Novel Models for Identification of the Ruptured Aneurysm in Patients with Subarachnoid Hemorrhage with Multiple...

In patients with SAH with multiple intracranial aneurysms, often the hemorrhage pattern does not indicate the rupture source. Angiographic findings (intracranial aneurysm size and shape) could help but may not be reliable. Our purpose was to test whether existing parameters could identify the ruptured intracranial aneurysm in patients with multiple intracranial aneurysms and whether composite predictive models could improve the identification.

We retrospectively collected angiographic and medical records of 93 patients with SAH with at least 2 intracranial aneurysms (total of 206 saccular intracranial aneurysms, 93 ruptured), in which the ruptured intracranial aneurysm was confirmed through surgery or definitive hemorrhage patterns. We calculated 13 morphologic and 10 hemodynamic parameters along with location and type (sidewall/bifurcation) and tested their ability to identify rupture in the 93 patients. To build predictive models, we randomly assigned 70 patients to training and 23 to holdout testing cohorts. Using a linear regression model with a customized cost function and 10-fold cross-validation, we trained 2 rupture identification models: RIM_C using all parameters and RIM_M excluding hemodynamics.

The 25 study parameters had vastly different positive predictive values (31%-87%) for identifying rupture, the highest being size ratio at 87%. RIM_C incorporated size ratio, undulation index, relative residence time, and type; RIM_M had only size ratio, undulation index, and type. During cross-validation, positive predictive values for size ratio, RIM_M, and RIM_C were 86% ± 4%, 90% ± 4%, and 93% ± 4%, respectively. In testing, size ratio and RIM_M had positive predictive values of 85%, while RIM_C had 92%.

Size ratio was the best individual factor for identifying the ruptured aneurysm; however, RIM_C, followed by RIM_M, outperformed existing parameters.