Infectious Complications of Acute Pancreatitis Is Associated with Peripheral Blood Phagocyte Functional Exhaustion

Impact of Sepsis Onset Timing on All-Cause Mortality in Acute Pancreatitis: A Multicenter Retrospective Cohort Study

BackgroundSepsis complicates acute pancreatitis (AP), increasing mortality risk. Few studies have examined how sepsis and its onset timing affect mortality in AP. This study evaluates the association between sepsis occurrence and all-cause mortality in AP, focusing specifically on the impact of sepsis onset timing.MethodsThis multicenter retrospective cohort study included 494 ICU-admitted AP patients from the MIMIC-IV database and 91 from our center. Patients were grouped by sepsis occurrence and onset timing. Clinical outcomes were in-hospital and 90-day all-cause mortality. Machine learning identified key variables associated with mortality. Multivariable regression analyzed the impact of sepsis and its onset timing on mortality. To reduce baseline differences, propensity score matching (PSM) based on time to sepsis was conducted. After PSM, Kaplan-Meier survival analyses incorporated data from our center for validation. Restricted cubic spline analysis examined any nonlinear relationship between sepsis onset timing and mortality.ResultsPatients with sepsis had significantly higher in-hospital and 90-day mortality rates than those without sepsis (p < 0.05). Sepsis was identified as a significant risk factor for in-hospital mortality and remained significantly associated after adjusting for key variables (p < 0.05). However, sepsis onset timing did not significantly impact in-hospital or 90-day mortality. These findings were validated after PSM and with our center's data. No nonlinear relationship between sepsis onset timing and mortality was found.ConclusionSepsis significantly increases all-cause mortality in AP patients, but the timing of its onset has limited impact. Continuous monitoring and intervention for sepsis during hospitalization are recommended to improve prognosis.

Identification and mechanistic analysis of shared biomarkers and pathogenesis in acute pancreatitis and sepsis based on differential gene expression and protein interaction networks

Acute pancreatitis (AP) is a common gastrointestinal inflammatory disease that requires hospitalization, with 40-70% of patients in moderate to severe stages potentially developing sepsis, which is closely related to high mortality rates and poor prognosis. Therefore, early identification of AP patients at risk of developing sepsis is crucial for reducing mortality. This study aims to identify core genes associated with sepsis to provide new core genes for early warning and management of patients with acute pancreatitis. The study utilized the GSE54514, GSE57065, GSE95233, and GSE194331 datasets for analysis, employing weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network construction. Six core genes were identified using two machine learning methods and validated with the GSE3644 and GSE28750 datasets. The analysis revealed that the identified core genes (NDUFA1, COX7A2, COX7B, UQCRQ, SNRPG, and NDUFA4) are related to the oxidative phosphorylation (OxPhos) pathway, and significant differences were observed in the immune cell composition between AP and sepsis patients. SNRPG may play a role in the progression from AP to sepsis by regulating NDUFA4, linking it to cellular metabolism and redox balance. The newly identified core genes and their associated molecular mechanisms provide important clinical insights into the progression of acute pancreatitis to sepsis, potentially offering new research directions for future therapeutic strategies. Clinical trial number: This study was approved by the Ethics Committee of (Municipal Hospital affiliated to Taizhou University), in accordance with the Declaration of Helsinki. Approval number: LWSL202400220.

Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the MIMIC database

OBJECTIVE

This study developed and validated a stacked ensemble machine learning model to predict the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis.

DESIGN

A retrospective study based on patient data from public databases.

PARTICIPANTS

This study analysed 1295 patients with acute pancreatitis complicated by septicaemia from the US Intensive Care Database.

METHODS

From the MIMIC database, data of patients with acute pancreatitis and sepsis were obtained to construct machine learning models, which were internally and externally validated. The Boruta algorithm was used to select variables. Then, eight machine learning algorithms were used to construct prediction models for acute kidney injury (AKI) occurrence in intensive care unit (ICU) patients. A new stacked ensemble model was developed using the Stacking ensemble method. Model evaluation was performed using area under the receiver operating characteristic curve (AUC), precision-recall (PR) curve, accuracy, recall and F1 score. The Shapley additive explanation (SHAP) method was used to explain the models.

