Systemic immune‐inflammation index predicted short‐term outcomes in ATAD patients undergoing surgery

Xu, Hongjie; Yang, Li; Wang, He; Yao, Yuan; Chen, Di; Sun, Yinghao; Xu, Zhiyun

doi:10.1111/jocs.16300

Cited by 13 publications

(6 citation statements)

References 22 publications

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“…Lüt Öcal et al [37] demonstrated that SII was independently associated with all-cause mortality and adverse cardiovascular events during hospitalization and in 3-year follow-up after PPCI in patients with acute ST-segment elevation infarction. Additionally,other speci c studies had already shown that the SII may indicate short-and long-term mortality in patients with heart failure (HF) reduced with ejection fraction (HFrEF) and acute type A aortic dissection (ATAD) patients undergoing surgery and infective endocarditis [38][39][40] . All those studies used a single SII level at baseline, and illustrated that higher SII level was interrelated with poorer prognosis.…”

Section: Discussionmentioning

confidence: 99%

The prognostic value of dynamic changes in SII for the patients with STEMI undergoing PPCI

Liu

Sun

Gao

et al. 2023

Preprint

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Objective To investigate the dynamic changes in systemic immune inflammatory index (SII) during the perioperative period of primary percutaneous coronary intervention (PPCI) and to evaluate its predictive value for in-hospital and out-of hospital outcomes in patients with ST-segment elevation myocardial infarction (STEMI). Methods This retrospective study included 324 consecutive STEMI patients in a cardiac care unit. Blood samples were collected 12h before, 12h after(T1), 24h after, 48h after PPCI, the last time before hospital discharge (T2), and 1 month after hospital discharge. SII was calculated as (neutrophils×platelets)/lymphocytes. Based on whether the primary endpoint occurred, we divided the research sample into two groups, which were the event and the non-event group. Univariable and multi-variable logistic regression analyses were performed to detect the independent risk factors that might influence the occurrence of the primary endpoint. Dynamic curves with SII were plotted and Receiver-operating characteristic(ROC) curves were drawn for each node to calculate the optimal critical value, sensitivity and specificity to assess their predictive ability for the occurrence of in-hospital and out-of hospital course. Kaplan-Meier curves were used to analyze the differences in survival rates at different status of SII inflammation levels; Results High levels of SII were individually related with the occurrence of in-hospital period and long-term outcomes during the post-operative follow-up of STEMI patients (in-hospital SII:T1:OR 1.001,95%CI 1.001–1.001,P < 0.001;SII following hospital discharge:T1M: OR 1.008,95%CI 1.006–1.010,P < 0.001). And patients with high SII levels had lower survival rates than those with lower SII levels. The analysis showed that SII 12h after(T1) and SII 1 month(T1M) had a excellent predictive value for the occurrence of in-hospital and out-of hospital outcomes, respectively(AUC:0.896, p < 0.001; AUC:0.892, p < 0.001). Conclusion There is a significant relationship between dynamic status of SII and the prognosis in STEMI patients. This study found that SII 12h and SII 1 month had an effect on in-hospital and out-of hospital outcomes, respectively. Consequently, we should focus on the dynamic change of SII.

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Section: Discussionmentioning

confidence: 99%

The prognostic value of dynamic changes in SII for the patients with STEMI undergoing PPCI

Liu

Sun

Gao

et al. 2023

Preprint

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“…Although an understanding of the pathological mechanism that influences the development of ATAAD has been developed, the molecular pathways underlying this disease have been difficult to identify. Previous studies have reported that the systemic immune-inflammation index is a reliable biomarker for predicting short-term outcomes in ATAAD patients undergoing surgery ( Xu et al, 2022 ), and could be used for ATAAD patient stratification and selection ( Samanidis et al, 2022 ). Inflammation may also be a risk factor leading to weakening of the aortic wall in ATAAD ( Del Porto et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of Immune-Related Gene Signature in Stanford Type A Aortic Dissection

Wang

et al. 2022

Front. Genet.

