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Beyond the GRACE Score: A Multi-Biomarker Model for Improved Risk Stratification in Acute Coronary Syndromes
Gamze Yeter Arslan1, Erkan Baysal2
1Department of Cardiology, Kepez State Hospital, 07320 Antalya, Türkiye.
Insights
Integrating inflammation, liver, and kidney function biomarkers with the GRACE score improves early mortality prediction in acute coronary syndromes (ACS). This multi-biomarker approach enhances risk stratification for high-risk ACS patients.
Area of Science:
- Cardiology
- Biomarker Research
- Predictive Analytics
Background:
- The GRACE score is a standard tool for predicting early mortality in acute coronary syndromes (ACS).
- Existing models may not fully capture the interplay of inflammation, liver, and kidney dysfunction in ACS prognosis.
- Novel biomarkers are needed to refine risk stratification in ACS patients.
Purpose of the Study:
- To assess the incremental prognostic value of a multi-biomarker model (CAR, ALBI, BUN/Cr) beyond the GRACE score for in-hospital mortality in ACS.
- To determine if integrating inflammatory, hepatic, and renal biomarkers improves risk prediction in ACS.
Main Methods:
- Retrospective analysis of ACS patients' baseline laboratory data.
- Calculation of C-reactive protein/albumin ratio (CAR), albumin-bilirubin (ALBI) score, and blood urea nitrogen/creatinine (BUN/Cr) ratio.
- Logistic regression and ROC curve analysis to compare predictive accuracy of GRACE score alone versus the integrated multi-biomarker model.
Main Results:
- Elevated CAR, ALBI, and BUN/Cr ratios were independently associated with increased in-hospital mortality in ACS patients.
- The multi-biomarker model significantly improved the predictive accuracy of the GRACE score.
- The integrated model showed a higher area under the curve (AUC) compared to the GRACE score alone.
Conclusions:
- A multi-biomarker strategy combining CAR, ALBI, and BUN/Cr ratios offers enhanced early mortality prediction in ACS.
- These readily available laboratory markers can aid clinicians in precise identification of high-risk ACS patients upon admission.
Abstract:
Background: The GRACE score is widely used to estimate early mortality in acute coronary syndromes (ACS), yet its ability to capture the complex interaction between inflammation, hepatic dysfunction, renal impairment, and myocardial injury remains limited. Integrating biomarkers that reflect these complementary physiological pathways may enhance risk prediction and allow earlier identification of high-risk patients. This study evaluated whether a multi-biomarker model incorporating the C-reactive protein/albumin ratio (CAR), the albumin-bilirubin (ALBI) score, and the blood urea nitrogen/creatinine (BUN/Cr) ratio provides incremental prognostic value beyond the GRACE score and traditional cardiac markers. Methods: This retrospective study included patients hospitalized with ACS. Baseline laboratory results were used to calculate CAR, ALBI, and BUN/Cr ratios. Troponin and hemoglobin values were recorded as standard cardiac and hematologic indicators. The primary outcome was in-hospital mortality. Logistic regression models, receiver operating characteristic (ROC) curve analysis, and comparisons of area under the curve (AUC) were performed to determine whether the multi-biomarker model improved risk stratification beyond the GRACE score alone. Results: Higher CAR, ALBI, and BUN/Cr values were each associated with increased in-hospital mortality. When combined with the GRACE score, the multi-biomarker model significantly improved predictive accuracy. The integrated model demonstrated a higher AUC compared with GRACE alone, indicating incremental prognostic value across inflammatory, hepatic, and renal pathways. Conclusions: A multi-biomarker strategy combining CAR, ALBI, and BUN/Cr ratios enhances early mortality prediction beyond the GRACE score in patients with ACS. Incorporating these readily available laboratory indices may help clinicians identify high-risk patients more precisely at the time of hospital admission.
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