ArticleBMC cardiovascular disorders2025
Relative hyperglycemia and new-onset atrial fibrillation after coronary artery bypass grafting: a focus on stress hyperglycemia ratio.
Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Artificial intelligence and biomarker-driven prediction of post-coronary artery bypass grafting atrial fibrillation: Integrating clinical, genomic, and metabolic insights.Heart rhythm O2 · 2026Review
- Machine learning and Regression-Based models for prediction of postoperative atrial fibrillation following coronary artery bypass grafting: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Elevated stress hyperglycemia ratio predicts intensive care unit admission after surgery for gastrointestinal tumors: an INSPIRE database analysis.Frontiers in medicine · 2026Article
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6 authors.
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Abstract
backgroundNew-onset atrial fibrillation (NOAF) after coronary artery bypass grafting (CABG) is associated with an increased risk of adverse outcomes. The stress hyperglycemia ratio (SHR), a novel biomarker reflecting relative hyperglycemia, has an undetermined role in NOAF risk assessment. This study aimed to evaluate the association between SHR and NOAF after CABG, and to further develop an interpretable machine learning (ML) model to validate its potential value in clinical risk stratification.
methodsCABG patients were retrospectively identified from the Medical Information Mart for Intensive Care (MIMIC) database. Baseline characteristics from the first postoperative ICU day were extracted. The association between SHR and NOAF was assessed using logistic regression and restricted cubic spline methods. Feature selection was performed via multivariable logistic regression, the Boruta algorithm, and LASSO regression, with the intersection of selected variables used to construct 15 ML prediction models. Finally, the SHapley Additive exPlanation (SHAP) method was applied for interpretability analysis of the best-performing model, which was subsequently deployed as an online tool.
resultsA total of 3,498 eligible CABG patients were enrolled, with an NOAF incidence of approximately 18.6% (651 patients). Multivariate logistic regression analysis revealed that, after adjusting for confounding factors, SHR was independently associated with the risk of NOAF after CABG (OR = 1.39, 95% CI: 1.04-1.85, P = 0.027). Restricted cubic spline analysis demonstrated a positive correlation between SHR and NOAF risk, with no significant non-linear relationship observed (P for non-linearity > 0.05). The intersection of the three feature selection approaches identified eight key features, with SHR consistently emerging as an important predictor of NOAF. Among the 15 ML models developed, the XGBoost model exhibited the best performance with an area under the curve (AUC) of 0.813, while removing SHR reduced AUC to 0.779. SHAP analysis further confirmed the positive contribution of elevated SHR levels to NOAF risk.
conclusionThis study demonstrates that SHR is significantly associated with NOAF after CABG, with a higher SHR serving as an independent risk factor. As a novel serological biomarker, SHR shows substantial clinical potential for early risk stratification and management optimization.
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