Computational Fluid Dynamics Evaluation of the Cross-Limb Stent Graft Configuration for Endovascular Aneurysm Repair
Bibliographic record
Abstract
The technique of crossing the limbs of bifurcated modular stent grafts for endovascular aneurysm repair (EVAR) is often employed in the face of splayed aortic bifurcations to facilitate cannulation and prevent device kinking. However, little has been reported about the implications of cross-limb EVAR, especially in comparison to conventional EVAR. Previous computational fluid dynamics studies of conventional EVAR grafts have mostly utilized simplified planar stent graft geometries. We herein examined the differences between conventional and cross-limb EVAR by comparing their hemodynamic flow fields (i.e., in the "direct" and "cross" configurations, respectively). We also added a "planar" configuration, which is commonly found in the literature, to identify how well this configuration compares to out-of-plane stent graft configurations from a hemodynamic perspective. A representative patient's cross-limb stent graft geometry was segmented using computed tomography imaging in Mimics software. The cross-limb graft geometry was used to build its direct and planar counterparts in SolidWorks. Physiologic velocity and mass flow boundary conditions and blood properties were implemented for steady-state and pulsatile transient simulations in ANSYS CFX. Displacement forces, wall shear stress (WSS), and oscillatory shear index (OSI) were all comparable between the direct and cross configurations, whereas the planar geometry yielded very different predictions of hemodynamics compared to the out-of-plane stent graft configurations, particularly for displacement forces. This single-patient study suggests that the short-term hemodynamics involved in crossing the limbs is as safe as conventional EVAR. Higher helicity and improved WSS distribution of the cross-limb configuration suggest improved flow-related thrombosis resistance in the short term. However, there may be long-term fatigue implications to stent graft use in the cross configuration when compared to the direct configuration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".