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

A novel nomogram model to predict in-hospital mortality in patients with acute type A aortic dissection after surgery

BACKGROUND

Acute type A aortic dissection is a dangerous disease that threatens public health. In recent years, with the progress of medical technology, the mortality rate of patients after surgery has been gradually reduced, leading that previous prediction models may not be suitable for nowadays. Therefore, the present study aims to find new independent risk factors for predicting in-hospital mortality and construct a nomogram prediction model.

METHODS

The clinical data of 341 consecutive patients in our center from 2019 to 2023 were collected, and they were divided into two groups according to the death during hospitalization. The independent risk factors were analyzed by univariate and multivariate logistic regression, and the nomogram was constructed and verified based on these factors.

RESULTS

age, preoperative lower limb ischemia, preoperative activated partial thromboplastin time (APTT), preoperative platelet count, Cardiopulmonary bypass (CPB) time and postoperative acute kidney injury (AKI) independently predicted in-hospital mortality of patients with acute type A aortic dissection after surgery. The area under the receiver operating characteristic curve (AUC) for the nomogram was 0.844. The calibration curve and decision curve analysis verified that the model had good quality.

CONCLUSION

The new nomogram model has a good ability to predict the in-hospital mortality of patients with acute type A aortic dissection after surgery.

Inflammation in aortic surgery: postoperative evolution of biomarkers according pathologies and segments of the aorta

OBJECTIVES

Aortic pathologies often present with elevated inflammatory biomarkers due to the nature of the disease. Open aortic surgery causes significant trauma to the body due to often mandatory ischemic periods, long cardiopulmonary bypass times and polytransfusion. We aim to determine postoperative trends on inflammation biomarkers for different aortic pathologies and type of surgery in different segments of the aorta.

METHODS

Retrospective review of prospectively collected data of 193 consecutive patients who underwent aortic surgery in our centre between 2017 and 2021, grouped according to the type of aortic intervention: (1) Type A aortic dissection (AD) repair with ascending aorta/hemiarch replacement, (2) Aortic root replacement (ARR), (3) Aortic arch + Frozen elephant trunk (FET), (4) Descending thoracic aorta (DTA)/Thoraco-Abdominal aortic repair (TAA). Primary outcomes were daily values of white blood cells (WBC) and C-Reactive Protein (CRP) during the first 15 postoperative days.

RESULTS

All groups had a similar inflammatory peak in the first 2-4 days (WBC 12-15 × 109 c/L). AD and FET groups show similar trends with WBC and CRP peaks on days 2 and 10. The ARR group didn't experience the 2nd peak as most patients were already discharged. DTA/TAA patients experienced a more prolonged inflammatory response, reaching a plateau by day 5-10. AD group shows the highest WBC levels and the DTA/TAAA group the highest CRP levels. CRP levels remain elevated (100-200 mg/L) in all groups after 15 postoperative days.

CONCLUSIONS

Inflammatory biomarkers show different postoperative trends depending on the clinical presentation and complexity of the aortic procedure performed. Further understanding of the inflammatory response to different aortic pathologies and surgical procedures will permit reduction on the liberal use of antibiotics that this cohort of patients are usually exposed to. An earlier version of the data included in this manuscript was presented as Oral Abstract in the UK Society of Cardiothoracic Surgery Annual meeting in 2021.

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

BACKGROUND

Predicting the prognosis of primary percutaneous coronary intervention(PPCI) in ST-segment elevation myocardial infarction (STEMI) patients in the perioperative period is of great clinical significance. The inflammatory response during the perioperative period is also an important factor. This study aimed to investigate the dynamic changes in the systemic immune inflammatory index (SII) during the perioperative period of PPCI and evaluate its predictive value for in-hospital and out-of-hospital outcomes in patients with STEMI.

METHODS

This retrospective study included 324 consecutive patients with STEMI who were admitted to the cardiac care unit. Blood samples were collected before PPCI, 12 h (T1), 24 h, 48 h after PPCI, the last time before hospital discharge (T2), and 1 month after hospital discharge. The SII was calculated as (neutrophils×platelets)/lymphocytes. Based on whether the primary endpoint occurred, we divided the patients into event and non-event groups. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors that might influence the occurrence of the primary endpoint. Dynamic curves of 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 in-hospital and out-of-hospital courses. Kaplan-Meier curves were used to analyze the differences in survival rates at different SII inflammation levels.

