The Impact of Combining CIRS-G and Clinical Frailty Score on One-Month Mortality in Acute Coronary Syndrome

Background/Objectives: Acute coronary syndrome (ACS) remains a leading cause of short-term mortality, particularly in elderly patients with multimorbidity and frailty. Conventional models such as the GRACE score provide robust prognostication but do not incorporate comorbidity or frailty burden. This study investigated the prognostic value of combining the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) and Clinical Frailty Score (CFS) with GRACE in predicting one-month mortality in older ACS patients. Methods: A single-center, retrospective cohort study was conducted including 90 patients aged ≥65 years admitted with ACS. Demographic, clinical, echocardiographic, and laboratory data were collected. CIRS-G, CFS, and GRACE scores were calculated at admission. The primary endpoint was one-month all-cause mortality. Statistical analyses included group comparisons, correlation tests, logistic regression, and ROC curve analysis. Results: The mean age was 74.8 ± 6.6 years, and 73.3% were male. At one month, mortality was 8.9% (n = 8). Non-survivors had significantly higher CIRS-G (median 18.5 vs. 14.0, p = 0.006), CFS (6.0 vs. 4.0, p = 0.008), and GRACE scores (183 vs. 122, p < 0.001), and lower ejection fraction (32.5 vs. 50.0, p < 0.001) compared with survivors. Logistic regression identified GRACE as the only independent predictor of mortality (OR = 1.081 per 10-point increase, p = 0.044). ROC analysis showed GRACE had the highest discriminative performance (AUC = 0.919), while CIRS-G (AUC = 0.796) and CFS (AUC = 0.777) also demonstrated significant predictive value. The combined CIRS-G + CFS model provided comparable discrimination (AUC = 0.785; sensitivity 75%, specificity 87%). Conclusions: GRACE remains the strongest independent predictor of one-month mortality in elderly ACS patients; however, comorbidity and frailty scores also contribute meaningful prognostic information. Integrating these geriatric assessments with traditional risk models may improve individualized risk stratification and management.