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.

PubMed

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.

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