Systemic Immune-Inflammation Index for Predicting the Prognosis of Critically Ill Patients with Acute Pancreatitis

Immune-Related Genes can Serve as Potential Biomarkers for Predicting Severe Acute Pancreatitis

We aimed to investigate immune-related candidate genes for predicting the severity of acute pancreatitis (AP). RNA sequencing profile GSE194331 was downloaded, and differentially expressed genes (DEGs) were investigated. Meanwhile, the infiltration of immune cells in AP were assessed using CIBERSORT. Genes related with the infiltration of immune cells were investigated using weighted gene co-expression network analysis (WGCNA). Furthermore, immune subtypes, micro-environment, and DEGs between immune subtypes were explored. Immune-related genes, protein-protein interaction (PPI) network, and functional enrichment analysis were further performed. Overall, 2533 DEGs between AP and healthy controls were obtained. After trend cluster analysis, 411 upregulated and 604 downregulated genes were identified. Genes involved in two modules were significantly positively related to neutrophils and negatively associated with T cells CD4 memory resting, with correlation coefficient more than 0.7. Then, 39 common immune-related genes were obtained, and 56 GO BP were enriched these genes, including inflammatory response, immune response, and innate immune response; 10 KEGG pathways were enriched, including cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, and IL-17 signaling pathway. Genes, including S100A12, MMP9, IL18, S100A8, HCK, S100A9, RETN, OSM, FGR, CAMP, were selected as genes with top 10 degree in PPI, and the expression levels of these genes increased gradually in subjects of healthy, mild, moderately severe, and severe AP. Our findings indicate a central role of immune-related genes in predicting the severity of AP, and the hub genes involved in PPI represent logical candidates for further study.

Associations of systemic inflammation markers with myocardial enzymes in pediatric adenotonsillar hypertrophy: A cross-sectional study

Objective

The present study aimed to investigate the relationship between systemic inflammation markers and myocardial enzymes in children with adenotonsillar hypertrophy (ATH).

Methods

The levels of myocardial enzymes were detected and the systemic inflammatory biomarkers including neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and systemic immune inflammation index (SII) were calculated. Regression analyses were performed and a prediction model for screening myocardial injury was established by receiver operating characteristic (ROC) curve.

Results

Finally, a total of 804 children with ATH were included. After adjusting for age, BMI, fasting blood glucose and lipid profiles, both NLR and SII were significantly associated with CK-MB (p = 0.041 and 0.034, respectively) and LDH (p = 0.002 and 0.001, respectively), and PLR was associated with CK-MB (p = 0.008). In addition, NLR, SII were independently associated with hyper-LDH [OR = 1.447, 95%CI (1.063, 1.968); OR = 1.001, 95%CI (1.000, 1.002), respectively] and the associations were more significant in girls. A prediction model for hyper-LDH based on SII was developed with the area under the ROC curve of 0.715 (0.682, 0.746).

Conclusion

Systemic inflammation markers were only independently associated with serum hyper-LDH in children with ATH, especially in girls. Further investigation was needed to determine the relationship between systemic inflammation with myocardial enzymes in ATH children.

Systemic immune-inflammation index may predict the acute kidney injury and prognosis in patients with spontaneous cerebral hemorrhage undergoing craniotomy: a single-center retrospective study

BACKGROUND

The systemic immune-inflammation index (SII) is an emerging prognostic marker of cancer. We aimed to explore the predictive ability of the SII on acute kidney injury (AKI) and prognosis in patients with spontaneous cerebral hemorrhage (SCH) who underwent craniotomy.

METHODS

Patients with SCH who underwent craniotomy between 2014 and 2021 were enrolled in this study. The epidemiology and predictive factors for AKI after SCH were analyzed. The prognostic factors for clinical outcomes in patients with SCH and AKI were further investigated. The prognostic factors were then analyzed using a logistic regression model and a receiver operating characteristic curve.

RESULTS

In total, 305 patients were enrolled in this study. Of these, 129 (42.3%) patients presented with AKI, and 176 (57.7%) patients were unremarkable. The SII (odds ratio [OR], 1.261; 95% confidence interval [CI], 1.036-1.553; P = 0.020) values and serum uric acid levels (OR, 1.004; 95% CI, 1.001-1.007; P = 0.005) were significant predictors of AKI after SCH craniotomy. The SII cutoff value was 1794.43 (area under the curve [AUC], 0.669; 95% CI, 0.608-0.730; P < 0.001; sensitivity, 65.9%; specificity, 65.1%). Of the patients with AKI, 95 and 34 achieved poor and good outcomes, respectively. SII values (OR, 2.667; 95% CI, 1.167-6.095; P = 0.020), systemic inflammation response index values (OR, 1.529; 95% CI, 1.064-2.198; P = 0.022), and Glasgow Coma Scale (GCS) scores on admission (OR, 0.593; 95% CI, 0.437-0.805; P = 0.001) were significant in the multivariate logistic regression analysis. The cutoff SII value was 2053.51 (AUC, 0.886; 95% CI, 0.827-0.946; P < 0.001; sensitivity, 78.9%; specificity, 88.2%).

