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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, the authors 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 MRI scans within 24 h of ictus with follow-up 2 months after discharge. Using logistic regression analysis (univariate and multivariate), the authors 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 (3-5). After a mean follow-up of 2 months, 121 individuals (68.36%) with a-SAH and three control subjects developed incident CI. The CT interobserver 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.
CONCLUSIONS: 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.
METHODS: In this study, 202 consecutive patients with spontaneous a-SAH underwent initial computed tomography (CT) or MRI scans within 24 h of ictus with follow-up 2 months after discharge. Using logistic regression analysis (univariate and multivariate), the authors 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 (3-5). After a mean follow-up of 2 months, 121 individuals (68.36%) with a-SAH and three control subjects developed incident CI. The CT interobserver 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.
CONCLUSIONS: 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.
摘要：
背景：动脉瘤性蛛网膜下腔出血（a-SAH）后脑水肿和血肿的早期评估和处理可显著影响临床认知结果。然而,目前的临床实践缺乏预测模型来识别影响认知的早期结构性脑异常。为了解决这个差距，我们建议开发一种称为a-SAH早期脑水肿/血肿压缩神经（结构性脑）网络评分系统（SEBE-HCNNSS）的预测模型。
方法：在本研究中，202例自发性a-SAH患者在出院后24小时内接受了初始计算机断层扫描（CT）或磁共振成像（MRI）扫描，并在出院后2个月进行了随访。使用逻辑回归分析(单变量和多变量)，我们评估了临床相关因素和各种传统量表评分与认知障碍(CI)的相关性.多变量分析和最小绝对收缩和选择算子（LASSO）分析或Cox回归分析包括曲线下面积（AUC）值最高的危险因素。
结果：总共177名患者被纳入研究，43例患者的SEBE-HCNNSS分级较高(3~5).平均随访2个月后，121例(68.36%)患有a-SAH的个体和3例对照受试者发生事件CI。SEBE-HCNNSS量表的CT观察者间信度较高，Kappa值为1。此外，ROC分析确定SEBE-HCNNSS量表（OR3.322，95％CI2.312-7.237，P=0.00025）作为水肿的独立预测因子，CI和不良预后。这些结果也在验证队列中重复。
结论：总体而言，SEBE-HCNNSS量表是一种简单的评估工具,对a-SAH后的CI和临床结局具有良好的预测价值.我们的发现表明其作为a-SAH后风险评估的预后工具的实用性，可能有助于早期干预和治疗。
方法：在本研究中，202例自发性a-SAH患者在出院后24小时内接受了初始计算机断层扫描（CT）或磁共振成像（MRI）扫描，并在出院后2个月进行了随访。使用逻辑回归分析(单变量和多变量)，我们评估了临床相关因素和各种传统量表评分与认知障碍(CI)的相关性.多变量分析和最小绝对收缩和选择算子（LASSO）分析或Cox回归分析包括曲线下面积（AUC）值最高的危险因素。
结果：总共177名患者被纳入研究，43例患者的SEBE-HCNNSS分级较高(3~5).平均随访2个月后，121例(68.36%)患有a-SAH的个体和3例对照受试者发生事件CI。SEBE-HCNNSS量表的CT观察者间信度较高，Kappa值为1。此外，ROC分析确定SEBE-HCNNSS量表（OR3.322，95％CI2.312-7.237，P=0.00025）作为水肿的独立预测因子，CI和不良预后。这些结果也在验证队列中重复。
结论：总体而言，SEBE-HCNNSS量表是一种简单的评估工具,对a-SAH后的CI和临床结局具有良好的预测价值.我们的发现表明其作为a-SAH后风险评估的预后工具的实用性，可能有助于早期干预和治疗。
