The Systemic Immune-Inflammation Index May Predict the Coronary Slow Flow Better Than High-Sensitivity C-Reactive Protein in Patients Undergoing Elective Coronary Angiography


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KARAÜZÜM K., KARAÜZÜM İ., HANCI K., Gokcek D., GÜNAY B., BAKHSHIAN H., ...More

CARDIOLOGY RESEARCH AND PRACTICE, vol.2022, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 2022
  • Publication Date: 2022
  • Doi Number: 10.1155/2022/7344639
  • Journal Name: CARDIOLOGY RESEARCH AND PRACTICE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CINAHL, EMBASE, Directory of Open Access Journals
  • Kocaeli University Affiliated: Yes

Abstract

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.