目的 急性冠脉综合症（acute coronary syndrome， ACS）合并慢性肾病（chronic kidney disease， CKD）患者的主要心血管不良事件（major adverse cardiovascular events， MACE）风险较高，早期预测对改善预后至关重要。无创检测方法因其简便、安全，成为评估此类患者风险的重要工具。本研究旨在评估无创检测指标在预测ACS合并CKD患者MACE发生中的应用价值。 方法 研究纳入216例ACS合并CKD患者，分为Non-MACE组（n = 149）和MACE组（n = 67）。收集患者的一般资料、无创检测指标、心电图及心功能指标，通过单因素和多因素logistic回归分析探讨与MACE的关系，并构建列线图预测模型，通过ROC曲线、校准曲线与决策曲线评估模型性能。 结果 单因素分析中，年龄、BMI、SVR、SVRI、HRV、QTd、SDNN、LVEF、LVEDd、SCOPA-AUT评分和GRACE评分与MACE显著相关。多因素分析发现，SVRI、QTd、SDNN、LVEF、LVEDd、SCOPA-AUT评分和GRACE评分为MACE的独立危险因素。ROC曲线分析显示，模型的AUC值为0.979，灵敏度为0.925，特异度为0.966，准确率为0.9537，表明模型具有极高的诊断准确性。校准曲线和决策曲线分析进一步验证了模型的可靠性。 结论 无创检测指标SVRI、QTd、SDNN、LVEF、LVEDd、SCOPA-AUT评分和GRACE评分对ACS合并CKD患者MACE的预测具有重要价值，构建的预测模型可为临床提供有效的风险评估工具。

Objective Patients with acute coronary syndrome （ACS） combined with chronic kidney disease （CKD） are at high risk of major adverse cardiovascular events （MACE）， and early prediction is crucial for improving prognosis. Non-invasive detection methods， due to their simplicity and safety， have become important tools for assessing risks in such patients. This study aims to evaluate the application value of non-invasive detection indicators in predicting MACE in ACS patients with CKD. Methods The study included 216 ACS patients with CKD， divided into a Non-MACE group （n = 149） and a MACE group （n = 67）. General patient data， non-invasive detection indicators， electrocardiogram （ECG）， and cardiac function indicators were collected. Univariate and multivariate logistic regression analyses were performed to explore the relationship between these indicators and MACE. A nomogram prediction model was constructed， and its performance was evaluated using ROC curve analysis， calibration curve， and decision curve analysis. Results Univariate analysis showed that age， BMI， SVR， SVRI， HRV， QTd， SDNN， LVEF， LVEDd， SCOPA-AUT score， and GRACE score were significantly associated with MACE. Multivariate analysis identified SVRI， QTd， SDNN， LVEF， LVEDd， SCOPA-AUT score， and GRACE score as independent risk factors for MACE. ROC curve analysis revealed that the model had an AUC value of 0.979， sensitivity of 0.925， specificity of 0.966， and accuracy of 0.9537， indicating high diagnostic accuracy. Calibration curve and decision curve analyses further confirmed the model's reliability. Conclusion Non-invasive detection indicators， including SVRI， QTd， SDNN， LVEF， LVEDd， SCOPA-AUT score， and GRACE score， have significant value in predicting MACE in ACS patients with CKD. The constructed prediction model provides an effective tool for clinical risk assessment.