Objective: Determining factors predictive of the natural risk of rupture of cerebral aneurysms is difficult because of the need to control for confounding variables. We studied factors associated with rupture in a study model of patients with multiple cerebral aneurysms, one aneurysm that had ruptured and one or more that had not, in which each patient served as their own internal control.
Methods: We collected aneurysm location, one-dimensional measurements, and two-dimensional indices from the computed tomographic angiograms of patients in the proposed study model and compared ruptured versus unruptured aneurysms. Bivariate statistics were supplemented with multivariable logistic regression analysis to model ruptured status. A total of 40 candidate models were evaluated for predictive power and fit with Wald scoring, Cox and Snell R2, Hosmer and Lemeshow tests, case classification counting, and residual analysis to determine which of the computed tomographic angiographic measurements or indices were jointly associated with and predictive of aneurysm rupture.
Results: Thirty patients with 67 aneurysms (30 ruptured, 37 unruptured) were studied. Maximum diameter, height, maximum width, bulge height, parent artery diameter, aspect ratio, bottleneck factor, and aneurysm/parent artery ratio were significantly (P < 0.05) associated with ruptured aneurysms on bivariate analysis. When best subsets and stepwise multivariable logistic regression was performed, bottleneck factor (odds ratio = 1.25, confidence interval = 1.11-1.41 for every 0.1 increase) and height-width ratio (odds ratio = 1.23, confidence interval = 1.03-1.47 for every 0.1 increase) were the only measures that were significantly predictive of rupture.
Conclusion: In a case-control study of patients with multiple cerebral aneurysms, increased bottleneck factor and height-width ratio were consistently associated with rupture.