RESULTS

High levels of SII were individually related to the occurrence of the 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). Patients with high SII levels had lower survival rates than those with low SII levels. The analysis showed that the SII 12 h after (T1) and SII 1 month (T1M) had excellent predictive values 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 the dynamic status of SII and prognosis in patients with STEMI. This study found that the 12 h and SII 1 month affected in-hospital and out-of-hospital outcomes, respectively. Consequently, we focused on the dynamic changes in the SII.

A neural network model was constructed by screening the potential biomarkers of aortic dissection based on genes associated with pyroptosis

BACKGROUND

Aortic dissection (AD) is one of the crucial and common cardiovascular diseases, and pyroptosis is a novel cell delivery mechanism that is probably involved in the pathogenesis of various cardiovascular diseases. However, no study has investigated the role of pyroptosis in AD.

METHODS

We obtained two AD datasets, GSE153434 and GSE190635, from the Gene Expression Omnibus database. The differential expression of AD-related genes was determined by differential analysis, and their enrichment analysis was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Additionally, a protein-protein interaction network was established. Next, potential biomarkers were screened by Lasso regression analysis, and a neural network model was constructed. Finally, the potential biomarkers were validated by constructing a mouse model of AD.

RESULTS

A total of 1033 differentially expressed related genes were distinguished and these genes were mainly associated with the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) and mitogen-activated protein kinase signaling pathways. The Lasso regression results showed five potential biomarkers, namely platelet endothelial cell adhesion molecule-1 (PECAM1), caspase 4 (CASP4), mixed lineage kinase domain-like pseudokinase (MLKL), APAF1-interacting protein (APIP), and histone deacetylase 6 (HDAC6) and successfully constructed a neural network model to predict AD occurrence. The results showed that CASP4 and MLKL were highly expressed, whereas PECAM1 and HDAC6 were lowly expressed in AD samples, and no statistically significant difference was observed in APIP expression in AD samples.

CONCLUSION

Pyroptosis plays a crucial role in AD occurrence and development. Moreover, the five potential biomarkers identified in the present study can act as targets for the early diagnosis of AD in patients.

Using complete blood count-derived inflammatory markers to compare postoperative inflammation in dogs undergoing open or laparoscopic ovariectomy

BACKGROUND

The inflammatory response triggered in dogs after laparoscopic ovariectomy (LapOV) or ovariectomy through mini-celiotomy (COV) has never been compared using the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic inflammatory index (SII).

METHODS

Bitches underwent LapOV (n = 25) or COV (n = 25). NLR, PLR and SII were calculated 1 hour before ovariectomy (T0) and 2-3, 6-8 and 20-24 hours (T1-T3) after surgery.

RESULTS

Surgical time was longer in the LapOV group. Changes over time were observed in the NLR, PLR and SII in both groups (p < 0.001). PLR at baseline and T1 (p = 0.03 and 0.01) and NLR, PLR and SII at T2 (p = 0.01, 0.01 and 0.009) were higher in the LapOV group than in the COV group, but they did not differ at T3.

LIMITATIONS

The overrepresentation of Greyhounds in the LapOV group and the short-term follow-up are the study's main limitations.

CONCLUSION

Although an inflammatory peak was observed 6-8 hours after COV or LapOV, it was higher after the laparoscopy. However, there was no difference in the bitches' inflammatory status 24 hours after surgery.

Is the First-Trimester Systemic Immune-Inflammation Index Associated With Preeclampsia?

OBJECTIVE

Preeclampsia (PE) is a serious and common pregnancy issue. There is a systemic inflammation in PE and it is accompanied by increased oxidative stress, but the clear etiology has not been revealed. We aimed to predict PE with the systemic immune-inflammation index (SII) value calculated in the first trimester.

