STEMI患者早期高危室性心律失常发生的危险因素分析

Risk factors of early high-risk ventricular arrhythmia in patients with STEMI

目的分析急性ST段抬高型心肌梗死(STEMI)患者早期高危室性心律失常(HRVA)发生的危险因素。方法纳入164例接受急诊经皮冠脉介入术(PCI)治疗的STEMI患者,并按照术后是否发生HRVA分为HRVA组(31例)与非HRVA组(133例)。收集2组患者的临床资料,使用Logistic回归模型分析发生HRVA的危险因素,采用受试者工作特征(ROC)曲线分析指标的预测价值。结果HRVA组Killip分级Ⅲ~Ⅳ级患者比例、T波峰末间期(Tpe)及校正QT间期(QTc)高于非HRVA组(P<0.05);Killip分级Ⅲ~Ⅳ级、高Tpe、高QTc为HRVA发生的独立危险因素(P<0.05);ROC分析显示,Killip分级、Tpe、QTc及三者联合预测HRVA发生曲线下面积(AUC)及95%CI分别为0.708(0.592~0.824)、0.718(0.614~0.822)、0.670(0.562~0.777)、0.843(0.757~0.928),三者联合预测HRVA的敏感度为74.21%,特异度为88.01%。结论STEMI患者早期HRVA发生的危险因素主要为Killip分级Ⅲ~Ⅳ级、高Tpe、高QTc,三者联合对HRVA有良好的预测效能。

Objective To analyze the risk factors of early high-risk ventricular arrhythmia(HRVA)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods Totally 164 patients with STEMI treated by emergency percutaneous coronary intervention(PCI)in the hospital were selected.According to the presence or absence of HRVA after operation,patients enrolled were divided into the HRVA group(n=31)and the non-HRVA group(n=133).Clinical data of the patients were collected,and Logistic regression analysis was performed to screen the risk factors of HRVA.Receiver operating characteristic(ROC)curve was used to analyze the predictive value of these indicators.Results The proportion of patients with Killip gradeⅢ-Ⅳ,T-peak end interval(Tpe)and corrected QT interval(QTc)were higher/longer in the HRVA group than those in the non-HRVA group(P<0.05).Killip gradeⅢ-Ⅳ,long Tpe and long QTc were independent risk factors for HRVA(P<0.05).ROC analysis showed that the area under the curve(AUC)values and 95%CI of HRVA predicted by Killip classification,Tpe,QTc and their combination were 0.708(0.592-0.824),0.718(0.614-0.822),0.670(0.562-0.777)and 0.843(0.757-0.928),respectively.The prediction sensitivity and specificity of the three in combination were 74.21%and 88.01%.Conclusion Killip gradeⅢ-Ⅳ,high Tpe and high QTc are main risk factors of early HRVA in patients with STEMI.Combination of the three can achieve good predictive performance for HRVA.