Systemic Inflammatory Indices as Biomarkers for Diagnosing and Predicting Coronary Artery Disease

Coronary artery disease (CAD) is the main cause of mortality worldwide. Inflammation is linked to progression in CAD. The study aimed to estimate the values of main inflammatory indices: the systemic inflammation index (SII), the systemic inflammation response index (SIRI), and the aggregate index of systemic inflammation (AISI) for diagnosing and predicting CAD. This case-control study included (200) participants, (140) patients who were categorized into 80 acute coronary syndrome (ACS) and 60 chronic coronary syndrome patients (CCS) because of coronary angiograms, in addition to 60 health controls. For each participant, the main inflammatory indices SII, SIRI, and AISI were computed based on hematological parameters, as well as serum concentrations of fasting sugar, lipids, hs-CRP, and other biochemical profiles, which were estimated by an immune turbidimetric assay. Clinical and demographic information was documented by a specially designed questionnaire form. ROC and regression analysis were used to assess the predictive values for CAD. Among ACS patients, levels of inflammatory indices SII and AISI were markedly raised in comparison to CCS and controls (p<0.001) while SIRI increased non-significantly (p=0.054), WBC, NLR, PLR, and hs-CRP were also elevated (p<0.001). Logistic regression analyses exposed SII, AISI, hs-CRP, and NLR as strong significant predictors of ACS (OR: 1.901, 1.810, 1.760 and 1.336, all p<0.001), respectively, while SIRI showed slightly lower predictive value (OR:0.908, p=0.020). SII had the highest diagnostic accuracy with an area under the curve (AUC) of 0.934, with an excellent sensitivity and specificity of 88.6% and 83.3% (p=0.020). AISI followed with noteworthy diagnostic capacity AUC:0.921 with 84.8% sensitivity and 92.6% specificity (p=0.005). The SII and AISI have arisen as significant clinical prognostic biomarkers for ACS diagnosis; they could serve as novel predictors for CAD assessment. Combination with clinical assessments could help refine risk stratification and guide treatment intervention.