Mandel Daniel, Moody Scott, Pan Kelly, Subramaniam Thanujaa, Thompson Bradford B, Wendell Linda C, Reznik Michael E, Furie Karen L, Mahta Ali
1Department of Neurology, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island.
2Department of Physician Assistant Studies, Massachusetts General Hospital Institute of Health Professions, Boston, Massachusetts.
J Neurosurg. 2022 May 6;138(1):165-172. doi: 10.3171/2022.3.JNS22157. Print 2023 Jan 1.
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.
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.
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.
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.
非动脉瘤性中脑周围蛛网膜下腔出血（pmSAH）被认为比其他类型的蛛网膜下腔出血（SAH）具有更低的风险模式。然而，少数pmSAH患者可能存在导致出血的后循环动脉瘤。为排除这种可能性，许多机构进行详尽的影像学检查。在本研究中，作者旨在基于初始非增强头部CT（NCHCT）特征开发一种新型预测模型，以区分pmSAH和动脉瘤性病因。
作者回顾性分析了2016年至2021年期间因疑似动脉瘤性SAH（aSAH）入住学术中心接受治疗的患者。纳入最终诊断为pmSAH或后循环aSAH的患者。使用NCHCT，比较两组之间基底池和外侧裂中血液的厚度（连续变量）及位置（分类变量）。利用两组间有显著差异的特征创建一个评分系统。采用受试者操作特征曲线分析来衡量该模型预测动脉瘤性病因的准确性。使用另一独立患者队列对该模型进行外部验证。
在420例SAH病例中，确定了48例pmSAH患者和37例后循环aSAH患者。两组之间在脚间池和环池、半球间裂和外侧裂中的血液厚度测量值以及外侧裂内的扩展程度均有显著差异（均p<0.001）。作者开发了一个10分的评分模型，能够高精度地预测动脉瘤性病因（曲线下面积[AUC]0.99；95%CI 0.98 - 1.00；每增加一分的OR为10；95%CI 2.18 - 46.4）。外部验证结果显示该模型持续具有高准确性（AUC 0.97；95%CI 0.92 - 1.00）。
使用初始血凝块负荷的风险分层评分可准确区分动脉瘤性和非动脉瘤性pmSAH。鼓励开展更大规模的前瞻性研究以进一步验证这一定量工具。
