急性冠症候群におけるリスク層別化向上のための多バイオマーカーモデル：GRACEスコアを超えて

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.