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Background: Stanford type A aortic dissection (ATAAD) is a common life-threatening event in the aorta. Recently, immune disorder has been linked to the risk factors that cause ATAAD at the molecular level. However, the specific immune-related gene signature during the progression is unclear.Methods: The GSE52093 and GSE98770 datasets related to ATAAD from the Gene Expression Omnibus (GEO) database were acquired. The immune gene expression levels were analyzed by single sample gene set enrichment analysis (ssGSEA). The correlations between gene networks and immune scores were determined by weighted gene correlation network analysis (WGCNA). The different immune subgroups were finally divided by consensus clustering. The differentially expressed genes (DEGs) were identified and subsequent functional enrichment analyses were conducted. The hub genes were identified by protein–protein interaction (PPI) network and functional similarities analyses. The immune cell infiltration proportion was determined by the CIBERSORT algorithm.Results: According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results. Sixty-eight immune-related DEGs (IRDEGs) between the two subgroups were enriched in inflammatory-immune response biological processes, including leukocyte cell–cell adhesion, mononuclear cell migration, and myeloid leukocyte migration. Among these IRDEGs, 8 genes (CXCR4, LYN, CCL19, CCL3L3, SELL, F11R, DPP4, and VAV3) were identified as hub genes that represented immune-related signatures in ATAAD after the PPI and functional similarities analyses. The proportions of infiltrating CD8 T cells and M1 macrophages were significantly higher in ATAAD patients in the immune-high group than the immune-low group.Conclusion: Eight immune-related genes were identified as hub genes representing potential biomarkers and therapeutic targets linked to the immune response in ATAAD patients.

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“…Xu et al reported that SII could predict postoperative short-term adverse outcomes in aTAAD patients after surgery. 18 Another retrospective study reported that SII is associated with in-hospital and mid-term survival in patients with type B aortic dissection undergoing thoracic endovascular repair. 17 Our study also identified similar results.…”

Section: Discussionmentioning

confidence: 99%

“…[14][15][16] Three recent studies reported that a high SII was associated with short-come mortality and other poor outcomes in aortic dissection. 10,17,18 However, there is no study attempt to build a survival prediction model based on SII combined with other clinical information. In our study, we aim to examine the prediction ability of SII in patients with aTAAD undergoing surgical repair and develop a simple prediction model base on SII and other clinical characteristics.…”

Section: Introductionmentioning

confidence: 99%

Short- and Mid-Term Survival Prediction in Patients with Acute Type A Aortic Dissection Undergoing Surgical Repair: Based on the Systemic Immune-Inflammation Index

Li¹,

Zhang²,

Baraghtha³

et al. 2022

JIR

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Purpose The postoperative survival of patients with acute type A aortic dissection (aTAAD) remains unsatisfactory. The current study developed an easy-to-use survival prediction model and calculator. Methods A total of 496 patients with aTAAD undergoing surgical repair were included in this study. The systemic immune-inflammation index (SII) and other clinical features were collected and subjected to logistic and Cox regression analyses. The survival prediction model was based on Cox regression analyses and exhibited as a nomogram. For convenience of use, the nomogram was further developed into calculator software. Results We demonstrated that a higher preoperative SII was associated with in-hospital death (OR: 4.116, p < 0.001) and a higher postoperative overall survival rate (HR: 2.467, p < 0.001) in aTAAD patients undergoing surgical repair. A survival prediction model and calculator based on SII and four other clinical features were developed. The overall C-index of the model was 0.743. The areas under the curves (AUCs) of the 1- and 3-month and 1- and 3-year survival probabilities were 0.73, 0.71, 0.71 and 0.72, respectively. The model also showed good calibration and clinical utility. Conclusion Preoperative SII is significantly associated with postoperative survival. Based on SII and other clinical features, we created the first easy-to-use prediction model and calculator for predicting the postoperative survival rate in aTAAD patients, which showed good prediction performance.

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Systemic immune‐inflammation index predicted short‐term outcomes in ATAD patients undergoing surgery

Cited by 13 publications

References 22 publications

The prognostic value of dynamic changes in SII for the patients with STEMI undergoing PPCI

The prognostic value of dynamic changes in SII for the patients with STEMI undergoing PPCI

Identification of Immune-Related Gene Signature in Stanford Type A Aortic Dissection

Short- and Mid-Term Survival Prediction in Patients with Acute Type A Aortic Dissection Undergoing Surgical Repair: Based on the Systemic Immune-Inflammation Index

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