O-071 Estimation of optimal coil packing density using lagrangian particle tracking
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».