MAIN OUTCOME MEASURES

AKI in patients with acute pancreatitis complicated by sepsis.

RESULTS

The final study included 1295 patients with acute pancreatitis complicated by sepsis, among whom 893 cases (68.9%) developed acute kidney injury. We established eight base models, including Logit, SVM, CatBoost, RF, XGBoost, LightGBM, AdaBoost and MLP, as well as a stacked ensemble model called Multimodel. Among all models, Multimodel had an AUC value of 0.853 (95% CI: 0.792 to 0.896) in the internal validation dataset and 0.802 (95% CI: 0.732 to 0.861) in the external validation dataset. This model demonstrated the best predictive performance in terms of discrimination and clinical application.

CONCLUSION

The stack ensemble model developed by us achieved AUC values of 0.853 and 0.802 in internal and external validation cohorts respectively and also demonstrated excellent performance in other metrics. It serves as a reliable tool for predicting AKI in patients with acute pancreatitis complicated by sepsis.

Clinical effects of phospholipase D2 in attenuating acute pancreatitis

BACKGROUND

The objective of the current study was to elucidate the clinical mechanism through which phospholipase D2 (PLD2) exerted a regulatory effect on neutrophil migration, thereby alleviating the progression of acute pancreatitis.

AIM

To elucidate the clinical mechanism through which PLD2 exerted a regulatory effect on neutrophil migration, thereby alleviating the progression of acute pancreatitis.

METHODS

The study involved 90 patients diagnosed with acute pancreatitis, admitted to our hospital between March 2020 and November 2022. A retrospective analysis was conducted, categorizing patients based on Ranson score severity into mild (n = 25), moderate (n = 30), and severe (n = 35) groups. Relevant data was collected for each group. Western blot analysis assessed PLD2 protein expression in patient serum. Real-time reverse transcription polymerase chain reaction was used to evaluate the mRNA expression of chemokine receptors associated with neutrophil migration. Serum levels of inflammatory factors in patients were detected using enzyme-linked immunosorbent assay. Transwell migration tests were conducted to compare migration of neutrophils across groups and analyze the influence of PLD2 on neutrophil migration.

RESULTS

Overall data analysis did not find significant differences between patient groups (P > 0.05). The expression of PLD2 protein in the severe group was lower than that in the moderate and mild groups (P < 0.05). The expression level of PLD2 in the moderate group was also lower than that in the mild group (P < 0.05). The severity of acute pancreatitis is negatively correlated with PLD2 expression (r = -0.75, P = 0.002). The mRNA levels of C-X-C chemokine receptor type 1, C-X-C chemokine receptor type 2, C-C chemokine receptor type 2, and C-C chemokine receptor type 5 in the severe group are significantly higher than those in the moderate and mild groups (P < 0.05), and the expression levels in the moderate group are also higher than those in the mild group (P < 0.05). The levels of C-reactive protein, tumor necrosis factor-α, interleukin-1β, and interleukin-6 in the severe group were higher than those in the moderate and mild groups (P < 0.05), and the levels in the moderate group were also higher than those in the mild group (P < 0.05). The number of migrating neutrophils in the severe group was higher than that in the moderate and mild groups (P < 0.05), and the moderate group was also higher than the mild group (P < 0.05). In addition, the number of migrating neutrophils in the mild group combined with PLD2 inhibitor was higher than that in the mild group (P < 0.05), and the number of migrating neutrophils in the moderate group combined with PLD2 inhibitor was higher than that in the moderate group (P < 0.05). The number of migrating neutrophils in the severe group + PLD2 inhibitor group was significantly higher than that in the severe group (P < 0.05), indicating that PLD2 inhibitors significantly stimulated neutrophil migration.

