The Establishment And Assessment Of A Model For Predicting Rupture Risk Of Multiple Intracranial Anterior Circulation Aneurysm

Objective To investigate the risk factors related to the rupture of multiple intracranial anterior circulation aneurysm (AC-MIA), and to establish and assess a model for predicting the rupture probability of AC-MIA.Methods A cohort of patients with AC-MIA underwent three dimensional-computer tomography angiography (3D-CTA) to measure several morphological parameters of the aneurysm such as size, width, neck width, bottle factor (BNF), height/width ratio (H/W), aspect ratio (AR) and size ratio (SR).All the morphological parameters combined with the aneurysm location, aneurysm type, bleb formation and the patient’s baseline data were conducted univariate analysis. Then the significant factors were used to derive a backward binary logistic regression model. The model was tested by another prospective cohort.Results Univariate analysis suggested that age (P=0.017), aneurysm location (P=0.019), aneurysm type (P=0.015) and bleb formation (P=0.000) were correlated with rupture risk. Size, width, BNF, AR and SR were significantly larger (each with P<0.05) in ruptured aneurysms than unruptured aneurysms. Binary logistic regression applied to an independent prospective cohort demonstrated the model’s stability, showing 91% sensitivity,89% specificity and 90% accuracy.Conclusions AR, SR, bleb formation were correlated with the rupture of AC-MIA. This binary logistic regression model identified the status of an aneurysm with good accuracy. This is the first model to predict the rupture of AC-MIA.