急性脑梗死患者血清miR-130a水平表达与溶栓后再出血转化风险的预测模型及验证

Prediction Model and Validation of Serum miR-130a Expression in Patients with Acute Cerebral Infarction and the Risk of Hemorrhagic Transformation after Thrombolysis

目的研究微小核糖核酸(micro RNA,miR)-130a与急性脑梗死(acute cerebral infarction,ACI)患者溶栓后再出血转化的关系,并分析再出血转化的危险因素,建立列线图预测模型。方法纳入2018年8月~2020年8月于邢台市第三医院诊治的175例急性脑梗死患者作为建模组,根据出血转化情况分为发生组、未发生组,另纳入同期于该院诊治的100例急性脑梗死患者作为验证组。比较发生组、未发生组的miR-130a表达情况,采用ROC曲线分析miR-130a对于出血转化的预测价值。应用单因素、多因素Logistic回归模型分析出血转化的影响因素。应用R软件建立预测急性脑梗死患者溶栓后出血转化的列线图模型并进行验证,采用ROC曲线分析该模型预测建模组、验证组患者发生出血转化的效能。结果建模组中再出血转化组(发生组)45例,未发生组130例,出血转化率为25.71%。单因素、多因素Logistic回归分析显示心房颤动(OR=1.684,95%CI:1.124~2.521)、基线NIHSS评分(OR=2.627,95%CI:1.169~5.903)、溶栓药物剂量(OR=0.535,95%CI:0.306~0.938)、溶栓前血糖(OR=12.305,95%CI:1.250~4.248)、CT早期梗死面积(OR=1.747,95%CI:1.144~2.668)和miR-130a(OR=2.106,95%CI:1.123~3.952)均是急性脑梗死患者溶栓后再出血转化的影响因素(均P<0.05)。ROC曲线分析显示,血清miR-130a预测再出血转化的最佳截断值为1.08,曲线下面积为0.803(95%CI:0.730~0.876)。列线图模型预测建模组与验证组的C-index分别为0.844和0.816。建模组的AUC为0.823(95%CI:0.753~0.894),敏感度和特异度分别为88.89%,76.92%,验证组的AUC为0.797(95%CI:0.722~0.872),敏感度和特异度分别为81.82%,74.36%。结论心房颤动、基线NIHSS评分、溶栓药物剂量、溶栓前血糖、CT早期梗死面积和miR-130a均是急性脑梗死患者溶栓后再出血转化的影响因素,以这6项指标建立的列线图模型具有良好的再出血转化预测效能。

Objective To study the relationship between miR-130a and rebleeding transformation after thrombolysis in patients with acute cerebral infarction(ACI),analyze the risk factors of rebleeding transformation,and establish a nomogram prediction model.Methods 175 patients with ACI who were diagnosed and treated in the Third Hospital of Xiangtai City from August 2018 to August 2020 were included as the modeling group.According to the bleeding transformation,they were divided into an occurrence group and a non-occurrence group.In addition,100 patients with ACI diagnosed and treated in the Third Hospital of Xingtai City during the same period were included as the verification group.The expression of miR-130a in the occurrence group and the non-occurrence group was compared,and the ROC curve was used to analyze the predictive value of miR-130a for hemorrhage transformation.Single-factor and multi-factor Logistic regression model was used to analyze the influencing factors of hemorrhage transformation.The R software was used to establish and verify the nomogram model for predicting hemorrhage transformation after thrombolysis in patients with acute cerebral infarction.The ROC curve was used to analyze the effectiveness of the model to predict hemorrhage transformation in themodeling group and the verification group.Results In the modeling group,there were 45 cases in the rebleeding conversion group(occurrence group)and 130 cases in the non-occurring group.The bleeding conversion rate was 25.71%.Univariate and multivariate Logistic regression analysis showed atrial fibrillation(OR=1.684,95%CI:1.124~2.521),baseline NIHSS score(OR=2.627,95%CI:1.169~5.903),thrombolytic drug dose(OR=0.535,95%CI:0.306~0.938),prethrombolysis blood glucose(OR=12.305,95%CI:1.250~4.248),CT early infarct area(OR=1.747,95%CI:1.144~2.668)and miR-130a(OR=2.106,95%CI:1.123~3.952)were the influencing factors of rebleeding transformation after thrombolysis in patients with acute cerebral infarction(P<0.05).ROC curve analysis showed that the best cut-off value of serum miR-130a for predicting rebleeding transformation was 1.08,and the area under the curve was 0.803(95%CI:0.730~0.876).The C-index of the nomogram model predictive modeling group and verification group were 0.844 and 0.816,respectively.The AUC of the modeling group was 0.823(95%CI:0.753~0.894),the sensitivity and specificity were 88.89%and 76.92%,respectively.The AUC of the verification group was 0.797(95%CI:0.722~0.872),the sensitivity and specificity were 81.82%and 74.36%respectively.Conclusion Atrial fibrillation,baseline NIHSS score,thrombolytic drug dosage,blood glucose before thrombolysis,CT early infarct size,and miR-130a were all influencing factors for the transformation of rebleeding after thrombolysis in patients with acute cerebral infarction.The nomogram model established by the above 6 indicators has a good performance in predicting the conversion of rebleeding.