血栓
医学
改良兰金量表
放射科
闭塞
冲程(发动机)
纸牌密码算法
内科学
心脏病学
缺血性中风
缺血
机械工程
工程类
作者
Harikrishnan Ramachandran,Sachin Girdhar,Sapna Erat Sreedharan,Jayadevan Enakshy Rajan,Santhosh Kumar Kannath,Jissa Vinoda Thulaseedharan,Sajith Sukumaran,PN Sylaja
标识
DOI:10.1016/j.jstrokecerebrovasdis.2022.106621
摘要
Background Identification of computed tomography (CT) thrombus imaging characteristics can predict the degree of recanalization and outcome after endovascular thrombectomy (EVT) in patients with acute ischaemic stroke and large vessel occlusion. Aim We analyzed the thrombus imaging characteristics and procedural factors and correlated with the degree of recanalization and functional outcome after EVT. Methods We evaluated the thrombus imaging characteristics (hyperdense MCA sign, thrombus location, length and thrombus permeability) from thin slice CT and CT angiogram. In addition, groin to recanalization time, number of passes, and EVT technique were documented. The primary outcome was degree of recanalization (mTICI score) and secondary outcome was modified Rankin scale (mRS) at 3 months. Results The mean age of 102 patients was 60.5±11.8 years. Patients with hyperdense MCA sign (90 % vs 75%, p=0.07) and permeable thrombus (86 % vs 70 %, p=0.09) had good recanalization (mTICI grade 2b,2c or 3). The requirement of <3 passes (90 % vs 62 %, p= 0.001) was associated with good recanalization. Multiple logistic regression analysis showed thrombus permeability (OR 5.9; 95% CI 1.3-26.6, p=0.02), use of stent retreiver alone (without aspiration) (OR 5.4; 95% CI 1.3-22.5, p=0.02) and a puncture to recanalization ≤60 minutes (OR 7.9; 95% CI 1.7-36.8; p=0.008) were associated with good recanalization. The requirement of ≥3 passes was associated with poor functional outcome (OR 3.4;95% CI 1.2-9.8; p=0.02). Conclusions Thrombus permeability was a predictor of successful recanalization after EVT. The requirement of three or more passes during EVT was associated with poor recanalization and poor functional outcome.
科研通智能强力驱动
Strongly Powered by AbleSci AI