Predicting aneurysm rupture probabilities through the application of a computed tomography angiography–derived binary logistic regression model

Clinical article

Charles J. Prestigiacomo Departments of Neurological Surgery and
Radiology, and
Neurological Institute of New Jersey, New Jersey Medical School, University of Medicine of Dentistry of New Jersey, Newark, New Jersey

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 M.D.
,
Wenzhuan He Departments of Neurological Surgery and

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 M.D.
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Jeffrey Catrambone Departments of Neurological Surgery and

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 M.D.
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Stephanie Chung Departments of Neurological Surgery and

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 B.S.
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Lydia Kasper Departments of Neurological Surgery and

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 B.A.
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Latha Pasupuleti Departments of Neurological Surgery and

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 B.S.
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Neelesh Mittal Departments of Neurological Surgery and

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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.

Abbreviations used in this paper:

ACoA = anterior communicating artery; BA = basilar artery; ICA = internal carotid artery; MCA = middle cerebral artery; PCoA = posterior communicating artery; SAH = subarachnoid hemorrhage.
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