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<p>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 (<i>p</i> &lt; 0.001). While STOP-BANG was higher in event patients, risk group classification was non-significant (<i>p</i> = 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.</p>

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Incorporating the STOP-BANG questionnaire improves prediction of cardiovascular events during hospitalization after myocardial infarction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月31日

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  • Bahram Shahri,
  • Ali Tajik,
  • Mohsen Moohebati,
  • Vahid Mahdavizadeh

摘要

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.