Abstract: Objective To evaluate the risk factors associated with mid-to-long-term prognosis after transcatheter closure in patients over 40 years old with atrial septal defect (ASD).Methods A total of 257 ASD patients over 40 years old who successfully underwent transcatheter closure at the Affiliated Hospital of Xuzhou Medical University between 2015 and 2021 were consecutively enrolled. Major adverse cardiovascular events (MACE) were recorded during follow-up. A Cox regression model was constructed to analyze the risk factors for MACE, and the predictive value of these factors was assessed by receiver operating characteristic (ROC) curve analysis. Patients were further divided into subgroups, and Kaplan-Meier survival curves were plotted, with differences between groups compared using the log-rank test.Results Among the 257 enrolled patients, the average follow-up duration was (58.0±20.7) months. A total of 31 patients (12.06%) experienced MACE. Significant differences between the MACE and non-MACE groups were observed in terms of age, atrial fibrillation, coronary artery disease, pulmonary arterial hypertension (PAH), alkaline phosphatase (ALP), creatinine (CREA), high-density lipoprotein (HDL), left atrial diameter (LAD), and right atrial diameter (RAD). Multivariate Cox regression analysis indicated that age, PAH, and RAD as independent risk factors for MACE. Specifically, age ≥59 years, RAD≥46 mm, and presence of PAH were associated with higher MACE risk. The combined predictive value of these three factors yielded an area under the ROC curve (AUC) of 0.866, which is superior to any single factor alone. Kaplan-Meier survival analysis further confirmed statistically significant differences in MACE risk among different risk stratification groups.Conclusions Advanced age, increased RAD, and the presence of PAH are independent risk factors for MACE after transcatheter closure in ASD patients over 40 years old. Each of these factors has predictive value for MACE after transcatheter closure, and their combination improves prediction performance.