Risk factors for adverse events after percutaneous coronary intervention in patients with acute myocardial infarction： an analysis based on a random forest survival model

Abstract:

Objective To comprehensively analyze the influencing factors for the prognosis of patients with acute myocardial infarction（AMI） after percutaneous coronary intervention（PCI），to construct a prediction model and a prognosis scoring system，and to provide a reference for individualized vascular treatment in clinical practice.Methods A retrospective analysis was performed for all AMI patients who underwent PCI in The Second Affiliated Hospital of Nanchang University from January 2018 to June 2022，with the follow-up outcome of the onset of major adverse cardiovascular events（MACE） for the first time after surgery. The ten-fold cross-validated lasso regression analysis was used to determine the variables to be included in the model，and a random survival forest（RSF） model and a Cox proportional hazards model were constructed. The area under the ROC curve（AUC） and calibration curves were used to evaluate the performance of the model，and a risk calculator was developed according to the fitting results of RSF model.Results A total of 3 880 patients with AMI were finally included in the study，among whom 473（12.2%） experienced MACE within one year after surgery. Lasso regression obtained 15 variables including sex，type of AMI，and hypertension，and the multivariate Cox regression analysis showed that diabetes，low left ventricular ejection fraction（30%~40%），and degree of vascular stenosis were the risk factors for postoperative MACE. In the validation set，the RSF and Cox models had an AUC of 0.774（95%CI=0.761~0.787） and 0.597（95%CI=0.581~0.613），respectively. The calibration curves showed that the model had a relatively high accuracy in predicting the risk of MACE within one year，and RSF score with the optimal cut-off value of 133 could also accurately distinguish the cumulative risk of MACE（P<0.001）.Conclusion The RSF model and the scoring system constructed based on the above factors can effectively predict the risk of postoperative MACE and perform risk stratification，thereby helping cardiovascular physicians to formulate individualized treatment regimens in clinical practice.