CONCLUSION

PLD2 exerted a crucial regulatory role in the pathological progression of acute pancreatitis. Its protein expression varied among patients based on the severity of the disease, and a negative correlation existed between PLD2 expression and disease severity. Additionally, PLD2 appeared to impede acute pancreatitis progression by limiting neutrophil migration.

Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms

Background

Septic shock is a severe complication of acute pancreatitis (AP), often associated with poor prognosis. This study aims to analyze the clinical characteristics of patients with acute pancreatitis and develop an interpretable early prediction model for septic shock in these patients using machine learning (ML). The model is intended to assist emergency physicians in resource allocation and medical decision making.

Methods

Data were collected from the MIMIC-IV 3.0 database. The dataset was divided into a training set and a test set in a 7:3 ratio. Feature selection was performed using LASSO (Least Absolute Shrinkage and Selection Operator) regression. Subsequently, 10 ML models were developed: Random Forest, Logistic Regression, Gradient Boosting Machine, Neural Network, Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor, Adaptive Boosting, Light Gradient Boosting Machine, Category Boosting, and Support Vector Machine. To enhance and optimize model interpretability, Shapley Additive Explanations (SHAP) were employed.

Results

A total of 1032 patients with AP were included in this study, from which 31 variables were selected for model development. By comparing the area under the receiver operating characteristic curve and decision curve analysis results between the training and test sets, the XGBoost model demonstrated a significant advantage over other models. SHAP analysis revealed that white blood cell count, total bilirubin (bilirubin total), and bicarbonate (HCO3 -) levels were the three most critical risk factors for the development of septic shock in patients with AP.

Conclusion

ML approaches exhibited promising performance in predicting septic shock in patients with AP. These models may aid in guiding treatment decisions for patients with AP in the emergency department.

Reactive oxygen species and oxidative stress in acute pancreatitis: Pathogenesis and new therapeutic interventions

Acute pancreatitis (AP) is a common acute gastrointestinal disorder affecting approximately 20% of patients with systemic inflammatory responses that may cause pancreatic and peripancreatic fat necrosis. This condition often progresses to multiple organ failure, significantly increasing morbidity and mortality. Oxidative stress, characterized by an imbalance between the body's reactive oxygen species (ROS) and antioxidants, activates the inflammatory signaling pathways. Although the pathogenesis of AP is not fully understood, ROS are increasingly recognized as critical in the disease's progression and development. Modulating the oxidative stress pathway has shown efficacy in mitigating the progression of AP. Despite numerous basic studies examining this pathway, comprehensive reviews of recent research remain sparse. This systematic review offers an in-depth examination of the critical role of oxidative stress in the pathogenesis and progression of AP and evaluates the therapeutic potential of antioxidant interventions in its management.

Machine Learning Predictive Model for Septic Shock in Acute Pancreatitis with Sepsis

OBJECTIVE

Acute pancreatitis (AP) progresses to septic shock can be fatal. Early identification of high-risk patients and timely intervention can prevent and interrupt septic shock. By analyzing the clinical characteristics of AP with sepsis, this study uses machine learning (ML) to build a model for early prediction of septic shock within 28 days of admission, which guided emergency physicians in resource allocation and medical decision-making.

METHODS

This retrospective cohort study collected data from the emergency departments (EDs) of three tertiary care hospitals in China. The dataset was randomly divided into a training dataset (70%) and a testing dataset (30%). Ten ML classifiers were utilized to analyze characteristics of AP with sepsis in the training dataset upon admission. Results were evaluated through cross-validation analysis. The optimal model was then tested on the testing dataset without any parameter modifications. The ML model was evaluated using the receiver operating characteristic curve (ROC) and compared to scoring systems through the DeLong test.

