围术期肺部感染PCI冠心病患者术后6个月MACE危险因素及其预测模型

Risk factors and prediction model analysis of 6-month MACE in patients with coronary heart disease and perioperative pulmonary infection after PCI

目的分析围术期肺部感染的经皮冠状动脉介入术(PCI)术后冠心病患者6个月主要心血管不良事件(MACE)的危险因素,同时构建预测模型,并对其进行验证。方法选择绍兴文理学院附属医院心内科2018年3月-2021年3月接受PCI术并在围术期发生肺部感染患者119例为研究对象,根据术后6个月内随访结果,统计所有患者MACE情况,将患者分为MACE组和非MACE组。收集患者一般性资料,包括性别、年龄、美国纽约心脏病协会(NYHA)分级、肺部感染病原、临床肺部感染评分(CPIS)、合并糖尿病、合并高血压、既往卒中史、既往心肌梗死、支架数量。采用Logistic回归分析接受PCI术并在围术期发生肺部感染患者6个月内并发MACE危险因素并将回归分析中有意义的变量纳入预测模型中,受试者工作特征(ROC)曲线分析预测模型与单因素预测MACE价值,并将预测模型回代验证。结果多因素Logistic回归分析结果显示NAHA分级、CPIS评分、既往心肌梗死均为MACE发生的独立影响因素(OR=3.300、3.562、3.295,P<0.05);ROC分析结果显示,预测模型对于PCI术围术期发生肺部感染患者6个月内MACE曲线下面积为0.932(0.857~0.983),均高于单因素预测结果,预测模型不同得分患者MACE发生率比较差异有统计学意义(P<0.05),表明模型对于MACE风险有较好的区分价值。结论以NAHA分级、CPIS评分、既往心肌梗死3个临床因素建立围术期肺部感染的PCI术后冠心病患者6个月MACE预测模型对于临床预测MACE发生有较好的指导价值。

OBJECTIVE To analyze the risk factors of major adverse cardiovascular events(MACE)in coronary heart disease patients with lung infection after percutaneous coronary intervention(PCI)during perioperative period,and to construct a prediction model and verify it.METHODS Total of 119 patients underwent PCI in the department of cardiology,Affiliated Hospital of Shaoxing University of Arts and Sciences from Mar 2018 to Mar 2021 were enrolled.The MACE status of all patients was collected by 6-months-follow-up after surgery and the patients were divided into the MACE group and non-MACE group.General data of all the patients were collected,including gender,age,American Heart Association of new york(NYHA)classification,lung infection pathogen,clinical lung infection score(CPIS),combined diabetes,combined hypertension,previous stroke history,previous myocardial infarction,and number of stents.Logistic regression analysis was used to analyze the risk factors for MACE in patients with perioperative pulmonary infection within 6 months after PCI.The meaningful variables in the regression analysis were included in the predictive model.The receiver operating characteristic curve analyzed the predictive value in MACE of the model and the single factor,and the predict model was verified.RESULTS Multivariate logistic regression analysis showed that NAHA grade,CPIS score,and previous myocardial infarction were all independent influencing factor for MACE(OR=3.300,3.562,3.295,P<0.05).Receiver operating characteristic curve(ROC)analysis showed that the area under the MACE curve was 0.932(0.857-0.983),which was higher than univariate prediction model.There was significant difference in the incidence of MACE among patients with different scores by using the prediction model(P<0.05),indicating a better distinguishing value for MACE risk.CONCLUSION The 6-month MACE prediction model for patients with coronary heart disease complicated with pulmonary infection after PCI during perioperative period based on NAHA classification,CPIS score,and previous myocardial infarction has a good guiding value for the clinical prediction of MACE occurrence.