Department of Intervention Radiology,The First Affiliated Hospital of Nanjing Medical University
目的: 分析超时间窗急性缺血性脑卒中（Acute ischemic stroke，AIS）患者动脉取栓后颅内出血的危险因素，建立预警模型以指导临床。 方法: 回顾性分析2018年1月至2020年6月在我院接受动脉取栓治疗的146例超时间窗AIS患者的临床资料。依据是否发生颅内出血转化分为出血组和非出血组，比较两组患者的人口学、临床和影像资料。采用Logistic回归分析动脉取栓后颅内出血的危险因素并建立预警模型。采用受试者工作特性（ROC）曲线评估预警模型对动脉取栓后颅内出血的预测效能。 结果: 48例（32.9%）出现颅内出血。与非出血组比较，出血组基线美国国立卫生研究院脑卒中量表（NIHSS）评分更高（p<0.001）、支架取栓次数更多（p=0.049）、基线Alberta卒中项目早期CT（ASPECT）评分更低（p<0.001）。Logistic回归显示，高NIHSS评分（p =0.001）和低ASPECT评分（p<0.001）是出血转化的危险因素。预警模型为：颅内出血风险值= -0.535 + 0.130 NIHSS评分 - 0.597 ASPECT评分。模型预测取栓后颅内出血的ROC曲线下面积、敏感性和特异性分别为0.875、0.854和0.837。 结论: 高基线NIHSS评分和低基线ASPECT评分是超时间窗AIS患者动脉取栓后发生颅内出血的危险因素。预警模型可为超时间窗AIS患者动脉取栓后的临床观察和出血转化防治提供依据。
Objective: To analyze the risk factors of intracranial hemorrhage (IH) in patients with acute ischemic stroke (AIS) treated with intra-arterial thrombectomy (IAT) in extended time window, and to establish the predictive model for guiding clinical decision. Materials and methods: From January 2018 and June 2020, clinical data of 146 AIS patients treated with IAT in extended time window were retrospectively analyzed. Patients were divided into hemorrhage and no-hemorrhage group. Demographic, clinical and imaging data were compared between two groups. Logistic regression (LR) analysis was applied to clarify the risk factor of IH after IAT, and to establish the predictive model. Receiver operator characteristic curve (ROC) analysis was used to evaluate the performance of the model for predicting IH after IAT. Results: Intracranial hemorrhage occurred in 48 (32.9%) patients. Compared with no-hemorrhage group, hemorrhage group showed higher baseline National Institute of Health Stroke Scale (NIHSS) (p<0.001), higher numbers of stent thrombectomy (p=0.049) and lower baseline alberta stroke program early computed tomography score (ASPECTS) (p<0.001). LR analysis indicated that, high NIHSS (p=0.001) and low ASPECTS (p<0.001) were risk factors of IH. The predictive model was as follows: hemorrhage risk value = -0.535 + 0.130 baseline NIHSS - 0.597 ASPECTS. The predictive model showed an area under the ROC curve, sensitivity and specificity of 0.875, 0.854 and 0.837, respectively. Conclusions: In the AIS patients in extended time window, the patients with high baseline NIHSS and low ASPECTS were prone to IH after IAT. The predictive model can provide reference for prevention of IH and clinical observation.