• 纤维蛋白原、肿瘤坏死因子-α、D-二聚体可预测急性脑梗死患者溶栓后出血性转化的风险
  • 杨华.纤维蛋白原、肿瘤坏死因子-α、D-二聚体可预测急性脑梗死患者溶栓后出血性转化的风险[J].内科急危重症杂志,2023,29(4):293-297
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    DOI:10.11768/nkjwzzzz20230408
    中文关键词:  决策树  急性脑梗死  静脉溶栓  出血性转化  受试者工作特征曲线
    英文关键词:
    基金项目:邢台市重点研发计划项目(No:2020ZC210)
    作者单位E-mail
    杨华 邢台市第三医院 xtyuqing@126.com 
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    中文摘要:
          摘要 目的:分析血纤维蛋白原(FIB)、肿瘤坏死因子(TNF)-α、D-二聚体(D-D)对急性脑梗死(ACI)患者溶栓后出血性转化(HT)的风险预测价值。方法:收集进行阿替普酶静脉溶栓治疗的急性脑梗死患者166例,根据是否发生HT,分为HT组36例,非HT组130例。对2组的基本资料进行单因素分析,将单因素中差异有统计学意义的指标纳入决策树分析,获得风险因素和决策树模型。使用受试者工作特征(ROC) 曲线判定决策树模型的预测价值。结果:2组患者高血压、糖尿病、房颤、脑梗死史、抗血小板药物史、大面积脑梗死史、年龄、溶栓前美国卫生研究院卒中量表(NIHSS)评分及溶栓后24h的收缩压、白细胞计数、FIB、D-D、TNF-α比较,差异有统计学意义(P均<0.05);决策树分析共获得5层二分类树,包括20个分类节点,10条决策路径:溶栓后24hD-D>2.58mg/L、TNF-α>161.74ng/L、溶栓前NIHSS评分>15分、溶栓后24hFIB<2.25mg/L、大面积脑梗死史是ACI患者溶栓后发生HT的独立危险因素(P均<0.05)。该模型的ROC曲线下面积为0.909,95%CI:0.874~0.945,灵敏度86.11%,特异性92.31%。结论: 溶栓后24hD-D、TNF-α、溶栓前NIHSS评分、溶栓后24hFIB、大面积脑梗死史对ACI静脉溶栓后发生HT具有较高的预测价值。
    英文摘要:
          Abstract Objective: To investigate the predictive value of fibrinogen (FIB), tumor necrosis factor-α (TNF-α) and D-dimer (D-D) in the risk of hemorrhagic transformation (HT) after thrombolysis in patients with acute cerebral infarction (ACI). Methods: Totally, 166 patients with ACI treated with alteplase intravenous thrombolysis were studied and divided into HT group (36 cases) and non-HT group (130 cases). The basic data of the two groups were analyzed by single factor analysis, and the indexes with statistically significant difference in single analysis were included in decision tree analysis to obtain independent risk factors. The receiver operating characteristic (ROC) curve was used to determine the predictive value of the decision tree model. Results: The basic data of HT group and non-HT group showed statistically significant difference in hypertension, diabetes, atrial fibrillation, history of cerebral infarction, antiplatelet drug history, large area cerebral infarction, age, NIHSS score before thrombolysis, systolic blood pressure 24h after thrombolysis, FIB, D-D and TNF-α 24h after thrombolysis (P< 0.05). A total of 5-level binary classification trees were obtained by decision tree analysis, including 20 classification nodes and 10 decision paths: D-D> 2.58mg/L and TNF-α> 161.74ng/L24h after thrombolysis, NIHSS score > 15 before thrombolysis, FIB< 2.25mg/L24h after thrombolysis and history of massive cerebral infarction were independent risk factors for HT after thrombolysis in ACI patients (P< 0.05). The area under ROC curve of the model was 0.909, 95% confidence interval was 0.874-0.945, sensitivity was 86.11%, specificity was 92.31%. Conclusion: D-D at 24h after thrombolysis, TNF-α, FIB and NIHSS score before thrombolysis, and history of massive cerebral infarction have high predictive value in HT after intravenous thrombolysis in ACI.