Kinetics and prognostic value of soluble VCAM‐1 in ST‐segment elevation myocardial infarction patients

Abstract

Background Soluble vascular cell adhesion molecule‐1 (sVCAM‐1) is a biomarker of endothelial activation and inflammation. There is still controversy as to whether it can predict clinical outcome after ST‐elevation myocardial infarction (STEMI). Our aim was to assess the sVCAM‐1 kinetics and to evaluate its prognostic predictive value. Method We prospectively enrolled 251 consecutive STEMI patients who underwent coronary revascularization in our university hospital. Blood samples were collected at admission, 4, 24, 48 h and 1 month after admission. sVCAM‐1 serum level was assessed using ELISA assay. All patients had cardiac magnetic resonance imaging at 1‐month for infarct size (IS) and left ventricular ejection fraction (LVEF) assessment. Clinical outcomes were recorded over 12 months after STEMI. Results sVCAM‐1 levels significantly increased from admission up to 1 month and were significantly correlated with IS, LVEF, and LV end‐systolic and diastolic volume. (H48 area under curve (AUC) ≥ H48 median) were associated with an increased risk of adverse clinical events during the 12‐month follow‐up period with a hazard ratio (HR) = 2.6 (95% confidence interval [CI] of ratio = 1.2–5.6, p = .02). The ability of H48 AUC for sVCAM‐1 to discriminate between patients with or without the composite endpoint was evaluated using receiver operating characteristics with an AUC at 0.67 (0.57–0.78, p = .004). This ability was significantly superior to H48 AUC creatine kinase (p = .03). Conclusions In STEMI patients, high sVCAM‐1 levels are associated with a poor clinical outcome. sVCAM‐1 is an early postmyocardial infarction biomarker and might be an interesting target for the development of future therapeutic strategies.

The abnormal level and prognostic potency of multiple inflammatory cytokines in PCI‐treated STEMI patients

“…For example, one previous study indicates that the elevation of IL‐6 is correlated with high MACE risk in STEMI patients who undergo PCI therapy 13 . Another study finds the important role of VCAM‐1 in the occurrence of post‐PCI restenosis 23 . Nevertheless, the prognostic value of inflammatory cytokines lacks comprehensive study at present.…”

“…A few studies have been carried out to explore the correlation of some specific inflammatory cytokines with MACE risk in STEMI patients. 13 , 15 , 23 , 24 , 25 For example, one previous study indicates that the elevation of IL‐6 is correlated with high MACE risk in STEMI patients who undergo PCI therapy. 13 Another study finds the important role of VCAM‐1 in the occurrence of post‐PCI restenosis.…”

Endothelitis profile in acute heart failure and cardiogenic shock patients: Endocan as a potential novel biomarker and putative therapeutic target

“…Circulating endothelial cells in the peripheral blood as well as E-selectin, von Willebrand factor and soluble thrombomodulin were shown to be significantly higher in patients with HF although they did not present significant differences between AHF and chronic HF and did not correlate with P-BNP ( Chong et al, 2006a ), ejection fraction or New York Heart Association (NYHA) class ( Chong et al, 2006b ). Also, VCAM-1 and ICAM-1 were shown to be associated with the development of new post-acute myocardial infarction HF symptoms ( Lino et al, 2019 ) and increased subset-specific monocyte expression of their receptors were observed in AHF patients, with potential prognostic value of VCAM-1R ( Wrigley et al, 2013 ) and sVCAM-1 after ST-elevation myocardial infarction ( Hayek et al, 2021 ). But of all these, serum endocan has been one of the most qualified for the cardiovascular arena ( Bessa et al, 2020 ).…”

CMRSegTools: An open-source software enabling reproducible research in segmentation of acute myocardial infarct in CMR images

“…CMRSegTools, includes customised tools for user-assisted myocardial segmentation; manual, semi-and automatic lesion segmentation which can be used in the generation of training data sets for Machine Learning (ML) strategies. This computer-based tool can be easily deployed on clinical infrastructures, closing the gap between analysis methods and their direct utilisation in clinical trials, as demonstrated in the studies [22][23][24][25].…”

“…A comparison between expert and automated segmentation methods is presented in Figs 7 and 8 as an example of the output of the current version. CMRSegTools has been tested and used successfully in several studies [22][23][24][25].…”