Objective To explore the prognostic value of regadenoson stress echocardiography in patients with chronic coronary syndromes after percutaneous coronary intervention （PCI）. Methods A total of 113 CCS patients who underwent PCI and completed regadenoson stress echocardiography at the Department of Cardiology， Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine， from October 2023 to June 2025， were enrolled. According to whether cardiovascular events （including heart failure and myocardial infarction） leading to rehospitalization or all-cause death occurred within 6 months after PCI， patients were divided into the good prognosis group （71 cases） and the poor prognosis group （42 cases）. Stress echocardiography indicators， including coronary flow velocity reserve （CFVR）， global longitudinal strain （GLS）， and left ventricular ejection fraction （LVEF）， were compared between the two groups， along with demographic information and relevant clinical data. Key factors affecting the prognosis of CCS patients after PCI were screened using Least Absolute Shrinkage and Selection Operator （LASSO） regression， and a predictive model for poor prognosis in CCS patients after PCI was constructed using logistic regression. Internally validate the predictive model using the Bootstrap resampling method and assess the clinical utility of the model using decision curves analysis. Analysis of covariance （ANCOVA） was used to adjust for age in a sensitivity analysis to further validate the prognostic value of CFVR and GLS. Results CFVR and GLS in the poor prognosis group were lower than those in the good prognosis group （P < 0.05）， and the age was higher than in the good prognosis group （P < 0.05）. LASSO regression analysis showed that CFVR， GLS， and age were key factors affecting poor prognosis in CCS patients after PCI. Multivariate logistic regression analysis indicated that reduced CFVR， reduced GLS， and advanced age were risk factors for poor prognosis in CCS patients after PCI （P < 0.05）. Receiver operating characteristic （ROC） curves showed that the area under the curve （AUC） for CFVR， GLS， age， and the combined prediction model were 0.835， 0.802， 0.607， and 0.875. Internal validation results and decision curve analysis show that this model has good calibration and can significantly improve clinical net benefit. After adjusting for age by ANCOVA， CFVR and GLS remained significantly lower in the poor prognosis group than in the good prognosis group （P < 0.01）， further confirming that reduced CFVR and GLS are independent risk factors for poor prognosis in CCS patients after PCI. Conclusions Reduced CFVR， reduced GLS， and advanced age are risk factors for poor prognosis in CCS patients after PCI. The combined model is highly feasible for evaluating postoperative prognosis in CCS patients after PCI.