Macrophage migration inhibitory factor is an early marker of severe acute pancreatitis based on the revised Atlanta classification

High-density lipoprotein cholesterol level as an independent protective factor against aggravation of acute pancreatitis: a case-control study

Background and aims

At present, evidence on the association between high-density lipoprotein cholesterol (HDL-C) levels and aggravation of acute pancreatitis (AP) is limited. This study aimed to investigate the relationship between the lowest HDL-C level during intensive care units (ICU) stay and AP aggravation and to determine the optimum cutoff lowest HDL-C level.

Methods

Patients admitted to the ICU of the Shandong Provincial Hospital for AP from 2015 to 2021 were included. The lowest HDL-C level during ICU stay was set as the independent variable, and the progression or non-progression to severe AP (SAP) was set as the dependent variable. Univariate and multivariate analyses were performed to determine the relationship between the two variables, and receiver operating characteristic (ROC) curves were plotted to analyze the predictive ability of the lowest HDL-C level for progression to SAP.

Results

This study included 115 patients. The difference in the lowest HDL-C level between the SAP and moderately SAP groups was significant (P < 0.05). After adjusting for covariates, the lowest HDL-C level showed a negative correlation with the occurrence of SAP, with a relative risk of 0.897 (95% confidence interval: 0.827-0.973). The area under the ROC curve for prediction of AP aggravation by the lowest HDL-C level was 0.707, and the optimum cutoff lowest HDL-C level was 0.545 mmol/L.

Conclusion

No less than 0.545 mmol/L of the HDL-C level during ICU stay may be an independent protective factor for the aggravation of AP.

Clinical utility of the pancreatitis activity scoring system in severe acute pancreatitis

Objective: To analyze clinical utility of pancreatitis activity scoring system (PASS) in prediction of persistent organ failure, poor prognosis, and in-hospital mortality in patients with moderately severe acute pancreatitis (MSAP) or severe acute pancreatitis (SAP) admitted to the intensive care unit (ICU).

Methods: The study included a total of 140 patients with MSAP and SAP admitted to the ICU of Shandong Provincial Hospital from 2015 to 2021. The general information, biochemical indexes and PASS scores of patients at ICU admission time were collected. Independent risk factors of persistent organ failure, poor prognosis and in-hospital mortality were analyzed by binary logistic regression. Through receiver operating characteristic curve (ROC), the predictive ability of lactic acid, procalcitonin, urea nitrogen, PASS, and PASS in combination with urea nitrogen for the three outcomes was compared. The best cut-off value was determined.

Results: Binary logistic regression showed that PASS might be an independent risk factor for patients with persistent organ failure (odds ratio [OR]: 1.027, 95% confidence interval [CI]: 1.014–1.039), poor prognosis (OR: 1.008, 95% CI: 1.001–1.014), and in-hospital mortality (OR: 1.009, 95% CI: 1.000–1.019). PASS also had a good predictive ability for persistent organ failure (area under the curve (AUC) = 0.839, 95% CI: 0.769–0.910) and in-hospital mortality (AUC = 0.780, 95% CI: 0.669–0.891), which was significantly superior to lactic acid, procalcitonin, urea nitrogen and Ranson score. PASS (AUC = 0.756, 95% CI: 0.675–0.837) was second only to urea nitrogen (AUC = 0.768, 95% CI: 0.686–0.850) in the prediction of poor prognosis. Furthermore, the predictive power of urea nitrogen in combination with PASS was better than that of each factor for persistent organ failure (AUC = 0.849, 95% CI: 0.779–0.920), poor prognosis (AUC = 0.801, 95% CI: 0.726–0.876), and in-hospital mortality (AUC = 0.796, 95% CI: 0.697–0.894).

Conclusion: PASS was closely correlated with the prognosis of patients with MSAP and SAP. This scoring system may be used as a common clinical index to measure the activity of acute pancreatitis and evaluate disease prognosis.

Early prediction of severe acute pancreatitis using machine learning

BACKGROUND

Acute pancreatitis (AP) is one of the most common causes of gastrointestinal-related hospitalizations in the United States. Severe AP (SAP) is associated with a mortality rate of nearly 30% and is distinguished from milder forms of AP. Risk stratification to identify SAP cases needing inpatient treatment is an important aspect of AP diagnosis.

METHODS

We developed machine learning algorithms to predict which patients presenting with AP would require treatment for SAP. Three models were developed using logistic regression, neural networks, and XGBoost. Models were assessed in terms of area under the receiver operating characteristic (AUROC) and compared to the Harmless Acute Pancreatitis Score (HAPS) and Bedside Index for Severity in Acute Pancreatitis (BISAP) scores for AP risk stratification.

RESULTS

61,894 patients were used to train and test the machine learning models. With an AUROC value of 0.921, the model developed using XGBoost outperformed the logistic regression and neural network-based models. The XGBoost model also achieved a higher AUROC than both HAPS and BISAP for identifying patients who would be diagnosed with SAP.

CONCLUSIONS

Machine learning may be able to improve the accuracy of AP risk stratification methods and allow for more timely treatment and initiation of interventions.

Targeting Macrophage Migration Inhibitory Factor in Acute Pancreatitis and Pancreatic Cancer

Macrophage migration inhibitory factor (MIF) is a pleiotropic cytokine implicated in the pathogenesis of inflammation and cancer. It is produced by various cells and circulating MIF has been identified as a biomarker for a range of diseases. Extracellular MIF mainly binds to the cluster of differentiation 74 (CD74)/CD44 to activate downstream signaling pathways. These in turn activate immune responses, enhance inflammation and can promote cancer cell proliferation and invasion. Extracellular MIF also binds to the C-X-C chemokine receptors cooperating with or without CD74 to activate chemokine response. Intracellular MIF is involved in Toll-like receptor and inflammasome-mediated inflammatory response. Pharmacological inhibition of MIF has been shown to hold great promise in treating inflammatory diseases and cancer, including small molecule MIF inhibitors targeting the tautomerase active site of MIF and antibodies that neutralize MIF. In the current review, we discuss the role of MIF signaling pathways in inflammation and cancer and summarize the recent advances of the role of MIF in experimental and clinical exocrine pancreatic diseases. We expect to provide insights into clinical translation of MIF antagonism as a strategy for treating acute pancreatitis and pancreatic cancer.