The role of early cerebral edema and hematoma assessment in aneurysmal subarachnoid hemorrhage (a-SAH) in predicting structural brain abnormalities in cognitive impairments-- case controlled study

Author:

Wang Ming-Dong12ORCID,Fu Qian-Hui3,Ni Andrew4,Yuan Yun-Peng5,Li Chun-Hui12,Wang Zhan-Xiang6,Wang Hong7

Affiliation:

1. Department of Neurosurgery. The First Hospital of Hebei Medical University, No. 89 Donggang Road, Shijiazhuang, 050000, Hebei, China

2. Department of Neurosurgery, Hebei Hospital of Xuanwu Hospital Capital Medical University, No. 89 Donggang Road, Shijiazhuang, 050000, Hebei, China

3. College of Pharmacy,MINZU university of China, Key Laboratory of Ministry of Education, Ministry of Education (MINZU university of China), Beijing, China, 100081

4. Warren Alpert Medical School, Brown University, 222 Richmond St, Providence, RI, 02903, USA

5. Department of Neurosurgery, Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, No. 181 Youyi Road Baoshan District, 201999, Shanghai, China

6. Department of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, 361003, China

7. Department of Neurosurgery, The Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, China

Abstract

Background: Early assessment and management of cerebral edema and hematoma following aneurysmal subarachnoid hemorrhage (a-SAH) can significantly impact clinical cognitive outcomes. However, current clinical practices lack predictive models to identify early structural brain abnormalities affecting cognition. To address this gap, we propose the development of a predictive model termed the a-SAH Early Brain Edema/Hematoma Compression Neural (Structural Brain) Networks Score System (SEBE-HCNNSS). Methods: In this study, 202 consecutive patients with spontaneous a-SAH underwent initial computed tomography (CT) or magnetic resonance imaging (MRI) scans within 24 hours of ictus with follow-up 2 months after discharge. Using logistic regression analysis (univariate and multivariate), we evaluated the association of clinically relevant factors and various traditional scale ratings with cognitive impairment (CI). Risk factors with the highest area under the curve (AUC) values were included in the multivariate analysis and least absolute shrinkage and selection operator (LASSO) analysis or Cox regression analysis. Results: A total of 177 patients were enrolled in the study, and 43 patients were classified with a high SEBE-HCNNSS grade (3 to 5). After a mean follow-up of 2 months, 121 individuals (68.36%) with a-SAH and 3 control subjects developed incident CI. The CT inter-observer reliability of the SEBE-HCNNSS scale was high, with a Kappa value of 1. Furthermore, ROC analysis identified the SEBE-HCNNSS scale (OR 3.322, 95% CI 2.312-7.237, P=0.00025) as an independent predictor of edema, CI, and unfavorable prognosis. These results were also replicated in a validation cohort. Conclusion: Overall, the SEBE-HCNNSS scale represents a simple assessment tool with promising predictive value for CI and clinical outcomes post-a-SAH. Our findings indicate its practical utility as a prognostic instrument for risk evaluation after a-SAH, potentially facilitating early intervention and treatment.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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