关键词: 冠状动脉粥样硬化, 系统性炎症反应指数, Gensini评分, 系统性免疫炎症指数, 综合炎症反应指数, 血常规, 炎症标志物

Abstract: Objective  To compare the associations between the three composite inflammatory indices—systemic immune-inflammation Index (SII), systemic inflammation response index (SIRI), and aggregate index of Systemic inflammation (AIRI)—and the severity of coronary artery disease (CAD), as assessed by the Gensini score, and to evaluate their predictive performance.Methods  A total of 845 hospitalized patients who underwent coronary angiography from April 2023 to September 2024 were retrospectively enrolled. Clinical and laboratory data were collected, and SII, SIRI, and AIRI were calculated alongside Gensini scores. Multivariable linear regression models were applied to analyze the associations between each index and the Gensini score. Receiver operating characteristic (ROC) curves were generated to assess the predictive value of these indices for severe coronary stenosis (Gensini score>40). Restricted cubic spline (RCS) analysis was used to examine potential nonlinear relationships, and subgroup interaction analyses were performed. Results  All three indices (SII, SIRI, AIRI) showed a significant upward trend across Gensini score quartiles (P<0.001) and were positively correlated with Gensini scores. After full adjustment for covariates, SIRI remained an independent predictor of Gensini score (β=3.79, P=0.007), demonstrating a stronger predictive effect than SII and AIRI. ROC analysis showed that the area under the curve of SIRI was 0.648, which was superior to AIRI (0.626) and SII (0.598), with statistical significance. Further analysis indicated that incorporating SIRI into the traditional risk factor model resulted in a modest but statistically significant improvement in predictive performance (net reclassification improvement=0.178, P=0.018; integrated discrimination improvement=0.009, P=0.022).RCS analysis suggested a borderline-significant nonlinear positive association between SIRI and Gensini scores (P-overall=0.030). In subgroup analysis, the predictive value of SIRI was significantly strengthened in diabetic patients, with a significant interaction between diabetes status and SIRI (P for interaction=0.045). Conclusion  All three composite inflammatory indices were associated with the severity of coronary artery disease, among which SIRI demonstrated relatively better performance. Incorporating SIRI into a model containing traditional risk factors led to a modest but statistically significant improvement in predictive performance. As a simple and cost-effective biomarker, SIRI holds significant potential for early risk stratification and screening in clinical settings, especially when coronary angiography is unavailable or resources are limited.

Key words: coronary atherosclerosis, systemic inflammation response index, Gensini score, systemic immune-inflammation index, aggregate index of systemic inflammation, complete blood count, inflammatory biomarkers