ALI对ST段抬高型心肌梗死患者PCI术后院内主要不良心血管事件的预测价值OA

Objective To investigate the predictive value of the advanced lung cancer inflammation index(ALI)for in-hospital major adverse cardiovascular events(MACEs)following percutaneous coronary intervention(PCI)in patients with ST-segment elevation myocardial infarction(STEMI).Methods Clinical data of 681 STEMI patients who underwent emergency PCI at the Department of Cardiology,the 904th Hospital of the Joint Logistics Support Force of PLA from November 2016 to March 2022 were retrospectively collected,including general information,laboratory indicators,and imaging parameters.ALI,neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),and systemic immune-inflammation index(SII)were calculated.Patients were divided into MACEs group(n=241)and non-MACEs group(n=440)based on the occurrence of in-hospital MACEs.Clinical characteristics were compared between the two groups.Receiver operating characteristic(ROC)curve analysis was used to evaluate the predictive performance of ALI for in-hospital MACEs,and then to compare it with those of NLR,PLR,and SII.Spearman rank correlation was employed to analyze the correlation between ALI and the Gensini score.Univariate and multivariate logistic stepwise regression analyses were performed to identify independent factors influencing in-hospital MACEs.A nomogram prediction model was constructed based on the independent factors and internally validated using the Bootstrap method(1000 resamples).The model's discrimination and calibration were assessed using the Hosmer-Lemeshow goodness-of-fit test,calibration curve,decision curve analysis(DCA),and ROC curve.Results The ALI index was significantly lower in MACEs group than in non-MACEs group(P<0.05).ROC curve analysis showed that the area under the curve(AUC)of preoperative ALI for predicting in-hospital MACEs was 0.675(95%CI 0.638-0.710),with an optimal cut-off value of 188.07,sensitivity of 58.51%,and specificity of 79.55%.The predictive performance of ALI was superior to that of NLR,PLR,and SII(P<0.01).Correlation analysis revealed a negative correlation between the ALI index and the Gensini score(r=-0.149,P<0.001).Univariate logistic analysis identified age,diabetes,Killip class≥Ⅱ,C-reactive protein,troponin Ⅰ,myoglobin,left main coronary artery lesion,left anterior descending artery lesion,left circumflex artery lesion,right coronary artery lesion,left ventricular ejection fraction(LVEF),Gensini score,ALI>188.07,white blood cell count,and number of vascular lesions≥2 as influencing factors for in-hospital MACEs(P<0.05).Multivariate logistic analysis demonstrated that age(OR=1.042,95%CI 1.023-1.062,P<0.001),Killip class≥Ⅱ on admission(OR=11.023,95%CI 6.738-18.032,P<0.001),and Gensini score(OR=1.012,95%CI 1.003-1.020,P=0.006)were independent risk factors for in-hospital MACEs,while LVEF(OR=0.895,95%CI 0.859-0.933,P<0.001)and preoperative high ALI index(>188.07)(OR=0.249,95%CI 0.156-0.397,P<0.001)were independent protective factors.The nomogram prediction model,incorporating age,Killip class,LVEF,Gensini score,and ALI index,showed a consistency index(C-index)of 0.892 upon internal validation.The model's AUC was 0.895(95%CI 0.867-0.923),with a sensitivity of 79.7%and specificity of 87.5%.The Hosmer-Lemeshow test indicated good model fit(χ²=8.02,P=0.43).Conclusions Preoperative ALI index is an independent protective factor for in-hospital MACEs in STEMI patients after PCI.The nomogram model combining age,Killip class,LVEF,Gensini score,and ALI index demonstrates good predictive performance for in-hospital MACEs.