RESULTS

A total of 604 AP patients with sepsis were included in this study. The auto-encoder (AE) model based on mean normalization, Pearson correlation coefficient (PCC), and recursive feature elimination (RFE) selection, achieved the highest Area Under the Curve (AUC) on the validation dataset (AUC 0.900, accuracy 0.868), with the AUC of 0.879 and accuracy of 0.790 on the testing dataset. Compared to the Sequential Organ Failure Assessment (AUC 0.741), quick Sequential Organ Failure Assessment (AUC 0.727), Acute Physiology and Chronic Health Evaluation II (AUC 0.778), and Bedside Index of Severity in Acute Pancreatitis (AUC 0.691), the AE model showed superior performance.

CONCLUSION

The AE model outperforms traditional scoring systems in predicting septic shock in AP patients with sepsis within 28 days of admission. This assists emergency physicians in identifying high-risk patients early and making timely medical decisions.

Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis

BACKGROUND

This study aimed to construct predictive models for the risk of sepsis in patients with Acute pancreatitis (AP) using machine learning methods and compared optimal one with the logistic regression (LR) model and scoring systems.

METHODS

In this retrospective cohort study, data were collected from the Medical Information Mart for Intensive Care III (MIMIC III) database between 2001 and 2012 and the MIMIC IV database between 2008 and 2019. Patients were randomly divided into training and test sets (8:2). The least absolute shrinkage and selection operator (LASSO) regression plus 5-fold cross-validation were used to screen and confirm the predictive factors. Based on the selected predictive factors, 6 machine learning models were constructed, including support vector machine (SVM), K-nearest neighbour (KNN), multi-layer perceptron (MLP), LR, gradient boosting decision tree (GBDT) and adaptive enhancement algorithm (AdaBoost). The models and scoring systems were evaluated and compared using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and the area under the curve (AUC).

RESULTS

A total of 1, 672 patients were eligible for participation. In the training set, 261 AP patients (19.51%) were diagnosed with sepsis. The predictive factors for the risk of sepsis in AP patients included age, insurance, vasopressors, mechanical ventilation, Glasgow Coma Scale (GCS), heart rate, respiratory rate, temperature, SpO2, platelet, red blood cell distribution width (RDW), International Normalized Ratio (INR), and blood urea nitrogen (BUN). The AUC of the GBDT model for sepsis prediction in the AP patients in the testing set was 0.985. The GBDT model showed better performance in sepsis prediction than the LR, systemic inflammatory response syndrome (SIRS) score, bedside index for severity in acute pancreatitis (BISAP) score, sequential organ failure assessment (SOFA) score, quick-SOFA (qSOFA), and simplified acute physiology score II (SAPS II).

CONCLUSION

The present findings suggest that compared to the classical LR model and SOFA, qSOFA, SAPS II, SIRS, and BISAP scores, the machine learning model-GBDT model had a better performance in predicting sepsis in the AP patients, which is a useful tool for early identification of high-risk patients and timely clinical interventions.

Fire in the belly: A scoping review of the immunopathological mechanisms of acute pancreatitis

Introduction

Acute pancreatitis (AP) is characterised by an inflammatory response that in its most severe form can cause a systemic dysregulated immune response and progression to acute multi-organ dysfunction. The pathobiology of the disease is unclear and as a result no targeted, disease-modifying therapies exist. We performed a scoping review of data pertaining to the human immunology of AP to summarise the current field and to identify future research opportunities.

Methods

A scoping review of all clinical studies of AP immunology was performed across multiple databases. Studies were included if they were human studies of AP with an immunological outcome or intervention.

Results

205 studies met the inclusion criteria for the review. Severe AP is characterised by significant immune dysregulation compared to the milder form of the disease. Broadly, this immune dysfunction was categorised into: innate immune responses (including profound release of damage-associated molecular patterns and heightened activity of pattern recognition receptors), cytokine profile dysregulation (particularly IL-1, 6, 10 and TNF-α), lymphocyte abnormalities, paradoxical immunosuppression (including HLA-DR suppression and increased co-inhibitory molecule expression), and failure of the intestinal barrier function. Studies including interventions were also included. Several limitations in the existing literature have been identified; consolidation and consistency across studies is required if progress is to be made in our understanding of this disease.

