Objective: The systemic immune-inflammation index (SII), derived from counts of neutrophil, platelet and lymphocyte, have been developed to predict clinical outcomes in several cancers and cardiovascular diseases. The aim of this study was to evaluate the utility of SII to predict contrast-induced nephropathy (CIN) in patients with ST-segment elevation myocardial infarction (STEMI) who underwent primary percutaneous coronary intervention (PCI). Methods: A total of 632 patients with STEMI who underwent primary PCI were retrospectively included. The patients divided into two groups based on the presence or absence of CIN. Baseline demographic, laboratory and clinic characteristics were evaluated between the two groups. Logistic regression analysis was used to identify independent predictors of CIN. Results: The receiver-operating characteristic (ROC) curve analysis demonstrated that the optimal cut-off value of SII for predicting CIN was 1282 with a sensitivity 76.1% and specificity 86.7% (AUC: 0.834; 95% CI 0.803-0.863; p<0.001). Multivariate analysis performed in two models (SII; as separate continuous and categorical variables) showed age, estimated glomerular filtration rate (eGFR), diabetes, left ventricular ejection fraction (LVEF), Killip class ≥2, use of intravenous diuretic, Troponin I, and SII as independent predictors of CIN in Model 1. In Model 2, age, eGFR, diabetes, LVEF, Killip class ≥2, use of intravenous diuretic, Troponin I, and a value of SII >1282 (p<0.001, OR 6.205, 95% CI 2.301-12.552) remained as independent predictors of CIN. Conclusion: SII may be a useful and reliable indicator to predict the development of CIN in patients with STEMI undergoing primary PCI than NLR and PLR.

Background and Objectives Lymphocyte-to-monocyte ratio (LMR) has emerged as a new indirect marker of inflammation, which is associated with adverse outcomes in cardiovascular diseases. The aim of this study was to evaluate whether admission LMR is associated with contrast-induced nephropathy (CIN) in patients who underwent percutaneous coronary intervention for acute coronary syndrome (ACS). Methods A total of 873 patients were assessed. LMR was calculated via dividing lymphocyte count by monocyte count. Results LMR was significantly lower in the with-CIN group. ROC analysis showed that the LMR ratios <2.52 predicted CIN development with sensitivity of 66.3% and specificity of 55.8%. Multivariate analysis showed that eGFR, admission glucose, and LMR were independent predictors of CIN in patients with ACS. Conclusion LMR is an easily accessible marker and could be used as a predictor of CIN in patients with ACS undergoing percutaneous coronary intervention.

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Background and Objectives. The coronary slow flow (CSF) is an angiographic finding characterized by delayed opacification of nonobstructive epicardial coronary arteries. Chronic inflammation has been suggested to be mainly responsible for the underlying mechanism of CSF. The systemic immune-inflammation index (SII) is a relatively novel inflammation-based biomarker, derived from counts of peripheral neutrophils, platelets, and lymphocytes, and has been shown to predict clinical outcomes in various malignancies and cardiovascular diseases. The aim of this study is to evaluate the relationship between SII and CSF. Methods. A total of 197 patients (102 patients with CSF; 95 patients with normal coronary flow) were included in this retrospective study. Clinical and angiographic characteristics of patients were obtained from hospital records. Results. Patients with CSF had higher SII, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), and high-sensitivity C-reactive protein (hsCRP) levels compared with the control group. Body mass index ( p = 0.022 , OR 1.151, 95% CI 1.121–1.299), low-density lipoprotein ( p = 0.018 , OR 1.028, 95% CI 1.005–1.052), hsCRP ( p = 0.044 , OR 1.161, 95% CI 1.004–1.343), and SII ( p < 0.001 , OR 1.015, 95% CI 1.003–1.026) were independent predictors of CSF in the multivariable analysis. The optimal cutoff value of SII in predicting CSF was >877 in ROC curve analysis ( p < 0.001 , AUC = 0.892, 95% CI 0.848–0.936). This cutoff value of SII predicted the CSF with a sensitivity of 71.5% and specificity of 92.4%. Spearman correlation analysis showed a positive correlation between the mean TFC value and PLR, NLR, hsCRP, and SII. Conclusions. SII may be used as a better indicator for the prediction of CSF than hsCRP.

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