目的 探讨瑞加诺生负荷超声心动图对经皮冠状动脉介入治疗（PCI）术后慢性冠脉综合征（CCS）患者预后的评估价值。 方法 选取2023年10月至2025年6月贵州中医药大学第二附属医院心血管内科收治的PCI术后完成瑞加诺生负荷超声心动图检查的CCS患者共113例，按PCI术后6个月内是否发生心血管事件（包括心力衰竭及心肌梗死）再住院或全因死亡分为预后良好组71例及预后不良组42例，比较两组患者负荷超声心动图指标，包括冠状动脉血流速度储备（CFVR）、整体纵向应变（GLS）、左室射血分数（LVEF），并比较两组患者的人口学信息及相关病历资料等。经最小绝对收缩和选择算法（LASSO）回归筛选影响PCI术后CCS患者预后的关键因素，通过logistic回归构建PCI术后CCS患者预后不良预测模型，通过Bootstrap重抽样法对预测模型进行内部验证并采用决策曲线分析评估模型的临床实用性。采用协方差分析（ANCOVA）校正年龄因素进行敏感性分析，进一步验证CFVR和GLS对预后的评估价值。 结果 PCI术后CCS患者预后不良组CFVR、GLS均低于预后良好组患者（P < 0.05），年龄高于预后良好组患者（P < 0.05）。LASSO回归分析结果显示，CFVR、GLS和年龄是PCI术后CCS患者预后不良的关键影响因素，多因素logistic回归分析结果显示，CFVR、GLS降低及高龄是PCI术后CCS患者预后不良的危险因素（P < 0.05）。受试者工作特征曲线（ROC曲线）显示CFVR、GLS、年龄和联合预测曲线下面积（AUC）分别为0.835、0.802、0.607、0.875，内部验证结果及决策曲线分析显示该模型校准度良好并且可显著提升临床净获益率。ANCOVA校正年龄后，预后不良组的CFVR和GLS仍显著低于预后良好组（P < 0.01），进一步证实CFVR和GLS降低是PCI术后CCS患者预后不良的独立危险因素。 结论 CFVR、GLS降低及高龄是PCI术后CCS患者预后不良发生的危险因素，联合模型评估PCI术后CCS患者预后情况可行性较高。

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.