基于残余炎症风险构建的列线图模型对急性心肌梗死患者介入术后院内MACE的预测价值

Predictive value of residual inflammation risk-based nomogram model for major adverse cardiovascular events in acute myocardial infarction patients after percutaneous coronary intervention

目的构建基于残余炎症风险(residual inflammatory risk,RIR)的列线图预测模型,并评估其预测急性心肌梗死(acute myocardial infarction,AMI)患者经皮冠状动脉介入术(percutaneous coronary intervention,PCI)后院内主要心血管不良事件(major adverse cardiovascular events,MACE)发生风险的价值。方法回顾性分析2017年6月至2019年3月本院心血管内科收入且行PCI治疗的297例AMI患者的临床资料,依据患者术前超敏C反应蛋白(hypersensitive-CRP,hs-CRP)和血浆低密度脂蛋白胆固醇(low-density lipoprotein cholesterol,LDL-C)指标将患者分为残余炎症风险(residual inflammation risk,RIR)组(n=28)和非RIR组(n=269)。通过患者住院期间的MACE发生情况将患者分为院内MACE(n=102)和非院内MACE组(n=195),采用最小绝对收缩与选择算子(least absolute shrinkage and selection operator,LASSO)回归法和多因素Logistic回归分析评估残余炎症风险与MACE发生之间的关联性,同时筛选出MACE发生的其他独立风险预测因素,应用R语言软件构建基于残余炎症指标的列线图模型并对其进行验证。结果残余炎症风险组患者院内泵衰竭、心源性休克、恶性心律失常、死亡及总体MACE的发生率均高于非残余炎症风险组(P<0.05);LASSO回归及多因素回归分析显示,左室射血分数(left ventricular ejection fraction,LVEF)、血红蛋白浓度与院内MACE发生风险呈负相关(P<0.05),RIR、糖化血红蛋白(hemoglobinA1c,Hb1Ac)、白细胞计数、氨基末端脑钠肽前体(N-terminal pro-B type natriuretic peptide,NT-proBNP)与院内MACE发生风险呈正相关(P<0.05)。受试者工作特征曲线(receiver operator characteristic curve,ROC曲线)提示RIR预测院内MACE发生的能力欠佳(AUC=0.592,95%CI:0.551~0.634),构建的列线图的Harrel’s C-index为0.872(95%CI:0.827~0.917),内部验证后列线图的ROC曲线下面积为0.866(95%CI:0.818~0.907),Hosmer-Lemeshow偏差性检验提示列线图的预测概率与现况值之间具有较好的一致性(χ^(2)=8.420,P=0.393)。临床决策曲线发现,当患者院内MACE发生的阈值概率处于0.08~0.88之间时,应用列线图模型所获得的净收益最高,提示列线图模型的临床适用性较好。结论基于残余炎症风险和其他5个因素构建的预测模型在急性心肌梗死患者术后MACE的发生风险方面具有较好的预测效率和临床适用性。

Objective To develop a nomogram prediction model based on residual inflammation risk(RIR)and evaluate its value in the prediction for the risk of major adverse cardiovascular events(MACE)in patients with acute myocardial infarction(AMI)after percutaneous coronary intervention(PCI).Methods Clinical data of 297 AMI patients who underwent PCI in our department from June 2017 to March 2019 were collected and retrospectively analyzed.According to their levels of preoperative hypersensitive-CRP(hs-CRP)and low-density lipoprotein cholesterol(LDL-C),they were divided into RIR group(n=28)and non RIR group(n=269).They also were assigned into the MACE group(n=102)and non-MACE group(n=195)according to whether MACEs occurred during hospitalization.Least absolute shrinkage and selection operator(LASSO)regression analysis and multivariate logistic regression analysis were used to evaluate the correlation between the risk of RIR and the occurrence of MACE,and other independent risk predictors of MACE were screened out.The nomogram model based on the indicators of RIR was constructed and verified by the R language software.Results The incidences of pump failure,cardiogenic shock,malignant arrhythmia and death,and overall MACE were significantly higher in the RIR group than the non-RIR group(P<0.05).The results of LASSO regression and multivariate regression analyses showed that left ventricular ejection fraction(LVEF)and hemoglobin concentration were negatively correlated with the risk of MACE in hospital(P<0.05),while residual inflammation risk,hemoglobinA1 c(Hb1 Ac),leukocyte count and N-terminal pro-B type natriuretic peptide(NT-proBNP)level were positively correlated with the risk of MACE(P<0.05).The ROC curve indicated that RIR was not good in prediction of MACE(AUC=0.592,95%CI:0.551~0.634).Based on the above 6 indicators,the nomogram model was constructed.The Harrel’s C-index of the nomogram was 0.872(95%CI:0.827~0.917),the AUC of the nomogram after re-sampling 1000 times was 0.866(95%CI:0.818~0.907),and the Hosmer lemeshow deviation test showed that the prediction probability of nomogram was consistent with the actual frequency(Chi-square=8.420,P=0.393).The clinical decision curve showed that when the threshold probability of MACE occurrence is between 0.08 and 0.88,and the nomogram model could obtain the highest net benefit,which indicated that the model had good clinical applicability.Conclusion The RIR-based nomogram model and other 5 factors have good prediction efficiency and clinical applicability in the prediction of the risk of MACE in AMI patients.