Conclusions

AP, particularly the more severe spectrum of the disease, is characterised by a multifaceted immune response that drives tissue injury and contributes to the associated morbidity and mortality. Significant work is required to develop our understanding of the immunopathology of this disease if disease-modifying therapies are to be established.

Circulating monocytes in acute pancreatitis

Acute pancreatitis is a common gastrointestinal disease characterized by inflammation of the exocrine pancreas and manifesting itself through acute onset of abdominal pain. It is frequently associated with organ failure, pancreatic necrosis, and death. Mounting evidence describes monocytes - phagocytic, antigen presenting, and regulatory cells of the innate immune system - as key contributors and regulators of the inflammatory response and subsequent organ failure in acute pancreatitis. This review highlights the recent advances of dynamic change of numbers, phenotypes, and functions of circulating monocytes as well as their underling regulatory mechanisms with a special focus on the role of lipid modulation during acute pancreatitis.

Significant Succession of Intestinal Bacterial Community and Function During the Initial 72 Hours of Acute Pancreatitis in Rats

Acute pancreatitis (AP) is followed by structural and functional changes in the intestine, resulting from microbiome dysbiosis. However, it remains unclear how gut microbiome changes within the initial 72h of onset. In this study, severe acute pancreatitis (SAP), mild acute pancreatitis (MAP), and sham operation (SO) were replicated in rat models. 16S ribosomal RNA gene sequencing was used to explore the gut bacteria community. The predicted Cluster of Orthologous Genes (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways were associated with the 16S rRNA profiles. Compared to the SO group, significant community succession was found during the initial 72h in AP group. At 72 h after AP induction, the Firmicutes/Bacteroidetes (F/B) ratios were significantly different, with the highest ratio in SAP group and the lowest in MAP group. Lactobacillus was the most abundant genus, but it nearly disappeared in SAP rats at 72 h. Clostridiaceae 1 and Clostridium sensu stricto 1 were significantly enriched in AP group. Bacteroidales S24-7 and Bacteroidales S24-7 group norank were enriched in MAP group, while Collinsella, Morganella, and Blautia were enriched in SAP group. Lactobacillus was significantly correlated with nine COGs. Nine COGs showed significant differences between AP group and SO group. Moreover, four COGs showed significant differences between the MAP and SAP groups. KEGG Level_3 pathways propanoate metabolism (Ko00640) in AP group was significantly higher than that in SO group. The aspartate‒ammonia ligase and four KEGG orthology terms of the AP group were lower than that in the SO group, respectively. All these results suggest that the intestinal bacterial community structure and function was changed during the initial 72h in AP rats. The intestinal F/B ratio and the relative abundance of Lactobacillus could be potential markers for early diagnosis of MAP and SAP. The genus Clostridium sensu stricto 1 was the most enriched genus in AP, and may be an important marker for AP.

