急性缺血性卒中静脉rt-PA溶栓后症状性颅内出血临床研究

发布时间：2018-05-10 05:18

本文选题：急性缺血性卒中 + 静脉rt-PA溶栓　；参考：《暨南大学》2015年博士论文

【摘要】：目的:探讨SEDAN预测模型、SITS-SICH预测模型、GRASPS预测模型、MSS预测模型与SPAN-100预测模型在预测急性缺血性卒中患者接受rt-PA溶栓治疗后症状性颅内出血(symptomatic intracerebral hemorrhage,SICH)中的效果。方法:为对多中心、前瞻性研究的回顾性再分析,对2007年5月至2012年4月全国范围内参加中国急性缺血性卒中溶栓安全监测研究(Thrombolysis Implementation and Monitor of acute ischemic Stroke in China,TIMS-China)的67个卒中中心的811例急性缺血性卒中4.5h时间窗内的静脉rt-PA标准剂量溶栓患者同时采用SEDAN预测模型、SITS-SICH预测模型、GRASPS预测模型、MSS预测模型与SPAN-100预测模型量表进行评定,比较5种评分方法在NINDS标准、SITS-MOST标准和ECASS-II标准的不同定义下对急性缺血性卒中患者静脉rt-PA溶栓后SICH的预测能力。结果:连续入选811例符合入选标准的急性缺血性卒中静脉rt-PA溶栓患者,预测溶栓后SICH时NINDS定义标准下SEDAN预测模型、SITS-SICH预测模型、GRASPS预测模型、MSS预测模型与SPAN-100预测模型的ROC曲线下面积分别为0.59(95%CI 0.51-0.67)、0.65(95%CI 0.56-0.75)、0.70(95%CI 0.60-0.79)、0.71(95%CI0.62-0.80)、0.51(95%CI 0.50-0.51);SITS-MOST标准下上述评分ROC曲线下面积分别为0.59(95%CI 0.49-0.69)、0.69(95%CI 0.55-0.84)、0.73(95%CI 0.61-0.84)、0.72(95%CI 0.60-0.84)、0.51(95%CI 0.50-0.51);ECASS-II标准下上述评分ROC曲线下面积分别为0.62(95%CI 0.53-0.71)、0.72(95%CI 0.62-0.83)、0.70(95%CI0.59-0.80)、0.73(95%CI 0.63-0.83)、0.51(95%CI 0.50-0.51)。按照NINDS、SITS-MOST和ECASS-II定义标准的SICH发生率分别为4.56%、1.73%和3.08%。结论:SITS-SICH预测模型、GRASPS预测模型、MSS预测模型均可预测中国急性缺血性卒中人群静脉rt-PA溶栓后SICH风险,但仅GRASPS预测模型和MSS预测模型有较理想的预测价值,且在NINDS及ECASS-II定义下MSS预测模型预测效果最好,而在SITS-MOST定义下GRASPS预测模型预测效果最好。
[Abstract]:Objective: to investigate the effect of SEDAN prediction model, SITS-SICH prediction model, GrASPS prediction model and SPAN-100 prediction model in predicting symptomatic intracranial hemorrhage after rt-PA thrombolytic therapy in patients with acute ischemic stroke. Methods: a retrospective reanalysis of multicenter, prospective studies was performed. A nationwide monitoring study on thrombolytic safety in acute ischemic stroke in China from May 2007 to April 2012 was conducted in 811 patients with acute ischemic stroke within 4.5 h time window in 87 stroke centers of Thrombolysis Implementation and Monitor of acute ischemic Stroke in China TIMS-China. The standard dose thrombolytic patients were evaluated with the SEDAN prediction model and the GrasPS prediction model and the SPAN-100 predictive model scale. To compare the predictive ability of five scoring methods for SICH after intravenous rt-PA thrombolysis in patients with acute ischemic stroke under different definitions of NINDS standard SITS-MOST and ECASS-II standard. Results: eight hundred and eleven consecutive patients with acute ischemic stroke received intravenous rt-PA thrombolysis. Prediction of SICH after thrombolysis the area under the ROC curve of SITS-SICH prediction model and SPAN-100 prediction model are 0.59(95%CI 0.51-0.67 CI 0.56-0.6595 CI 0.700-0.79 CI 0.60 0.79 CI 0.60 0.79 CI 0.70195CI0.62-0.80% 0.51C 0.50-0.51m area under SITS-MOST standard, respectively. 0.59(95%CI 0.49-0.6995 CI 0.55-0.84 / 0.7395; CI 0.61-0.84 / 0.72 / 95CI 0.60-0.84 / 0.51-0.51-95CI 0.50-0.51C = 0.62(95%CI 0.53-0.71-0.71CI-0.62-0.83CI = 0.7095CI0.59-0.800.7395CI = 0.7395 CI = 0.7395 CI = 0.7395 CI = 0.7395 CI = 0.7395 CI = 0.7395 CI = 0.7395 CI = 0.7395 CI = 0.7395 CI = 0.7395 CI = 0.7395 CI = 0.595 CI = 0.50-0.51c. The incidence of SICH according to NINDS SITS-MOST and ECASS-II was 4.56% and 3.08%, respectively. Conclusion the SICH risk after intravenous rt-PA thrombolysis in Chinese acute ischemic stroke population can be predicted by GRASS-SICH prediction model and GRASPS model, but only GRASPS prediction model and MSS prediction model are of good value in predicting SICH risk after thrombolytic therapy in Chinese patients with acute ischemic stroke. The prediction effect of MSS model is the best under the definition of NINDS and ECASS-II, while that of GRASPS model is the best under the definition of SITS-MOST.
【学位授予单位】：暨南大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：R743.3

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