Background And Purpose: 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.

Materials And Methods: 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 using all parameters and RIM excluding hemodynamics.

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

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

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6871507PMC
http://dx.doi.org/10.3174/ajnr.A6259DOI Listing

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