The Significance of Simple Inflammatory Markers in Off Pump Surgery—Review

Urbanowicz T., Gutaj P., Plewa S., Spasenenko I., Krasińska B., Olasińska-Wiśniewska A., Kowalczyk D., Krasiński Z., Grywalska E., Rahnama-Hezavah M., Kowalewski M., Tykarski A., Wender-Ożegowska E., Matysiak J.

Coronary artery atherosclerosis is a common condition characterized by different symptomatology and incidences of risk factors. The disease manifestation may differ; therefore, proper diagnosis is essential. The preventive, diagnostic, and therapeutic arms are still developing to improve patient outcomes. Among diagnostic steps, the non-invasive tools for evaluating non-classical factors related to metabolomic profiles are gaining attention. The aim of this study was to investigate possible metabolic profiling differences between patients with chronic coronary artery disease (CAD) and a control group based on plasma sphingomyelin levels. The study group consisted of 23 patients (72% male, median age of 69 (63–72) years) presenting with chronic coronary syndrome and confirmed epicardial disease in coronary angiography and 15 patients (33% male, median age of 70 (64–72) years) with normal angiographic results. Clinical data were recorded, and blood samples were collected for standard biochemical laboratory assessment and metabolomic profiling. The plasma sphingomyelin levels were evaluated in patients with different degrees of coronary artery atherosclerosis involvement. In addition, the severity of the epicardial disease was estimated by the Gensini Score. The study subgroups did not differ in terms of age (p = 0.765) and co-morbidities, though the male sex was more common in the CAD group (p = 0.007). The analysis revealed significant differences regarding neutrophil count (p = 0.014), neutrophil-to-lymphocyte ratio (NLR) (p = 0.016), and high-density lipoprotein (HDL) (p = 0.003). Among different plasma sphingomyelin species, there was a significant difference in plasma SM42:1 level (16.2 (14.2–19.1) vs. 20.8 (18.9–21.7) (p = 0.044) between the CAD and control groups, respectively. The SM 42:1 plasma level was independent of the number of involved epicardial arteries (p = 0.109). However, Spearman correlations tests were performed between the SM 42:1 plasma level and the number of coronary arteries diagnosed with atherosclerosis disease (rho = −0.356, p = 0.014) and the severity of the disease measured by the Gensini Score (rho = −0.403, p = 0.006). There was no correlation between plasma sphingomyelin levels and NLR (Spearman’s rho = −0.135, p = 0.420), suggesting a lack of inflammatory associations. Further, sphingomyelins showed no relationship with coronary artery disease risk factors such as dyslipidemia and diabetes. Lower plasma SM 42:1 levels were revealed in the CAD group compared with the control group, indicating a possible significance of sphingomyelin 42:1 in coronary artery disease progression.

Shvartz V., Sokolskaya M., Ispiryan A., Basieva M., Kazanova P., Shvartz E., Talibova S., Petrosyan A., Kanametov T., Donakanyan S., Bockeria L., Golukhova E.

Introduction. The pathogenesis of aortic stenosis includes the processes of chronic inflammation, calcification, lipid metabolism disorders, and congenital structural changes. The goal of our study was to determine the predictive value of novel biomarkers of systemic inflammation and some hematological indices based on the numbers of leukocytes and their subtypes in the development of early hospital medical conditions after mechanical aortic valve replacement in patients with aortic stenosis. Materials and methods. This was a cohort study involving 363 patients who underwent surgical intervention for aortic valve pathology between 2014 and 2020. The following markers of systemic inflammation and hematological indices were studied: SIRI (Systemic Inflammation Response Index), SII (Systemic Inflammation Index), AISI (Aggregate Index of Systemic Inflammation), NLR (Neutrophil/Lymphocyte Ratio), PLR (Platelet/Lymphocyte Ratio), and MLR (Monocyte/Lymphocyte Ratio). Associations of the levels of these biomarkers and indices with the development of in-hospital death, acute kidney injury, postoperative atrial fibrillation, stroke/acute cerebrovascular accident, and bleeding were calculated. Results. According to an ROC analysis, an SIRI > 1.5 (p < 0.001), an SII > 718 (p = 0.002), an AISI > 593 (p < 0.001), an NLR > 2.48 (p < 0.001), a PLR > 132 (p = 0.004), and an MLR > 0.332 (p < 0.001) were statistically significantly associated with in-hospital death. Additionally, an SIRI > 1.5 (p < 0.001), an NLR > 2.8 (p < 0.001), and an MLR > 0.392 (p < 0.001) were associated with bleeding in the postoperative period. In a univariate logistic regression, SIRI, SII, AISI, and NLR were statistically significant independent factors associated with in-hospital death. In a multivariate logistic regression model, SIRI was the most powerful marker of systemic inflammation. Conclusion. SIRI, SII, AISI, and NLR as novel biomarkers of systemic inflammation were associated with in-hospital mortality. Of all markers and indices of systemic inflammation in our study, SIRI was the strongest predictor of a poor outcome in the multivariate regression model.