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Associations between morphological parameters and ruptured anterior communicating artery aneurysm: A propensity score-matched, single center, case-control study

Interv Neuroradiol. 2022 Jun 19:15910199221108308. doi: 10.1177/15910199221108308. Online ahead of print.


BACKGROUND: To identify an association between morphological parameters and the rupture risk of anterior communicating artery (ACoA) aneurysms using propensity score matching (PSM).

METHODS: Data for 109 patients with ACoA aneurysms treated from January 2018 to October 2021 were reviewed; 94 patients were enrolled. The geometrical parameters of the ACoA aneurysms were measured and calculated using three-dimensional reconstructed digital subtraction angiography images. The aneurysms’ morphological parameters were analyzed using a propensity score for six factors (age, sex, excess alcohol intake, smoking, hypertension, diabetes mellitus). Univariate logistic regression was used to analyze the relationship between the aneurysms’ morphological parameters and rupture risk.

RESULTS: Twenty-five patients each with or without ruptured aneurysms were selected. After matching, no statistically significant differences were seen between the groups in their baseline characteristics. Aneurysm neck size (p = 0.038) was higher in the unruptured group than that in the ruptured group, and the dome-to-neck ratio (D/N; p = 0.009) and aspect ratio (AR; p = 0.003) were higher in the ruptured group than those in the unruptured group. Univariable logistic regression analysis demonstrated that ACoA aneurysm rupture was associated with AR (odds ratio: 8.047; 95% confidence interval: 1.569-41.213; p = 0.012) and D/N (odds ratio: 4.253; 95% confidence interval: 1.228-14.731; p = 0.022). The areas under the receiver operating characteristic curves for AR and D/N were 0.746 and 0.715, respectively.

CONCLUSIONS: After PSM, ACoA aneurysms with higher AR and D/N, and smaller neck size were more likely to rupture. AR may be a much more important predictor of aneurysm rupture than other predictors.

PMID:35722707 | DOI:10.1177/15910199221108308

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