On the Use of Geometric Modeling to Predict Aortic Aneurysm Rupture.

BACKGROUND

Currently, the risk of abdominal aortic aneurysm (AAA) rupture is determined using the maximum diameter (D) 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.

METHODS

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.

RESULTS

Linear regression identified 40 correlates of rupture. Following stepwise backward elimination, we developed a multivariate logistic regression model containing 15 geometric parameters, including D. This model was compared with a model containing D alone. The multivariate model correctly classified 98% of all cases, whereas the D-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-only model. This difference was highly significant (P < 0.0001).

CONCLUSIONS

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.