O-071 Estimation of optimal coil packing density using lagrangian particle tracking
Bibliographic record
Abstract
Background Aneurysm coil packing density has been studied as a surrogate for treatment outcome, but clinical results are conflicting. Previous work applying computational fluid dynamics simulations to coil embolization of cerebral aneurysms found that embolization increases residence time (RT) and lowers cumulative shear (shear history; SH) of blood components within the aneurysm dome, suggesting that embolization promotes clot formation via a low shear stress-mediated pathway associated with stagnation of flow.1 The goal of this investigation is to determine whether RT and SH can be used to identify optimal coil packing density for coiled cerebral aneurysm outcome prediction, using particle tracking simulations. Method Computational fluid dynamics simulations of patient-specific aneurysms were performed before and after coil embolization treatment. Massless particles were virtually injected and individually tracked in each simulation, and blood flow was simulated with patient-specific boundary conditions. The coil mass was treated as a porous medium with a porosity corresponding to the in vivo treatment packing density. Simulations were also run with porosities corresponding to 50%, 150%, and 200% of the in vivo treatment packing density for each subject. Results Five subjects were included. The relative decrease in the rate of particles entering an aneurysm had a strong correlation with increasing packing density (R2 = 0.944, P < 0.001). A packing density of approximately 33.3% resulted in approximately a 70% reduction in particles entering the aneurysm. Above this packing density, the absolute number of particles was considered to be too low to be statistically reliable, and therefore further particle tracking analyses focused on simulations with packing densities less than 33.3%. Within these simulations, increasing packing density was associated with a linear increase in RT (R2 = 0.462, P < 0.003) and a decrease in SH (R2 = 0.547, P < 0.001), with SH nearing 0 as packing density approached 33.3%. Conclusions The results show that increasing packing density has a predictable effect on biologically relevant metrics calculated via computational fluid dynamics with particle tracking. These changes are thought to reflect alterations in the biomechanical microenvironment that promote stable thrombus formation, which is critical for the success of endovascular therapies. Our results suggest that the maximal hemodynamic effects of packing density may be achieved near a 33.3% threshold. Reference Bass DI, Marsh LMM, Fillingham P, Lim D, Chivukula VK, Kim LJ, et al. Modeling the mechanical microenvironment of coiled cerebral aneurysms. J Biomech Eng. 2023;145(4):1-8. Disclosures D. Bass: None. L. Marsh: None. M. Barbour: None. V. Chivukula: None. P. Fillingham: None. L. Kim: None. A. Aliseda: None. M. Levitt: 1; C; Medtronic: Investigator-initiated unrestricted educational grant; consultant Stryker: Investigator-initiated unrestricted educational grant, Stryker: Investigator-initiated unrestricted educational grant. 2; C; Medtronic, Stereotaxis, Metis Innovative. 4; C; Fluid Biomed. 5; C; Aeaean Advisers. 6; C; JNIS and Frontiers in Surgery Editorial Boards.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".