李亮, 张继伟, 孙文浩, 王广, 田甜, 于淼. 急性脑卒中机械取栓术后病人症状性脑出血预测模型构建分析[J]. 蚌埠医科大学学报, 2023, 48(6): 779-782. DOI: 10.13898/j.cnki.issn.1000-2200.2023.06.016
    引用本文: 李亮, 张继伟, 孙文浩, 王广, 田甜, 于淼. 急性脑卒中机械取栓术后病人症状性脑出血预测模型构建分析[J]. 蚌埠医科大学学报, 2023, 48(6): 779-782. DOI: 10.13898/j.cnki.issn.1000-2200.2023.06.016
    LI Liang, ZHANG Ji-wei, SUN Wen-hao, WANG Guang, TIAN Tian, YU Miao. Construction and analysis of predictive model of symptomatic intracerebral hemorrhage in patients with acute stroke after mechanical thrombectomy[J]. Journal of Bengbu Medical University, 2023, 48(6): 779-782. DOI: 10.13898/j.cnki.issn.1000-2200.2023.06.016
    Citation: LI Liang, ZHANG Ji-wei, SUN Wen-hao, WANG Guang, TIAN Tian, YU Miao. Construction and analysis of predictive model of symptomatic intracerebral hemorrhage in patients with acute stroke after mechanical thrombectomy[J]. Journal of Bengbu Medical University, 2023, 48(6): 779-782. DOI: 10.13898/j.cnki.issn.1000-2200.2023.06.016

    急性脑卒中机械取栓术后病人症状性脑出血预测模型构建分析

    Construction and analysis of predictive model of symptomatic intracerebral hemorrhage in patients with acute stroke after mechanical thrombectomy

    • 摘要:
      目的构建急性脑卒中机械取栓术后症状性脑出血的预测模型,探讨该预测模型在预测急性脑卒中机械取栓术后发生症状性脑内出血的预测价值。
      方法纳入2017-2020年203例脑卒中病人为研究对象,术后受试者均接受急性脑卒中机械取栓术,并随访36 h。根据病人术后是否发生症状性脑出血分为出血组和未出血组,收集所有病人性别、年龄、基础疾病等临床资料;采用多因素logistics回归分析影响脑卒中机械取栓术后发生症状性脑出血的危险因素;通过逻辑回归方式构建预测模型,使用MedCalc软件绘制受试者特征性工作曲线分析(ROC),检验模型效能。
      结果截至末次随访共47例发生失访,最终纳入156例病人,急性脑卒中机械取栓术后共发生17例症状性脑内出血,发生率为10.90%,分为出血组(17例)和未出血组(139例)。2组病人年龄、空腹血糖、糖化血红蛋白、心房颤动、术前NIHSS评分、取栓次数组间比较差异均有统计学意义(P < 0.05~P < 0.01);多因素logistic回归分析显示,空腹血糖、糖化血红蛋白、心房颤动、NIHSS评分、取栓次数是导致急性脑卒中机械取栓术后发生症状性脑出血的独立危险因素(P < 0.01);ROC曲线分析显示,预测模型的曲线下面积为0.920(95%CI:0.915~0.925),截点值为0.40,敏感度为87.90 %,特异度为86.10%。
      结论空腹血糖、糖化血红蛋白、心房颤动、NIHSS评分、取栓次数均是影响急性脑卒中机械取栓术后发生症状性脑出血的因素,通过建立预测模型能够做到早期预警,指导临床制定干预手段。

       

      Abstract:
      ObjectiveTo construct a predictive model of symptomatic intracerebral hemorrhage after mechanical thrombectomy in acute stroke, and explore the predictive value of this predictive model in predicting the occurrence of symptomatic intracerebral hemorrhage after mechanical thrombectomy in acute stroke.
      MethodsA total of 203 stroke patients from January 2017 to December 2020 were included as the study subjects.All subjects received acute stroke mechanical thrombectomy and were followed up for 36 h.According to the occurrence of symptomatic intracerebral hemorrhage after the operation, the patients were divided into hemorrhage group and non-hemorrhage group.Clinical data were collected such as gender, age and disease history of all patients.Multivariate logistics regression analysis was used to analyze the risk factors of symptomatic intracerebral hemorrhage after mechanical thrombus removal after stroke, and a predictive model was constructed.Then the ROC curve was drawn using MedCalc to test the efficacy of the model.
      ResultsA total of 47 cases were lost after the last follow-up, and 156 patients were finally included.A total of 17cases of symptomatic intracerebral hemorrhage occurred after mechanical thrombectomy in acute stroke, with an incidence rate of 10.9%.Then they were divided into hemorrhage group (17 cases) and non-hemorrhage group (139 cases).Comparing the clinical data of the two groups of patients showed that the differences between the groups were statistically significant in terms of patient age, fasting blood glucose, HbA1c, atrial fibrillation, preoperative NIHSS score, and the number of thrombectomy times (P < 0.05 to P < 0.01). Multivariate logistic regression analysis showed that fasting blood glucose, HbA1c, atrial fibrillation, NIHSS score and the number of thrombectomy were independent risk factors for symptomatic intracerebral hemorrhage after mechanical thrombectomy in acute stroke (P < 0.01). ROC curve analysis showed that the area under the curve of the prediction model was 0.920 (95%CI: 0.915-0.925), the cutoff value was 0.40, the sensitivity was 87.90%, and the specificity was 86.10%.
      ConclusionsFasting blood glucose, HbA1c, atrial fibrillation, NIHSS score and the number of thrombectomy are all factors that influnce the occurrence of symptomatic intracerebral hemorrhage after mechanical thrombectomy for acute stroke.The establishment of predictive model can achieve the early warning and guide the clinical intervention measures.

       

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