CONCLUSIONS

The SII may predict AKI in patients with SCH who underwent craniotomy and may also predict the short-term prognosis of these patients.

Proteome-Wide Mendelian Randomization Identifies Causal Links Between Blood Proteins and Acute Pancreatitis

BACKGROUND & AIMS

Acute pancreatitis (AP) is a complex disease and the leading cause of gastrointestinal disease-related hospital admissions. Few therapeutic options exist for AP prevention. Blood proteins with causal evidence may represent promising drug targets, but few have been causally linked with AP. Our objective was to identify blood proteins linked with AP by combining genome-wide association meta-analysis and proteome-wide Mendelian randomization (MR) studies.

METHODS

We performed a genome-wide association meta-analysis totalling 10,630 patients with AP and 844,679 controls and a series of inverse-variance weighted MR analyses using cis-acting variants on 4719 blood proteins from the deCODE study (n = 35,559) and 4979 blood proteins from the Fenland study (n = 10,708).

RESULTS

The meta-analysis identified genome-wide significant variants (P <5 × 10^-8) at 5 loci (ABCG5/8, TWIST2, SPINK1, PRSS2 and MORC4). The proteome-wide MR analyses identified 68 unique blood proteins that may causally be associated with AP, including 29 proteins validated in both data sets. Functional annotation of these proteins confirmed expression of many proteins in metabolic tissues responsible for digestion and energy metabolism, such as the esophagus, adipose tissue, and liver as well as acinar cells of the pancreas. Genetic colocalization and investigations into the druggable genome also identified potential drug targets for AP.

CONCLUSIONS

This large genome-wide association study meta-analysis for AP identified new variants linked with AP as well as several blood proteins that may be causally associated with AP. This study provides new information on the genetic architecture of this disease and identified pathways related to AP, which may be further explored as possible therapeutic targets for AP.

Blood cell ratios in mood and cognitive outcomes following electroconvulsive therapy

Systemic inflammation is commonly reported in depression, with dysregulation of both the innate and adaptive arms of the immune system documented. Obtaining ratios of neutrophils, platelets, and monocytes to counts of lymphocytes (NLR, PLR, MLR, respectively) represents a low-cost and easily reproducible measure of an individual's inflammatory burden that can be calculated effortlessly from routine clinical full white blood cell counts. Electroconvulsive therapy (ECT) remains the most effective acute antidepressant treatment for depression but is often limited by its cognitive side-effects. Here, we examined differences in blood cell ratios in subgroups of depressed patients (unipolar/bipolar, psychotic/non-psychotic, early-onset/late-onset) and ECT-related subgroups (responder/non-responder, remitter/non-remitter). We also explored the relationships between blood cell ratios and depression severity and immediate cognitive outcomes post-ECT. Our results show baseline NLR was raised in patients with psychotic depression. In the entire group of patients, significant negative correlations were noted between the PLR and SII and baseline HAM-D24 score, signifying that lower systemic inflammation is associated with more severe depressive symptoms. Significant positive correlations were noted between various blood cell ratios and mean time to recovery of orientation in the entire group of patients and in depression subgroups, indicating that increased peripheral inflammation is linked to worse cognitive outcomes post-ECT. Overall, our results suggest that assessment of blood cell ratios could be useful for predicting mood changes in patients at risk of developing depressive episodes or relapse following successful treatment or for identifying those at risk for cognitive side-effects following ECT.

Systemic Inflammation Response Index and Systemic Immune Inflammation Index Are Associated with Clinical Outcomes in Patients with Acute Pancreatitis?

OBJECTIVES

The inflammatory response is critically important in acute pancreatitis (AP). Systemic immune-inflammation (SII) index and systemic inflammation response index (SIRI), which are novel inflammatory markers, have been linked to determining outcomes in various diseases. The goal of the current study was to examine the relation of the SII index and SIRI with disease severity and acute kidney injury (AKI) in subjects with AP.

METHODS

A total of 332 subjects with AP were analyzed retrospectively. SII index was calculated using the formula; platelet (P)×neutrophil (N)/lymphocyte (L), while SIRI was calculated as N × monocyte (M)/L count. Multivariate regression (MR) was done to determine the independent risk factors for AKI and severe AP (SAP).

RESULTS

Statistical analyses showed that both median SII index and median SIRI increased gradually with higher AP severity (p < 0.001). Both SII index and SIRI were higher in subjects with AKI compared to controls (p < 0.001). Using MR analysis, the SII index was found to independently predict both SAP (OR = 1.004, 95% CI: 1.001-1.008, p = 0.018) and AKI (OR = 1.005, 95% CI: 1.003-1.008, p < 0.001). ROC analysis showed that the SII index could accurately differentiate SAP (AUC = 0.809, p < 0.001) and AKI (AUC = 0.820, p = 0.001) in patients with acute pancreatitis. ROC analysis also showed that SIRI could also accurately differentiate SAP (0.782, p < 0.001) and AKI (AUC = 0.776, p = 0.001).

