The role of systemic inflammatory response index in predicting myocardial infarction in patients with unstable angina.

BACKGROUND

Unstable angina (UA) is a high-risk presentation of acute coronary syndrome (ACS) that can rapidly progress to myocardial infarction (MI) if not identified and managed promptly. Inflammation plays a key role in plaque instability and thrombotic events, making inflammatory biomarkers useful tools for early risk assessment. The Systemic Inflammatory Response Index (SIRI), derived from peripheral blood cell counts, has emerged as a novel marker of systemic inflammation, but its prognostic utility in UA remains underexplored.

OBJECTIVE

The purpose of this study was to investigate the role of the Systemic Inflammatory Response Index (SIRI) in predicting myocardial infarction and major adverse cardiovascular events (MACE) in patients presenting with unstable angina.

METHODS

This retrospective observational study included 129 adult patients diagnosed with unstable angina and admitted to a tertiary care center. SIRI was calculated as (neutrophil count × monocyte count)/lymphocyte count using laboratory values obtained at admission. Patients were stratified into low and high SIRI groups based on a cutoff derived from ROC analysis. Clinical, laboratory, and angiographic data were collected, and outcomes including MI and MACE (composite of MI, cardiovascular death, stroke, and urgent revascularization) were assessed. ROC curves, logistic regression, and Kaplan-Meier analysis were used for statistical evaluation.

RESULTS

Patients with high SIRI levels had significantly higher rates of myocardial infarction (38% vs. 10%, p < 0.001) and MACE (17.1% vs. 6.1%, p < 0.01). SIRI demonstrated excellent predictive performance for MI with an AUC of 0.858, sensitivity of 90%, and specificity of 94%. Multivariate logistic regression confirmed SIRI as an independent predictor of MI (OR = 2.15, 95% CI: 1.25-3.71).

CONCLUSION

SIRI is a simple, accessible, and powerful inflammatory marker that independently predicts myocardial infarction and MACE in patients with unstable angina. Its integration into early risk assessment may enhance clinical decision-making and improve patient outcomes.