Incremental Prognostic Value of Admission Blood Glucose to Albumin Ratio in Patients with Acute Coronary Syndrome: A Retrospective Observational Cohort Study

Abstract

Background: Blood glucose and serum albumin can be biomarkers at admission since they are easily accessible and demonstrate correlations with cardiovascular diseases. The predictive ability of the admission blood glucose to albumin ratio (AAR) for long-term prognosis in patients with acute coronary syndrome (ACS) and its potential to elevate the predictive value of the Global Registry of Acute Coronary Events (GRACE) risk score in ACS patients post-percutaneous coronary intervention (PCI) remains unknown. Hence, this study aimed to investigate the incremental prognostic value of the AAR in patients with ACS undergoing PCI.

Methods: A rigorous development-validation approach was implemented to optimize the GRACE risk score, utilizing the AAR parameter in 1498 patients suffering from ACS after PCI at the Third People's Hospital of Chengdu, Sichuan, China.

Results: Over a median of 31.25 (27.53, 35.10) months, the incidence of major adverse cardiac events (MACEs), defined as a composite outcome encompassing all-cause death, cardiac death, nonfatal myocardial infarction, nonfatal stroke, and unplanned repeat revascularization, was higher in individuals with higher AARs. Thus, the AAR was an independent predictor of long-term prognosis in ACS patients undergoing PCI (HR, 1.145; 95% CI: 1.045-1.255; p = 0.004). The integration of the AAR score with the GRACE risk score increased the C statistic from 0.717 (95% CI: 0.694-0.740) to 0.733 (95% CI: 0.690-0.776) (p < 0.01).

Conclusions: The AAR is an independent predictor of prognosis in ACS patients and significantly increased the predictive value of the GRACE risk score.

Keywords: GRACE score; acute coronary syndrome; admission blood glucose; albumin; percutaneous coronary intervention; prognosis.

Fig. 1.

Cumulative incidence of endpoints according to the groups of AAR. Kaplan-Meier curves for the cumulative incidence of major adverse cardiac events (MACEs) (A), all-cause death (B), cardiac death (C), nonfatal myocardial infarction (D), nonfatal stroke (E), and unplanned repeat revascularization (F) based on the AAR index tertiles.

Fig. 2.

ROC curve analyses for predicting MACEs. The AUC of the AAR was 0.665 (95% CI: 0.619–0.711, p 0.001). The AUC of GRACE score was 0.717 (95% CI: 0.673–0.761, p 0.001). The AUC of Alb was 0.645 (95% CI: 0.597–0.694, p 0.001). The AUC of ABG was 0.626 (95% CI: 0.577–0.675, p 0.001). ROC, receiver operating characteristic; AUC, area under the receiver operating characteristic curve.

Fig. 3.

The Restricted Cubic Splines (RCS) analyses between AAR and MACEs. The figure shows HR for MACEs adjusted for gender, smoking status, BMI, diabetes mellitus (DM), rSS, LVEF, and the history of medication use for hypertension, DM, and heart failure. Utilizing Cox proportional hazards regression models to fit data. Solid lines indicate HR, and shadow shapes indicate 95% CI.

Fig. 4.

Decision curve analysis for MACEs. The clinical utility was compared by decision curve analysis, the x-axis calculates the threshold probability, and the y-axis represents net benefits, which is calculated by subtracting the relative harm (false positives) from the benefits (true positives). Baseline model = Baseline GRACE score; Full model = Adding the AAR to the GRACE score.