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Updated: Aug 14, 2026
A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
Published on: July 28, 2018
A quantitative model to differentiate nonaneurysmal perimesencephalic subarachnoid hemorrhage from aneurysmal
Daniel Mandel1, Scott Moody1,2, Kelly Pan3
11Department of Neurology, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island.
Objective:
Nonaneurysmal perimesencephalic subarachnoid hemorrhage (pmSAH) is considered to have a lower-risk pattern than other types of subarachnoid hemorrhage (SAH). However, a minority of patients with pmSAH may harbor a causative posterior circulation aneurysm. To exclude this possibility, many institutions pursue exhaustive imaging. In this study the authors aimed to develop a novel predictive model based on initial noncontrast head CT (NCHCT) features to differentiate pmSAH from aneurysmal causes.
Methods:
The authors retrospectively reviewed patients admitted to an academic center for treatment of a suspected aneurysmal SAH (aSAH) during the period from 2016 to 2021. Patients with a final diagnosis of pmSAH or posterior circulation aSAH were included. Using NCHCT, the thickness (continuous variable) and location of blood in basal cisterns and sylvian fissures (categorical variables) were compared between groups. A scoring system was created using features that were significantly different between groups. Receiver operating characteristic curve analysis was used to measure the accuracy of this model in predicting aneurysmal etiology. A separate patient cohort was used for external validation of this model.
Results:
Of 420 SAH cases, 48 patients with pmSAH and 37 with posterior circulation aSAH were identified. Blood thickness measurements in the crural and ambient cisterns and interhemispheric and sylvian fissures and degree of extension into the sylvian fissure were all significantly different between groups (all p < 0.001). The authors developed a 10-point scoring model to predict aneurysmal causes with high accuracy (area under the curve [AUC] 0.99; 95% CI 0.98-1.00; OR per point increase 10; 95% CI 2.18-46.4). External validation resulted in persistently high accuracy (AUC 0.97; 95% CI 0.92-1.00) of this model.
Conclusions:
A risk stratification score using initial blood clot burden may accurately differentiate between aneurysmal and nonaneurysmal pmSAH. Larger prospective studies are encouraged to further validate this quantitative tool.