CONCLUSIONS

SIRI and the SII indexes can be used as potential biomarkers in predicting both disease severity and AKI development in subjects with AP.

Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals

Background

Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. This study aims to explore different ML models for early identification of severe acute pancreatitis (SAP) among patients hospitalized for acute pancreatitis.

Methods

This retrospective study enrolled patients with acute pancreatitis (AP) from multiple centers. Data from the First Affiliated Hospital and Changshu No. 1 Hospital of Soochow University were adopted for training and internal validation, and data from the Second Affiliated Hospital of Soochow University were adopted for external validation from January 2017 to December 2021. The diagnosis of AP and SAP was based on the 2012 revised Atlanta classification of acute pancreatitis. Models were built using traditional logistic regression (LR) and automated machine learning (AutoML) analysis with five types of algorithms. The performance of models was evaluated by the receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) based on LR and feature importance, SHapley Additive exPlanation (SHAP) Plot, and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML.

Results

A total of 1,012 patients were included in this study to develop the AutoML models in the training/validation dataset. An independent dataset of 212 patients was used to test the models. The model developed by the gradient boost machine (GBM) outperformed other models with an area under the ROC curve (AUC) of 0.937 in the validation set and an AUC of 0.945 in the test set. Furthermore, the GBM model achieved the highest sensitivity value of 0.583 among these AutoML models. The model developed by eXtreme Gradient Boosting (XGBoost) achieved the highest specificity value of 0.980 and the highest accuracy of 0.958 in the test set.

Conclusions

The AutoML model based on the GBM algorithm for early prediction of SAP showed evident clinical practicability.

Utility of the systemic immune-inflammation index to predict serious bacterial infections in infants with fever without a source

INTRODUCTION

This study analyzed the utility of the systemic immune-inflammation index (SII) in predicting serious bacterial infections (SBIs) in infants with fever without a source (FWS).

METHODS

Infants (aged 1-4 months) evaluated in the pediatric emergency department for FWS were divided into two groups: with SBI and without SBI. The efficacy of inflammatory markers in predicting SBI was compared.

RESULTS

The study included 223 infants with a mean age of 76.65 ± 25.42 days; 62 (27.8%) of them were included in the SBI group, and all of them were diagnosed with a urinary tract infection (UTI). The hospitalization rate and length of hospital stay were significantly higher in UTI patients (p < 0.001 for each). The mean SII was 795.76 ± 475.85 in the SBI group and 318.24 ± 300.70 in the non-SBI group, and there was a significant difference between the groups (p < 0.001). In diagnosis of SBI, the area under the curve values were found to be 0.89 [95% confidence interval (CI): 0.85-0.94] for C-reactive protein (CRP), 0.86 (95% CI: 0.81-0.91) for absolute neutrophil count (ANC), 0.84 (95% CI: 0.78-0.89) for the SII, and 0.81 (95% CI: 0.74-0.87) for WBC. In the multivariate logistic regression analysis, high CRP and SII values were found to be predictive factors for UTI without bacteremia (p < 0.001 and p = 0.008, respectively).

CONCLUSION

We found that high CRP and SII values could be predictive for UTI without bacteremia in infants with FWS. The SII may be preferred because it can be easily calculated using the hemogram results, is not accompanied by extra costs, and does not require further blood collection.

Association of Beta-2-Microglobulin With Coronary Heart Disease and All-Cause Mortality in the United States General Population

Few prospective studies explored the association of beta-2-microglobulin (B2M) with coronary heart disease (CHD) mortality. The primary objective of this study was to examine the association of serum B2M with CHD and all-cause mortality. This is a prospective cohort study of a nationally representative sample of 4,885 adults, aged 40–85 years, who participated in the National Health and Nutrition Examination Survey (NHANES III) from 1988 to 1994. The relationships between B2M and CHD and all-cause mortality were estimated using Cox proportional hazards regression models. During a median follow-up of 15.5 years, 845 CHD and 3,388 all-cause deaths occurred among 4,885 participants [2,568 women (55.7%); mean (S.D.) age, 66.4 (12.5) years], respectively. In the unadjusted model, B2M concentration was strongly linearly associated with CHD and all-cause mortality (p-trend < 0.001). After adjusting multivariable factors, a positive linear association between B2M and all-cause mortality was still observed (H.R. for Q4 vs. Q1 5.90; 95% CI: 5.31–6.57; p-trend < 0.001). In the multivariable adjustment model, B2M was significantly associated with an increased risk of CHD mortality (H.R. for Q4 vs. Q1 2.72; 95% CI: 2.07–3.57; p-trend < 0.001). In the stratified analyses, the associations of B2M with CHD and all-cause mortality varied by risk factors, such as age, smoking status, and history of hypertension. The findings suggest a significant relationship between the higher serum B2M concentration and increased risk for CHD and all-cause mortality. Further large-scale follow-up studies are also needed to validate this association.