LONG-TERM EFFECTS OF SHAM SURGERY ON PHAGOCYTE FUNCTIONS IN RATS

Animal models of inflammatory disorders, including those of the nervous system are commonly used to explore the pathophysiological role of immune cell response in disease triggering and course and to develop biotechnology products for therapeutic use. Modeling some of these disorders, particularly neurodegenerative diseases, implies surgical manipulations for the intracerebral introduction of disease-initiating substances (toxins, amyloids etc.). Design of these experiments involves the use of sham-operated animals as a control of non-specific intrinsic side-effects elicited by surgical manipulations per se, including local and systemic inflammation, where phagocytic cells are key participants. Short-term post-surgical immunomodulatory effects are widely reported. However, no study thus far has examined the long term effects of sham-surgery on phagocyte functions. The purpose of this study was to evaluate the effect of sham-surgery, commonly used for modeling neurodegenerative diseases, on phagocyte functions in the far terms after the surgical manipulations. Materials and Methods. Adult male Wistar rats were used in the study. Sham surgery consisted of stereotactic unilateral injection of saline solution into the median forebrain bundle (sham-operated 1, SO1) or directly into the substantia nigra (sham-operated 2, SO2). Before the placebo surgery, animals were anaesthetized using nembutal and ketamine/xylazine correspondingly. Functional characteristics (phagocytic activity, oxidative metabolism, CD80/86 and CD206 expression) of phagocytes (microglia, peritoneal macrophages, circulating monocytes and granulocytes) were examined by flow cytometry. Differential leukocyte count was conducted using hematological analyzer. Results. Phagocytes from animals underwent of different protocols of placebo surgery, demonstrated various patterns of functional changes on day 29 after the manipulations. In animals from SO1 group, we observed signs of residual neuroinflammation (pro-inflammatory shift of microglia functional profile) along with ongoing resolution of systemic inflammation (anti-inflammatory metabolic shift of circulating phagocytes and peritoneal macrophages). In rats from SO2 group, pro-inflammatory polarized activation of peritoneal phagocytes was registered along with anti-inflammatory shift in microglia and circulating phagocytes. Conclusions. Sham surgery influences functions of phagocytic cells of different locations even in the far terms after the manipulations. These effects can be considered as combined long-term consequences of surgical brain injury and the use of anesthetics. Our observations evidences, that sham associated non-specific immunomodulatory effects should always be taken into consideration in animal models of inflammatory central nervous system diseases.

[Etiology and pathogenesis of infected pancreatic necrosis]

Modern literature data confirm the central role of intestinal barrier complex not only as a target in acute necrotizing pancreatitis, but also as a trigger for septic complications. Intra-abdominal hypertension, endothelial dysfunction and gut microbiome changes following necrotizing pancreatitis might have an independent impact on acute intestinal distress syndrome and bacterial translocation. Monitoring of these conditions and early target therapy can improve the outcomes in patients with severe acute pancreatitis. Adverse outcomes of infected pancreatic necrosis including high mortality and morbidity are largely due to the prevalence of multidrug-resistant bacterial pathogens.

Circulating CitH3 Is a Reliable Diagnostic and Prognostic Biomarker of Septic Patients in Acute Pancreatitis

Purpose

Acute pancreatitis (AP) is an inflammatory disease. AP starts with sterile inflammation and is often complicated with critical local or systemic infection or sepsis in severe cases. Septic AP activates peptidyl arginine deiminase (PAD) and citrullinates histone H3 (CitH3), leading to neutrophil extracellular trap (NET) formation. Investigating the role of NETs and underlying mechanisms in septic AP may facilitate developing diagnostic and therapeutic approaches. In this study, we sought to identify the expression of CitH3 in septic AP patients and to analyze the correlation of CitH3 concentration with NET components as well as clinical outcomes.

Methods

Seventy AP patients with or without sepsis (40 septic cases, 30 nonseptic cases) and 30 healthy volunteers were recruited in this study. Concentration of NET components (CitH3 and double-strain DNA) and key enzymes (PAD2/4) were measured. Clinical and laboratory characteristics of patients were recorded and analyzed.

Results

Levels of CitH3 were elevated significantly in septic AP patients compared with those in nonseptic AP and healthy volunteers. The area under the curve (AUC, 95% confidence interval) for diagnosing septic AP was 0.93 (0.86–1.003), and the cutoff was 43.05 pg/ml. Among septic AP cases (n = 40), the concentration of CitH3 was significantly increased in those who did not survive or were admitted to the intensive care unit, when compared with that in those who survived or did not require intensive care unit. Association analysis revealed that CitH3 concentration was positively correlated with PAD2, PAD4, dsDNA concentration, and Sequential Organ Failure Assessment scores.

Conclusion

CitH3 concentration increased in septic AP patients and was closely correlated with disease severity and clinical outcomes. CitH3 may potentially be a diagnostic and prognostic biomarker of septic AP.