Predictive Value of Pan-Immune Inflammation Value and Systemic Immune-Inflammation Index for Identifying Coronary...

The predictive value of PIV and SII in identifying vulnerable plaques among ACS patients remains poorly understood. This study represents the inaugural use of OCT to identify vulnerable plaques and establishes a predictive model incorporating PIV and SII, enhancing clinical treatment strategies.

A total of 523 eligible ACS patients underwent coronary angiography and OCT. Clinical data were collected and analyzed. Multifactorial logistic regression was employed to identify factors influencing TCFA. Receiver operating characteristic (ROC) curves were constructed to assess the diagnostic accuracy of the PIV and SII for TCFA, with a calculation of the area under the ROC curve (AUC). The optimal cutoff values for PVI and SII were calculated.

Compared to the non-TCFA group, the TCFA group exhibited significantly higher levels of hypersensitive C-reactive protein (hs-CRP), PIV, and SII (all P <0.05). Multifactorial logistic regression analysis revealed that PIV (odds ratio [OR], 1.78; 95% confidence interval [CI], 1.35-2.06; P <0.001) and SII (OR, 1.52; 95% CI, 1.14-2.08; P <0.001) were independent risk factors for TCFA development. The optimal cutoff value for PIV was 490.7, achieving a diagnostic sensitivity and specificity of 75.44% and 89.32%, respectively. For SII, the optimal cutoff value was 802.9, with a diagnostic sensitivity and specificity of 67.54% and 79.61%, respectively.

This study suggests that PIV and SII can serve as noninvasive, practical, and cost-effective biomarkers for evaluating plaque vulnerability in patients with ACS.