Evaluation of outcome prediction of flow diversion for intracranial aneurysms

S. Hadad, F. Mut, R. Kadirvel, Y. H. Ding, D. Kallmes, J. R. Cebral

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND AND PURPOSE: Identifying and predicting which aneurysms are likely to quickly occlude and which ones are likely to remain open following treatment with flow-diverting devices is important to develop optimal patient management strategies. The purpose of this study was to evaluate predictions based on computational fluid dynamics models using the elastase rabbit aneurysm model. MATERIALS AND METHODS: A series of 13 aneurysms created in rabbits were treated with flow diverters, and outcomes were angiographically assessed at 8 weeks’ follow-up. Computational fluid dynamics models were constructed from pretreatment 3D rotational angiograms and Doppler ultrasound flow velocity measurements. Postimplantation mean aneurysm inflow rate and flow velocity were used to prospectively predict aneurysm occlusion blinded to the actual outcomes. Specifically, if both variables were below their corresponding thresholds, fast occlusion was predicted, while if one of them was above the threshold, slow or incomplete occlusion was predicted. RESULTS: Of the 13 aneurysms included, 8 were incompletely occluded 8 weeks after treatment, and 5 were completely occluded. A total of 10 computational fluid dynamics–based predictions agreed with the angiographic outcome, reaching 77% accuracy, 80% sensitivity, and 75% specificity. Posttreatment mean velocity alone was able to achieve the same predictive power as the combination of inflow rate and velocity. CONCLUSIONS: Subject-specific computational fluid dynamics models of the hemodynamic conditions created immediately after implantation of flow-diverting devices in experimental aneurysms created in rabbits are capable of prospectively predicting, with a reasonable accuracy, which aneurysms will completely occlude and which ones will remain incompletely occluded.

Original languageEnglish (US)
JournalAmerican Journal of Neuroradiology
Volume42
Issue number11
DOIs
StatePublished - Nov 1 2021

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