Objective: To explore the value of predicting shunt-dependent hydrocephalus (SDHC) in patients with aneurysmal subarachnoid hemorrhage (aSAH) based on whole brain CT perfusion(CTP) and clinical data within 24 hours at admission. Methods: The clinical and imaging data of aSAH patients who received interventional embolization in our hospital were retrospectively collected from March 2018 to August 2022. All patients underwent one-stop whole brain CT examination within 24 hours after symptom onset, and the qualitative and quantitative CTP parameters were obtained after post-processing. Follow-up was conducted once every 2 months by consulting electronic medical records or by telephone for 6 months. According to whether SDHC occurred or not, the patients were divided into SDHC group and non-SDHC group. The differences between the two groups were compared. Logistic regression model was used to analyze and determine the predictive factors of SDHC, and the SDHC predictive model was established. The effectiveness of the predictive model was evaluated by drawing the receiver operating characteristic (ROC) curve of the subjects. Results: A total of 414 patients were included, including 132 males and 282 females, aged (59±11) years. 17.6%(73/414) patients had SDHC. There were significant differences in the occurrence of acute hydrocephalus, the World Neurosurgical League Scale (WFNS), the Hunt-Hess scale, the modified Fisher score (mFS), and the qualitative and quantitative parameters of CTP between the two groups (both P<0.001). Multivariate logistic regression analysis showed that acute hydrocephalus (OR=8.621, 95%CI: 4.237-17.542),old age (OR=1.107, 95%CI: 1.068-1.148), high mFS and high Hunt-Hess classification (OR=3.740, 95%CI: 1.352-10.342) were the risk factors of SDHC in aSAH patients, and high mean cerebral blood flow (mCBF) (OR=0.931, 95%CI: 0.885-0.980) was a protective factor of SDHC.The area under ROC curve (AUC) of the prediction model constructed by these five variables was 0.923(95%CI: 0.89-0.95), with 84.5% sensitivity and 87.7% specificity. Conclusion: The mCBF and acute hydrocephalus, age, mFS and Hunt-Hess classification within 24 hours at admission can be used to predict SDHC for aSAH patients.
目的： 探讨基于入院24 h内全脑CT灌注（CTP）及临床数据预测动脉瘤性蛛网膜下腔出血（aSAH）患者发生分流依赖性脑积水（SDHC）的价值。 方法： 回顾性收集2018年3月至2022年8月在皖南医学院第一附属医院接受介入栓塞治疗的aSAH患者入院时的临床和影像学数据。所有患者均在症状发生后24 h内完成一站式全脑CTP检查，并经后处理得到CTP的定性和定量参数。通过查阅电子病历或进行电话随访，每2个月随访1次，共随访6个月。根据是否发生SDHC，将患者分为SDHC组及非SDHC组，比较两组间的差异，采用logistic回归模型分析确定SDHC发生的影响因素，建立SDHC预测模型，通过绘制受试者工作特征（ROC）曲线评估预测模型效能。 结果： 共纳入414例患者，其中男132例，女282例，年龄为（59±11）岁。17.6%（73/414）患者发生SDHC。是否发生急性脑积水、世界神经外科联盟分级（WFNS）、Hunt-Hess分级、改良Fisher评分（mFS）、CTP定性及定量参数在两组间的差异有统计学意义（均P<0.001）。多因素logisitc回归分析显示急性脑积水（OR=8.621，95%CI：4.237~17.542）、高龄（OR=1.107，95%CI：1.068~1.148）、高mFS及高Hunt-Hess分级（OR=3.740，95%CI：1.352~10.342）是aSAH患者发生SDHC的危险因素，高平均脑血流量（mCBF）（OR=0.931，95%CI：0.885~0.980）是发生SDHC的保护因素，由这5个变量构建的预测模型的ROC曲线下面积（AUC）=0.923（95%CI：0.89~0.95），其灵敏度为84.5%、特异度为87.7%。 结论： aSAH患者入院24 h内的mCBF和急性脑积水、年龄、mFS、Hunt-Hess分级可用于预测其SDHC的发生。.