Atrial septal defect (ASD) is a prevalent congenital heart condition in adults, which finally leads to pulmonary hypertension and right heart failure if left untreated. Right heart catheterization (RHC), the current gold standard for determining ASD closure feasibility, is invasive. Thus, a noninvasive prescreening tool is urgently needed. In a multicenter, retrospective study, we assessed 924 ASD patients (2012-2022) to determine their suitability for ASD closure. Using LASSO regression, we identified predictors for a correctable shunt, enabling us to create the ASD model. The ASD model, comprising of estimated pulmonary artery systolic pressure (ePASP), peak velocity through the pulmonary valve (PV), peak E-wave velocity through the tricuspid valve (TVE), and right atrial longitudinal dimension (RA) by echocardiography, was constructed and exhibited favorable discriminative capability with an area under the curve (AUC) of 0.941 (95% CI: 0.920-0.961) in the derivation group. The model also demonstrated good calibration and discriminative abilities in the validation cohort. When juxtaposed with the earlier congenital heart disease (CHD) model, the newly developed ASD model demonstrated superior predictive capabilities for correctable shunt, supported by the net reclassification index (NRI) [0.063 (95% CI: 0.001-0.127, p = 0.047)] and integrated discrimination improvement (IDI) [0.023 (95% CI: 0.011-0.036, p <0.001)]. In summary, our research advocates the ASD model as a superior tool for screening suitable ASD defect closure candidates.