Dynamics of Serum Procalcitonin Can Predict Outcome in Patients of Infected Pancreatic Necrosis: A Prospective Analysis

Diagnosis and Management of Acute Pancreatitis

Acute pancreatitis (AP) is increasing in incidence across the world, and in all age groups. Major changes in management have occurred in the last decade. Avoiding total parenteral nutrition and prophylactic antibiotics, avoiding overly aggressive fluid resuscitation, initiating early feeding, avoiding endoscopic retrograde cholangiopancreatography in the absence of concomitant cholangitis, same-admission cholecystectomy, and minimally invasive approaches to infected necrosis should now be standard of care. Increasing recognition of the risk of recurrence of AP, and progression to chronic pancreatitis, along with the unexpectedly high risk of diabetes and exocrine insufficiency after AP is the subject of large ongoing studies. In this review, we provide an update on important changes in management for this increasingly common disease.

Venous Thrombosis in Acute Pancreatitis: What to and Not to Do?

Acute pancreatitis is an acute inflammatory condition of the pancreas that has not only local but systemic effects as well. Venous thrombosis is one such complication which can give rise to thrombosis of the peripheral vasculature in the form of deep vein thrombosis, pulmonary embolism, and splanchnic vein thrombosis. The prevalence of these complications increases with the severity of the disease and adds to the adverse outcomes profile. With better imaging and awareness, more cases are being detected, although many at times it can be an incidental finding. However, it remains understudied and strangely, most of the guidelines on the management of acute pancreatitis are silent on this aspect. This review offers an overview of the incidence, pathophysiology, symptomatology, diagnostic work-up, and management of venous thrombosis that develops in AP.

Neutrophil CD64 index as a new early predictive biomarker for infected pancreatic necrosis in acute pancreatitis

OBJECTIVE

Infectious pancreatic necrosis (IPN) is a serious complication of acute pancreatitis, and early recognition and timely intervention are the keys to improving clinical outcomes. The purpose of this study was to investigate the predictive capacity of the neutrophil CD64 index (nCD64 index) on IPN in patients with acute pancreatitis METHODS: This study comprises two independent cohorts: the training cohort consisted of 202 patients from Hunan Provincial People's Hospital, and the validation cohort consisted of 100 patients from Changsha Central Hospital. Peripheral blood samples were collected on the day of admission and on the 3rd, 5th, 7th, and 10th days of hospitalization, and the nCD64 index was detected by flow cytometry. Additionally, relevant clinical characteristics and laboratory biomarkers were collected and analyzed.

RESULTS

We observed that nCD64 index on admission was significantly higher in the IPN group than Non-IPN group (p < 0.001). In the training cohort, a higher occurrence rate of IPN was observed in the high nCD64 index group compared to the moderate and low nCD64 index group (p < 0.001). Further analysis showed that nCD64 index was significant positive correlated with the incidence rate of IPN (p < 0.001, correlation coefficient = 0.972). Furthermore, logistic regression analysis showed that high expression of the nCD64 index on admission was a risk factor for the occurrence of IPN (OR = 2.971, p = 0.038). We further found that the nCD64 index of IPN patients was significantly higher than the Non-IPN patients on the days 1, 3, and 5 after admission, and the nCD64 index of IPN patients before and after the onset (p < 0.05). At the same time, this study revealed that the nCD64 index on admission showed good predictive efficacy for IPN (AUC = 0.859, sensitivity = 80.8%, specificity = 87.5%), which was comparable to APACHE II score. And this finding was further validated in an independent cohort of 100 participants (AUC = 0.919, Sensitivity = 100.0%, Specificity = 76.6%).

CONCLUSION

This study demonstrated the clinical value of nCD64 index in patients with IPN patients for the first time through two independent cohort studies. The nCD64 index can be used as an early prediction and risk assessment tool for the occurrence of IPN, contributing to the improvement of patient outcomes and efficiency of medical resource allocation.

