Predictive value of non-invasive assessment for adverse events in acute coronary syndrome patients with chronic kidney disease

CHEN Xinyu; LUO Xiaolei; HUANG Yimeng; MA Li

doi:10.3969/j.issn.1006-5725.2025.13.005

Abstract：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 nomo-gram 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,sensi-tivity 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.