MATERIAL AND METHODS

 This is a retrospective study. One hundred fifty-seven pregnant women were included in the study. Twenty-seven pregnant women were excluded from the study. Age, gravida, parity, and hemogram values were recorded in the patients' first visit file records. The time and mode of delivery, birth weight, and APGAR scores were obtained from the file records of the patients. SII was created using the formula (neutrophil x platelet/lymphocyte).  Result: The study group included 30 pregnant women who had been diagnosed with PE. The control group consisted of the remaining 100 pregnant women. There was a statistically significant difference between PE and control groups in terms of SII (p=0.03). The SII level cut-off value for predicting PE was determined to be 836.83. This value's area was found to be 0.635 (0.519-0.752). Furthermore, the selectivity is 0.60 and the sensitivity is 0.40 for these values.  Conclusion: SII was found to be significantly higher in people with PE in the study. We showed that the SII value measured in the first trimester can be used to predict PE. It might make sense to combine this marker with the patient's history and other risk factors due to its low selectivity and sensitivity.

Acute aortic syndromes: An internist's guide to the galaxy

Acute aortic syndromes (AASs) are severe conditions defined by dissection, hemorrhage, ulceration or rupture of the thoracic aorta. AASs share etiological and pathophysiological features, including long-term aortic tissue degeneration and mechanisms of acute aortic damage. The clinical signs and symptoms of AASs are unspecific and heterogeneous, requiring large differential diagnosis. When evaluating a patient with AAS-compatible symptoms, physicians need to integrate clinical probability assessment, bedside imaging techniques such as point-of-care ultrasound, and blood test results such as d-dimer. The natural history of AASs is dominated by engagement of ischemic, coagulative and inflammatory pathways at large, causing multiorgan damage. Medical treatment, multiorgan monitoring and outcome prognostication are therefore paramount, with internal medicine playing a key role in non-surgical management of AASs.

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

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.

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

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.

Association of red blood cell distribution width, systemic-immune-inflammation index and poor cardiovascular outcomes in patients with newly diagnosed hypertension

BACKGROUND

Red cell distribution width (RDW) and the systemic immune-inflammation index (SII) have been extensively studied as predictors of morbidity and mortality in several cardiovascular diseases. This prospective study aimed to investigate the relationship between long term major adverse cardiac events (MACEs) and simple hematological parameters in hypertensive patients.

METHODS

The study included a total of 1202 patients with newly diagnosed HT. Of the patients, 662 (55.1%) were female and 540 (44.9%) were male, with a mean age of 53.0 ± 11.4 years. The primary endpoint of the study was long term MACE, including cardiac death, stroke, and myocardial infarction. This is the first study focusing on the association of SII with major adverse cardiovascular outcomes in patients with HT.

RESULTS

Eighty-nine patients (8.7%) developed at least one MACE during a mean follow-up period of 82.2 ± 1.3 months. RDW (13.0 ± 0.9 vs. 13.5 ± 1.2%, p < .001) and SII [465.0 (353.4-609.4) vs. 584.4 (468.9-794.0) x10³/µL, p < .001] were significantly higher in patients with MACEs. The prevalence of MACEs was significantly higher in patients with RDW>13.1% (10.4 vs. 5%; p < .001) and in patients with SII>465 x10³/µL (11.8 vs. 3.1%; p < .001). The multivariate logistic regression analysis showed SII and RDW were independent predictors of MACEs.

CONCLUSION

The results of the study demonstrated that the RDW and SII were independent predictors of long-term cardiovascular events in hypertensive patients. These simple hematological parameters may be used as prognosticators of MACE in patients with newly diagnosed HT.

Does systemic immune-inflammation index predict the short outcomes after an acute type A aortic dissection repair? Promising biomarker for acute aortic syndrome

The retrospectively presented by Xu et al. assessed the value of the systemic immune-inflammation index (SII) in the prediction of short-term outcomes in patients undergoing surgery for acute type A aortic dissection (ATAAD). The authors concluded that SII is a reliable biomarker that can predict postoperative short-term outcomes and this marker could be potentially applied to stratification and patient selection with ATAAD. Although the study is retrospective, it is well-matched and conducted in a large volume center and the surgical technique was standard for all procedures. In addition, there was no statistical difference in cardiopulmonary bypass, aortic cross-clamp and deep hypothermic time, comorbidities, blood and blood products transfusion between the study groups. In addition, 90.7% of patients underwent elephant trunk technique for ATAAD repair, while the 30-day mortality and postoperative temporary and permanent neurological dysfunctions were 14.8% and 11.1%, respectively. Randomized controlled and prospective studies are warranted to clarify these well-documented results to apply this useful biomarker in clinical practice for patients with the acute aortic syndrome.