Shahri Bahram, Tajik Ali, Moohebati Mohsen, Mahdavizadeh Vahid
Department of Cardiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Sci Rep. 2025 May 31;15(1):19180. doi: 10.1038/s41598-025-03882-z.
Obstructive sleep apnea (OSA) may impact outcomes in acute coronary syndrome (ACS) patients. The Global Registry of Acute Coronary Events (GRACE) score assesses cardiovascular risk post-ACS. This study evaluated whether incorporating the STOP-BANG score (a surrogate for OSA) enhances GRACE's predictive ability. A total of 227 myocardial infarction (MI) patients were included, with 66 (29.07%) experiencing in-hospital cardiovascular events. Patients with events were older, predominantly male, and had worse clinical markers, including lower hemoglobin and ejection fraction and higher RDW, creatinine, CRP, and GRACE scores (p < 0.001). While STOP-BANG was higher in event patients, risk group classification was non-significant (p = 0.3). Three models were trained: (1) all selected features, (2) GRACE alone, and (3) GRACE + STOP-BANG. The Extra Trees Classifier performed best (ROC-AUC = 0.82). Adding STOP-BANG improved the F1-score, accuracy, and precision but had a non-significant effect on ROC-AUC. The decision curve analysis showed an increased net benefit when STOP-BANG was incorporated. Feature importance analysis ranked STOP-BANG highest in models, reinforcing its relevance. While this study showed that STOP-BANG improved risk stratification, further multicenter validation is needed to confirm its clinical utility in ACS risk models.
阻塞性睡眠呼吸暂停（OSA）可能会影响急性冠状动脉综合征（ACS）患者的预后。全球急性冠状动脉事件注册研究（GRACE）评分用于评估ACS后的心血管风险。本研究评估纳入STOP-BANG评分（一种OSA替代指标）是否能增强GRACE的预测能力。共纳入227例心肌梗死（MI）患者，其中66例（29.07%）发生院内心血管事件。发生事件的患者年龄更大，以男性为主，临床指标更差，包括血红蛋白、射血分数更低，红细胞分布宽度、肌酐、C反应蛋白和GRACE评分更高（p<0.001）。虽然发生事件的患者STOP-BANG评分更高，但风险组分类无显著性差异（p=0.3）。训练了三种模型：（1）所有选定特征，（2）仅GRACE，（3）GRACE+STOP-BANG。极端随机树分类器表现最佳（ROC-AUC=0.82）。添加STOP-BANG可提高F1分数、准确性和精确性，但对ROC-AUC无显著影响。决策曲线分析显示纳入STOP-BANG时净效益增加。特征重要性分析在模型中将STOP-BANG排在最高位，强化了其相关性。虽然本研究表明STOP-BANG改善了风险分层，但仍需要进一步的多中心验证来证实其在ACS风险模型中的临床效用。
