Abstract
OBJECTIVE: To explore the risk factors for ventricular arrhythmia after percutaneous coronary intervention (PCI) in elderly patients with acute myocardial infarction (AMI). METHODS: A retrospective cohort of 201 elderly AMI patients who underwent PCI in the emergency department of No. 215 Hospital of Shaanxi Nuclear Industry from April 2020 to January 2023 was analyzed. The patients were randomly divided into a training set (n=134) for model development and a test set (n=67) for model validation. The training set was divided into a ventricular arrhythmia group (n=51) and a non-ventricular arrhythmia group (n=83), based on the occurrence of ventricular arrhythmia post-PCI. The factors affecting ventricular arrhythmias were analyzed by logistic regression and Lasso regression models. RESULTS: Lasso regression screened 12 characteristic factors at λ=0.1 se. In the training set, the area under the ROC curve (AUC) of the Lasso model for predicting ventricular arrhythmia was 0.954, which was significantly higher than 0.826 for the Logistic model (P < 0.001). In the test set, the AUC of the Lasso model was 0.962, which was also significantly higher than 0.825 for the Logistic model (P=0.003). CONCLUSION: Compared to the logistic regression model, the Lasso regression model can more accurately predict the occurrence of ventricular arrhythmia after PCI in elderly AMI patients. The Lasso regression model constructed in this study can provide a reference for the clinical identification of high-risk elderly AMI patients and the development of targeted monitoring and treatment.