Objective:To investigate the predictive value of combining multimodal echocardiographic parameters with sero-logical indicators for major adverse cardiac events(MACE)following percutaneous coronary intervention(PCI)in patients with acute myocardial infarction(AMI),and to develop a LASSO-Logistic regression-based prediction model.Methods:A retrospec-tive analysis was performed on clinically relevant data from 80 AMI patients who underwent PCI,including 30 patients in the MACE group and 50 patients in the non-MACE group.The LASSO-Logistic regression method was employed to identify risk factors associated with MACE following PCI in AMI patients.Additionally,a receiver operating characteristic(ROC)curve was constructed to assess the predictive performance of the LASSO-Logistic regression model.Results:The development of the LASSO-Logistic regression model based on multimodal echocardiography and serological indicators revealed that advanced age,Killip class Ⅲ-Ⅳ,multiple coronary artery lesions,TIMI blood flow grade less than 2,elevated BNP levels,high LDL-C,re-duced LVEF,and increased WMSI were risk factors influencing the occurrence of MACE following PCI in AMI patients(P<0.05).The nomogram model was internally validated using the Bootstrap method,demonstrating an area under the ROC curve of 0.921,with a sensitivity of 93.16%and specificity of 90.83%.Conclusion:The LASSO-Logistic regression model,construc-ted using multimodal echocardiographic parameters in conjunction with serological indicators,demonstrates effective predictive capability for the risk of MACE following PCI in AMI patients and exhibits significant clinical utility.