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

Abstract

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: RIMC using all parameters and RIMM excluding hemodynamics. The 25 study parameters had vastly different positive predictive values (31%-87%) for identifying rupture, the highest being size ratio at 87%. RIMC incorporated size ratio, undulation index, relative residence time, and type; RIMM had only size ratio, undulation index, and type. During cross-validation, positive predictive values for size ratio, RIMM, and RIMC were 86% ± 4%, 90% ± 4%, and 93% ± 4%, respectively. In testing, size ratio and RIMM had positive predictive values of 85%, while RIMC had 92%. Size ratio was the best individual factor for identifying the ruptured aneurysm; however, RIMC, followed by RIMM, outperformed existing parameters.