Endoscopic Ultrasound-Guided Drainage of Pancreatic Fluid Collections: Not All Queries Are Already Solved

Pancreatic fluid collections (PFCs) are well-known complications of acute pancreatitis. The overinfection of these collections leads to a worsening of the prognosis with an increase in the morbidity and mortality rate. The primary strategy for managing infected pancreatic necrosis (IPN) or symptomatic PFCs is a minimally invasive step-up approach, with endosonography-guided (EUS-guided) transmural drainage and debridement as the preferred and less invasive method. Different stents are available to drain PFCs: self-expandable metal stents (SEMSs), double pigtail stents (DPPSs), or lumen-apposing metal stents (LAMSs). In particular, LAMSs are useful when direct endoscopic necrosectomy is needed, as they allow easy access to the necrotic cavity; however, the rate of adverse events is not negligible, and to date, the superiority over DPPSs is still debated. Moreover, the timing for necrosectomy, the drainage technique, and the concurrent medical management are still debated. In this review, we focus attention on indications, timing, techniques, complications, and particularly on aspects that remain under debate concerning the EUS-guided drainage of PFCs.

Persistently High Procalcitonin and C-Reactive Protein Are Good Predictors of Infection in Acute Necrotizing Pancreatitis: A Systematic Review and Meta-Analysis

Infected necrotizing pancreatitis (INP) is associated with an increased risk of organ failure and mortality. Its early recognition and timely initiation of antibiotic therapy can save patients' lives. We systematically searched three databases on 27 October 2022. In the eligible studies, the presence of infection in necrotizing pancreatitis was confirmed via a reference test, which involved either the identification of gas within the necrotic collection through computed tomography imaging or the examination of collected samples, which yielded positive results in Gram staining or culture. Laboratory biomarkers compared between sterile necrotizing pancreatitis and INP were used as the index test, and our outcome measures included sensitivity, specificity, the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Within the first 72 hours (h) after admission, the AUC of C-reactive protein (CRP) was 0.69 (confidence interval (CI): 0.62-0.76), for procalcitonin (PCT), it was 0.69 (CI: 0.60-0.78), and for white blood cell count, it was 0.61 (CI: 0.47-0.75). After the first 72 h, the pooled AUC of CRP showed an elevated level of 0.88 (CI: 0.75-1.00), and for PCT, it was 0.86 (CI: 0.60-1.11). The predictive value of CRP and PCT for infection is poor within 72 h after hospital admission but seems good after the first 72 h. Based on these results, infection is likely in case of persistently high CRP and PCT, and antibiotic initiation may be recommended.

Early prediction of acute respiratory distress syndrome complicated by acute pancreatitis based on four machine learning models

BACKGROUND

Acute Respiratory Distress syndrome (ARDS) is a common complication of Acute Pancreatitis (AP) and is associated with high mortality. This study used Machine Learning (ML) to predict ARDS in patients with AP at admission.

METHODS

The authors retrospectively analyzed the data from patients with AP from January 2017 to August 2022. Clinical and laboratory parameters with significant differences between patients with and without ARDS were screened by univariate analysis. Then, Support Vector Machine (SVM), Ensembles of Decision Trees (EDTs), Bayesian Classifier (BC), and nomogram models were constructed and optimized after feature screening based on these parameters. Five-fold cross-validation was used to train each model. A test set was used to evaluate the predictive performance of the four models.

RESULTS

A total of 83 (18.04%) of 460 patients with AP developed ARDS. Thirty-one features with significant differences between the groups with and without ARDS in the training set were used for modeling. The Partial Pressure of Oxygen (PaO₂), C-reactive protein, procalcitonin, lactic acid, Ca²⁺, the neutrophil:lymphocyte ratio, white blood cell count, and amylase were identified as the optimal subset of features. The BC algorithm had the best predictive performance with the highest AUC value (0.891) than SVM (0.870), EDTs (0.813), and the nomogram (0.874) in the test set. The EDT algorithm achieved the highest accuracy (0.891), precision (0.800), and F1 score (0.615), but the lowest FDR (0.200) and the second-highest NPV (0.902).

CONCLUSIONS

A predictive model of ARDS complicated by AP was successfully developed based on ML. Predictive performance was evaluated by a test set, for which BC showed superior predictive performance and EDTs could be a more promising prediction tool for larger samples.