Prediction of Outcome in Acute Pancreatitis by the qSOFA and the New ERAP Score

American College of Gastroenterology Guidelines: Management of Acute Pancreatitis

Acute pancreatitis (AP), defined as acute inflammation of the pancreas, is one of the most common diseases of the gastrointestinal tract leading to hospital admission in the United States. It is important for clinicians to appreciate that AP is heterogenous, progressing differently among patients and is often unpredictable. While most patients experience symptoms lasting a few days, almost one-fifth of patients will go on to experience complications, including pancreatic necrosis and/or organ failure, at times requiring prolonged hospitalization, intensive care, and radiologic, surgical, and/or endoscopic intervention. Early management is essential to identify and treat patients with AP to prevent complications. Patients with biliary pancreatitis typically will require surgery to prevent recurrent disease and may need early endoscopic retrograde cholangiopancreatography if the disease is complicated by cholangitis. Nutrition plays an important role in treating patients with AP. The safety of early refeeding and importance in preventing complications from AP are addressed. This guideline will provide an evidence-based practical approach to the management of patients with AP.

Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence

Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomography, magnetic resonance imaging, and ultrasound, and scoring systems, including Ranson, Acute Physiology and Chronic Health Evaluation II, and Bedside Index for Severity in AP scores. Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity, while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications. Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild, moderate, or severe categories, guiding treatment decisions, such as intensive care unit admission, early enteral feeding, and antibiotic use. Despite the central role of imaging technologies and scoring systems in AP management, these methods have limitations in terms of accuracy, reproducibility, practicality and economics. Recent advancements of artificial intelligence (AI) provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data. AI algorithms can analyze large amounts of clinical and imaging data, identify scoring system patterns, and predict the clinical course of disease. AI-based models have shown promising results in predicting the severity and mortality of AP, but further validation and standardization are required before widespread clinical application. In addition, understanding the correlation between these three technologies will aid in developing new methods that can accurately, sensitively, and specifically be used in the diagnosis, severity prediction, and prognosis assessment of AP through complementary advantages.

Medical imaging for pancreatic diseases: Prediction of severe acute pancreatitis complicated with acute respiratory distress syndrome

In this editorial we comment on the article published in the recent issue of the World Journal of Gastroenterology [2022; 28 (19): 2123-2136]. We pay attention to how to construct a simpler and more reliable new clinical predictive model to early identify patients at high risk of acute respiratory distress syndrome (ARDS) associated with severe acute pancreatitis (SAP), and to early predict the severity of organ failure from chest computed tomography (CT) findings in SAP patients. As we all know, SAP has a sudden onset, is a rapidly changing condition, and can be complicated with ARDS and even multiple organ dysfunction syndrome, and its mortality rate has remained high. At present, there are many clinical scoring systems for AP, including the bedside index for severity in AP, acute physiology and chronic health evaluation II, systemic inflammatory response syndrome, Japanese severe score, quick sepsis-related organ failure assessment, etc. However, some of these scoring systems are complex and require multiple and difficult clinical parameters for risk stratification. Although the aforementioned biomarkers are readily available, their ability to predict ARDS varies. Accor-dingly, it is extremely necessary to establish a simple and valuable novel model to predict the development of ARDS in AP. In addition, the extra-pancreatic manifestations of AP patients often involve the chest, among which pleural effusion and pulmonary consolidation are the more common complications. Therefore, by measuring the semi-quantitative indexes of chest CT in AP patients, such as the amount of pleural effusion and the number of lobes involved as pulmonary consolidation, it has important reference value for the early diagnosis of SAP complicated with ARDS and is expected to provide a basis for the early treatment of ARDS.

Ideal scoring system for acute pancreatitis: Quest for the Holy Grail

Clinical scoring systems are required to predict complications, severity, need for intensive care unit admission, and mortality in patients with acute pancreatitis. Over the years, many scores have been developed, tested, and compared for their efficacy and accuracy. An ideal score should be rapid, reliable, and validated in different patient populations and geographical areas and should not lose relevance over time. A combination of scores or serial monitoring of a single score may increase their efficacy.

Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning

Background: Current approaches to predicting intervention needs and mortality have reached 65–85% accuracy, which falls below clinical decision-making requirements in patients with acute pancreatitis (AP). We aimed to accurately predict therapeutic intervention needs and mortality on admission, in AP patients, using machine learning (ML). Methods: Data were obtained from three databases of patients admitted with AP: one retrospective (Chengdu) and two prospective (Liverpool and Chengdu) databases. Intervention and mortality differences, as well as potential predictors, were investigated. Univariate analysis was conducted, followed by a random forest ML algorithm used in multivariate analysis, to identify predictors. The ML performance matrix was applied to evaluate the model’s performance. Results: Three datasets of 2846 patients included 25 potential clinical predictors in the univariate analysis. The top ten identified predictors were obtained by ML models, for predicting interventions and mortality, from the training dataset. The prediction of interventions includes death in non-intervention patients, validated with high accuracy (96%/98%), the area under the receiver-operating-characteristic curve (0.90/0.98), and positive likelihood ratios (22.3/69.8), respectively. The post-test probabilities in the test set were 55.4% and 71.6%, respectively, which were considerably superior to existing prognostic scores. The ML model, for predicting mortality in intervention patients, performed better or equally with prognostic scores. Conclusions: ML, using admission clinical predictors, can accurately predict therapeutic interventions and mortality in patients with AP.

Influence of extracorporeal cytokine adsorption on hemodynamics in severe acute pancreatitis: Results of the matched cohort pancreatitis cytosorbents inflammatory cytokine removal (PACIFIC) study

BACKGROUND

Outcome of severe acute pancreatitis (SAP) highly depends on the degree of systemic inflammation and organ failure. Although treatment approaches targeting the inflammatory cascade have failed in pancreatitis, recent studies suggest that extracorporeal cytokine adsorption effectively reduces concentrations of pro-inflammatory cytokines and potentially improves the outcome of sepsis.

METHODS

Sixteen patients with SAP, presenting within 7 days upon onset of pain, an APACHE-II score of ≥10 and ≥1 marker of poor prognosis, received 2 consecutive 24-h treatments with CytoSorb® extracorporeal cytokine adsorption (intervention group). Hemodynamics, organ failure, and mortality were compared with an APACHE-II score-matched retrospective control group of 32 patients.

RESULTS

The primary objective (20% decrease in the vasopressor dependency index or 20% increase in the cardiac index) was reached in 68.8% of the intervention and 28.1% of the control patients (p = 0.007), respectively. The cytokine adsorption significantly reduced IL-6 (-1998 pg/ml, p = 0.005) serum levels and resulted in stable CRP (p = 0.101) and decreased PCT (p = 0.003) levels in contrast to increased CRP (p = 0.014) and stable PCT levels (p = 0.695) in the control group. While mortality and improvement of respiratory failure were similar in both groups, renal failure significantly improved (change of KDIGO classification 72 h postcytokine adsorption [-1 vs. 0, p = 0.005]) and the SOFA score significantly decreased (day 5: -1.8 ± 2.0 vs. 1 ± 3.8, p = 0.013) in the intervention group.

CONCLUSION

Cytokine adsorption might be an effective treatment option to stabilize hemodynamics in SAP. It decreases levels of the pro-inflammatory marker IL-6 and stabilizes organ function according to serial SOFA score assessments.

Predictors of Mortality and Drug Resistance Among Carbapenem-Resistant Enterobacteriaceae-Infected Pancreatic Necrosis Patients

Introduction

Carbapenem-resistant Enterobacteriaceae (CRE) has emerged as a global threat to hospitalization patients. Infected pancreatic necrosis (IPN) leads to high risks of CRE infections with increasing mortality. Our study aims to determine the predictors related to 90-day overall mortality of CRE IPN.

Methods

We retrospectively reviewed the drug resistance rates and clinical characteristics of CRE IPN patients from January 1, 2016, to January 1, 2021. Independent predictors of mortality were identified via univariate and multivariate analyses.

Results

During the 5-year period, 75 IPN patients suffered from 135 episodes of CRE infections with mortality up to 50.7%. CRE strains were highly resistant (> 50%) to nine of ten common antibiotics, except tigecycline (18%). The most common pathogen was carbapenem-resistant Klebsiella pneumoniae (84 of 135). Lung was the main site of extrapancreatic infections, followed by bloodstream and biliary tract. The independent predictors of mortality were Sequential Organ Failure Assessment (SOFA) score > 2 (hazard ratio 3.746, 95% confidence interval 1.209–11.609, P = 0.022) and procalcitonin > 6 ng/l (hazard ratio 2.428, 95% confidence interval 1.204–4.895, P = 0.013).

Conclusion

CRE is widespread as a global challenge with a high mortality rate among IPN patients due to limited therapeutic options. Carbapenem-resistant K. pneumoniae is the leading category of CRE which requires more attention in clinical practice. High SOFA score and procalcitonin level represent two independent predictors of mortality in CRE IPN patients. Greater efforts are needed toward timely therapeutic intervention for CRE IPN.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40121-021-00489-5.