E-238 Relative angle of deflection correlates with aneurysmal rupture status in posterior communicating artery aneurysms. (4th August 2020)

• Record Type: Journal Article
• Authors: Damodara, N; Thomas, K; Quinn, J; Gandhi, C; Prestigiacomo, C
• Is Part Of: Journal of neurointerventional surgery. Volume 12(2020)Supplement 1
• Page Start: A158
• Page End: A158
• Publication Date: 2020-08-04
• Subjects: Nervous system -- Surgery -- Periodicals; Cerebrovascular disease -- Surgery -- Periodicals; 617.48
• DOI: 10.1136/neurintsurg-2020-SNIS.269
• Languages: English
• ISSNs: 1759-8478

Abstract

Abstract : Background: The ability to prognosticate rupture for cerebral aneurysms is paramount to prevent the risks inherent to open clipping or endovascular coiling. The goal of this study was to create a mathematical model to predict the probability of rupture incorporating the salient biomorphometric characteristics of the aneurysm. Methods: Posterior communicating artery aneurysms confirmed by computed tomography angiography were subjected to three-dimensional reconstruction to ascertain the following biomorphometric parameters: height, width, neck size, aspect ratio, bottle neck factor, aneurysm angle, deflection angle, neck angle, and proximal internal carotid artery- distal internal carotid artery angle. Significant factors related to rupture were determined and a forward stepwise binary logistic regression was performed to establish the log-odds of rupture. Results: A total of 101 aneurysms (80 ruptured and 21 unruptured) were included. Of the six statistically significant biomorphometric parameters measured, aneurysm deflection angle and aspect ratio both were considerably larger (p=0.001) in ruptured cases compared to unruptured ones. Binary logistic regression applied to the dataset demonstrated a 96% sensitivity and 89% overall accuracy. Conclusions: This updated binary logistic regression model was able to identify aneurysm rupture more robustly when compared to previous models. Future studies combining patient specific characteristics, along with previously determined biomorphometric parameters may further enhance this model. Disclosures: N. Damodara: None. K. Thomas: None. J. Quinn: None. C. Gandhi: None. C. Prestigiacomo: None.