摘要
目的研究急性心肌梗死(AMI)患者在接受经皮冠状动脉介入治疗(PCI)后,院内发生主要不良心血管事件(MACE)的潜在影响要素,构建一个高效且准确的预测模型。方法回顾性收集山东省济宁医学院附属医院2020年1月至12月就诊于急诊科的AMI患者临床资料,将患者根据院内是否发生MACE分为MACE组和无MACE组。对比分析两组患者的临床指标结果,筛选出差异有统计学意义的指标,并将其纳入多因素Logistic回归分析中。基于此分析,构建针对AMI患者PCI术后院内MACE风险的列线图模型。通过绘制模型的受试者工作特征(ROc)曲线来评估其预测准确性,并通过Hosmer-Lemeshow检验来评估模型的拟合优度。结果通过纳入和排除标准的筛选,本研究共纳入了583名患者,其中发生院内MACE的患者85例(14.58%)。单因素分析结果表明,与无MACE组比较,MACE组年龄、Killip分级≥I级、肌红蛋白(MYO)、B型脑钠肽(BNP)、白细胞计数(WBC)凝血酶原时间(PT)心电图T波改变、心脏彩超室壁运动异常发生率、心肌缺血时间>6h、PCI术前发生MACE例数以及左前降支狭窄率等指标升高;发病前曾口服抗板药物、冠心病史、冠心病家族史例数、入院收缩压、左室射血分数(LVEF)等指标降低。多因素分析结果表明,Killip分级、BNP、心肌缺血时间>6h以及PCI术前已经发生MACE是AMI患者PCI术后发生MACE的独立风险因素,发病前曾口服抗板药物和LVEF是独立保护因素。在进一步的研究中,通过独立风险因素构建出列线图模型,进行ROC曲线分析的结果显示,ROC曲线下面积(AUC)为0.817,模型的灵敏度、特异度分别为81.48%和67.66%。此外,通过Hosmer-Lemeshow检验对模型的拟合优度进行了验证,结果显示χ^(2)=1.937,P=0.983。结论本研究构建的列线图模型可以有效评估AMI患者在PCI术后发生院内MACE的风险,为PCI术后AMI患者的预后判断提供了新的辅助工具。
Objective To investigate the potential factors influencing the occurrence of major adverse cardiovascular events(MACE)in patients with acute myocardial infarction(AMI)after percutaneous coronary intervention(PCI)and develop an efficient and accurate predictive model.Methods Clinical data of AMI patients treated in the emergency department of Jining Medical University Affiliated Hospital between January and December 2020 were retrospectively collected.Patients were divided into two groups based on the occurrence of in-hospital MACE,the MACE group and the non-MACE group.Clinical indicators of the two groups were compared,and statistically significant variables were selected for inclusion in a multivariate logistic regression analysis.Based on this analysis,a nomogram model was constructed to predict the risk of in-hospital MACE in AMI patients after PCI.The model's predictive accuracy was evaluated using the receiver operating characteristic(ROC)curve,and the goodness of fit was assessed using the Hosmer-Lemeshow test.Results A total of 583 patients were included after screening based on inclusion and exclusion criteria,of whom 85(14.58%)experienced in-hospital MACE.Univariate analysis showed that compared to the non-MACE group,the MACE group had higher values for age,Killip classification,myoglobin(MYO),brain natriuretic peptide(BNP),white blood cell count(WBC),prothrombin time(PT),T-wave changes on electrocardiogram(ECG),abnormal wall motion on echocardiography,ischemia duration greater than 6 hours,the number of MACE before PCI,and left anterior descending artery stenosis.In contrast,the number of patients with a history of oral antiplatelet medication use,coronary artery disease(CAD),family history of CAD,admission systolic blood pressure,and left ventricular ejection fraction(LVEF)were lower in the MACE group.Multivariate analysis indicated that Killip classification,BNP,ischemia duration greater than 6 hours,and MACE before PCI were independent risk factors for in-hospital MACE in AMI patients after PCI,while pre-onset use of antiplatelet medications and LVEF were independent protective factors.The nomogram model constructed based on independent risk factors demonstrated good predictive ability,with an area under the ROC curve(AUC)of 0.817,a sensitivity of 81.48%,and a specificity of 67.66%.The Hosmer-Lemeshow test confirmed the model's good fit(χ^(2)=1.937,P=0.983).Conclusion The nomogram model developed in this study effectively assesses the risk of in-hospital MACE in AMI patients after PCI,providing a valuable tool for predicting patient outcomes post-PCl.
作者
郭延吉
刘成林
王蔓蔓
师猛
李勇
李若萌
付敏
肖子亚
Guo Yanji;Liu Chenglin;Wang Manman;Shi Meng;Li Yong;Li Ruomeng;Fu Min;Xiao Ziya(Department of Emergency,Affiliated Hospital of Jining Medical University,Jining 272100,Shandong,China;Department of Cardiovascular,Affiliated Hospital of Jining Medical University,Jining 272100,Shandong,China)
出处
《中国中西医结合急救杂志》
CSCD
2024年第5期549-554,共6页
Chinese Journal of Integrated Traditional and Western Medicine in Intensive and Critical Care
基金
山东省医药卫生科技发展计划资助项目(202010000964)。
关键词
急性心肌梗死
院内主要不良心血管事件
列线图
预测价值
Acute myocardial infarction
In-hospital major adverse cardiovascular events
Nomogram
Predictive value
