Predicting Aneurysm Rupture Probabilities through the Application of a Computed Tomography Angiography-Derived Binary Logistic Regression Model.
OBJECT: The goal of this study was to establish a biomathematical model to accurately predict the probability of aneurysm rupture. Biomathematical models incorporate various physical and dynamic phenomena that provide insight into why certain aneurysms grow or rupture. Prior studies have demonstrated that regression models may determine which parameters of an aneurysm contribute to rupture. In this study, the authors derived a modified binary logistic regression model and then validated it in a distinct cohort of patients to assess the model's stability.
METHODS: Patients were examined with CT angiography. Three-dimensional reconstructions were generated and aneurysm height, width, and neck size were obtained in 2 orthogonal planes. Forward stepwise binary logistic regression was performed and then applied to a prospective cohort of 49 aneurysms in 37 patients (not included in the original derivation of the equation) to determine the log-odds of rupture for this aneurysm.
RESULTS: A total of 279 aneurysms (156 ruptured and 123 unruptured) were observed in 217 patients. Four of 6 linear dimensions and the aspect ratio were significantly larger (each with p < 0.01) in ruptured aneurysms than unruptured aneurysms. Calculated volume and aneurysm location were correlated with rupture risk. Binary logistic regression applied to an independent prospective cohort demonstrated the model's stability, showing 83% sensitivity and 80% accuracy.
CONCLUSIONS: This binary logistic regression model of aneurysm rupture identified the status of an aneurysm with good accuracy. The use of this technique and its validation suggests that biomorphometric data and their relationships may be valuable in determining the status of an aneurysm.
Published In/Presented At
PRESTIGIACOMO, CJ; et al. Predicting aneurysm rupture probabilities through the application of a computed tomography angiography-derived binary logistic regression model. Journal Of Neurosurgery. United States, 110, 1, 1-6, Jan. 2009. ISSN: 0022-3085.
Medical Sciences | Medicine and Health Sciences | Neurology
Department of Medicine