氯吡格雷
冲程(发动机)
阿司匹林
西洛他唑
外科
改良兰金量表
作者
Jang Hyun Baek,Cheolkyu Jung,Byung Moon Kim,Ji Hoe Heo,Dong Joon Kim,Hyo Suk Nam,Young Dae Kim,Eun Hyun Lim,Jun Hwee Kim,Jun Yup Kim,Jae Hyoung Kim
标识
DOI:10.3389/fneur.2021.608270
摘要
Background and Purpose: Intracranial atherosclerosis-related large-vessel occlusion caused by in situ thrombo-occlusion (ICAS-LVO) has been regarded an important reason for refractoriness to mechanical thrombectomy (MT). To achieve better outcomes for ICAS-LVO, different endovascular strategies should be explored. We aimed to investigate an optimal endovascular strategy for ICAS-LVO. Methods: We retrospectively reviewed three prospective registries of acute stroke underwent endovascular treatment. Among them, patients with ICAS-LVO were assigned to four groups based on their endovascular strategy: (1) MT alone, (2) rescue intracranial stenting after MT failure (MT-RS), (3) glycoprotein IIb/IIIa inhibitor infusion after MT failure (MT-GPI), and (4) a combination of MT-RS and MT-GPI (MT-RS+GPI). Baseline characteristics and outcomes were compared among the groups. To evaluate whether the endovascular strategy resulted in favorable outcome, multivariable analysis was also performed. Results: A total of 184 patients with ICAS-LVO were included. Twenty-four patients (13.0%) were treated with MT alone, 25 (13.6%) with MT-RS, 84 (45.7%) with MT-GPI, and 51 (27.7%) with MT-RS+GPI. The MT-RS+GPI group showed the highest recanalization efficiency (98.0%). Frequency of patent arteries on follow-up (98.0%, p < 0.001) and favorable outcome (84.3%, p < 0.001) were higher in the MT-RS+GPI group than other groups. The MT-RS+GPI strategy remained an independent factor for favorable outcome (odds ratio, 20.4; 95% confidence interval, 1.97-211.4; p = 0.012). Conclusion: Endovascular strategy was significantly associated with procedural and clinical outcomes in acute stroke by ICAS-LVO. A combination of RS and GPI infusion might be an optimal rescue modality when frontline MT fails.
科研通智能强力驱动
Strongly Powered by AbleSci AI