic aneurysm expansion using longitudinal CT images. Ann Biomed Eng. (2016) 44:1502–14. 10.1007/s10439-015-1461-x
36.
PedersenEMAgerb˦kMKristensenIBYoganathanAP. Wall shear stress and early atherosclerotic lesions in the abdominal aorta in young adults. Eur J Vasc Endovasc Surg. (1997) 13:443–51. 10.1016/S1078-5884(97)80171-2
37.
ArzaniAShaddenSC. Characterization of the transport topology in patient-specific abdominal aortic aneurysm models. Phys Fluids (1994). (2012) 24:81901. 10.1063/1.4744984
38.
ZambranoBAGharahiHLimCYLeeWBaekS. Association of vortical structures and hemodynamic parameters for regional thrombus accumulation in abdominal aortic aneurysms. Int J Numer Method Biomed Eng. (2022) 38:e3555. 10.1002/cnm.3555
39.
RayzVZaidatOHalbachVSalonerDLawtonM. P-023 computational modeling of postoperative flow and thrombosis in cerebral aneurysms. J Neurointerv Surg. (2015) 7:A34. 10.1136/neurintsurg-2015-011917.62
40.
ChunSHSuhYJHanKParkSJShimCYHongG-Ret al. Differentiation of left atrial appendage thrombus from circulatory stasis using cardiac CT radiomics in patients with valvular heart disease. Eur Radiol. (2021) 31:1130–9. 10.1007/s00330-020-07173-1
41.
LiXCaiYChenXMingYHeWLiuJet al. Radiomics based on single-phase CTA for distinguishing left atrial appendage thrombus from circulatory stasis in patients with atrial fibrillation before ablation. Diagnostics (Basel). (2023) 13:2474. 10.3390/diagnostics13152474
42.
MathewsRSetthavongsackNLe-CookAKaempfALoftisJMWoltjerRLet al. Role of platelet count in a murine stasis model of deep vein thrombosis. Platelets. (2024) 35:2290916. 10.1080/09537104.2023.2290916
Summary
Keywords
blood stasis, Computational Fluid Dynamics (CFD), ILT, OBVF, thoracoabdominal aortic aneurysm
Citation
Zhao G, Hu N, Wu P, Dong Z, Han K, Li X, Tao R and Jiang X (2026) Association of intraluminal thrombus in thoracoabdominal aortic aneurysms with a blood stasis model. Front. Cardiovasc. Med. 13:1815923. doi: 10.3389/fcvm.2026.1815923
Received
23 February 2026
Revised
09 June 2026
Accepted
16 June 2026
Published
02 July 2026
Volume
13 - 2026
Edited by
Kohei Tatsumi, Nara Medical University, Japan
Reviewed by
Yogesh Karnam, University of Wisconsin-Madison, United States
Xuehuan Zhang, Beijing Institute of Technology, China
Updates
Copyright
© 2026 Zhao, Hu, Wu, Dong, Han, Li, Tao and Jiang.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xudong Jiang jiangxudong2025@fy.ahmu.edu.cn Ran Tao taoran@ahmu.edu.cn Xiaoqiang Li flytsg@126.com
† These authors have contributed equally to this work
Disclaimer
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