On the Use of Geometric Modeling to Predict Aortic Aneurysm Rupture

Currently, the risk of abdominal aortic aneurysm (AAA) rupture is determined using the maximum diameter (D_max) of the aorta. We sought in this study to identify a set of computed tomography (CT)-based geometric parameters that would better predict the risk of rupture than D_max.

We obtained CT scans from 180 patients (90 ruptured AAA and 90 elective AAA repair) and then used automated software to calculate 1- , 2- , and 3-dimensional geometric parameters for each AAA. Linear regression was used to identify univariate correlates of membership in the rupture group. We then used stepwise backward elimination to generate a logistic regression model for prediction of rupture.

Linear regression identified 40 correlates of rupture. Following stepwise backward elimination, we developed a multivariate logistic regression model containing 15 geometric parameters, including D_max. This model was compared with a model containing D_max alone. The multivariate model correctly classified 98% of all cases, whereas the D_max-only model correctly classified 72% of cases. Receiver operating characteristic analysis showed that the multivariate model had an area under the curve of 0.995, as compared with 0.770 for the D_max-only model. This difference was highly significant (P < 0.0001).

This study demonstrates that a multivariable model using geometric factors entirely measurable from CT scanning can be a better predictor of AAA rupture than maximum diameter alone.