ABSTRACT
Objective:To analyze the clinical effect of TREVO stent thrombectomy combined with tirofiban on patients with acute large-artery occlusion of the anterior circulation.Methods:Seventy-two patients with acute large-artery occlusion of the anterior circulation accepted thrombectomy in our hospital from November 2016 to May 2020 were divided into two groups according to different treatment methods: 35 patients in the control group were treated with TREVO stent thrombectomy, and 37 patients in the treatment group were treated with TREVO stent thrombectomy combined with tirofiban via intra-variceal injection. The success rate of recanalization, specific conditions of thrombolysis, improvement degrees of nerve defect, coagulation function, prognoses 90 d after thrombectomy, and complications were compared between the two groups.Results:The success rate of postoperative vascular recanalization in the treatment group and control group was 91.89% (34/37) and 88.57% (31/35), respectively, without statistically significant difference ( P>0.05). The time and times of thrombotomy in the treatment group were significantly shorter/smaller than those in the control group (P<0.05). The National Institute of Health stroke scale (NIHSS) scores of patients from the treatment group 14 d after thrombectomy were significantly lower than those of the control group ( P<0.05). The postoperative thrombin time, prothrombin time, and activated partial thrombin time of the treatment group were significantly longer than those of the control group ( P<0.05). The good prognosis rate of patients in the treatment group and control group 90 d after thrombectomy was 86.49% (32/37) and 60.0% (21/35), with significant differences ( P<0.05); and the incidence of complications was 8.11% (3/37) and 14.29% (5/35), without significant differences ( P>0.05). Conclusion:TREVO stent thrombectomy combined with tirofiban has a significant effect on treatment of acute large-artery occlusion of the anterior circulation, enjoying high safety.
ABSTRACT
Background and Purpose- The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinical outcome of LVO before endovascular treatment and to compare our method with previously developed pretreatment scoring methods. Methods- The derivation cohort included 387 LVO patients, and the external validation cohort included 115 LVO patients with anterior circulation who were treated with mechanical thrombectomy. The statistical model with logistic regression without regularization and machine learning algorithms, such as regularized logistic regression, linear support vector machine, and random forest, were used to predict good clinical outcome (modified Rankin Scale score of 0-2 at 90 days) with standard and multiple pretreatment clinical variables. Five previously reported pretreatment scoring methods (the Pittsburgh Response to Endovascular Therapy score, the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale index, the Totaled Health Risks in Vascular Events score, the Houston Intra-Arterial Therapy score, and the Houston Intra-Arterial Therapy 2 score) were compared with these models for the area under the receiver operating characteristic curve. Results- The area under the receiver operating characteristic curve of random forest, which was the worst among the machine learning algorithms, was significantly higher than those of the standard statistical model and the best model among the previously reported pretreatment scoring methods in the derivation (the area under the receiver operating characteristic curve were 0.85±0.07 for random forest, 0.78±0.08 for logistic regression without regularization, and 0.77±0.09 for Stroke Prognostication using Age and National Institutes of Health Stroke Scale) and validation cohorts (the area under the receiver operating characteristic curve were 0.87±0.01 for random forest, 0.56±0.07 for logistic regression without regularization, and 0.83±0.00 for Pittsburgh Response to Endovascular Therapy). Conclusions- Machine learning methods with multiple pretreatment clinical variables can predict clinical outcomes of patients with anterior circulation LVO who undergo mechanical thrombectomy more accurately than previously developed pretreatment scoring methods.