Role of Static and Dynamic Intra-abdominal Pressure Monitoring in Acute Pancreatitis: A Prospective Study on Its Impact

Intra-abdominal hypertension and abdominal compartment syndrome in acute pancreatitis

Intra-abdominal hypertension (IAH) and abdominal compartment syndrome (ACS) are underrecognized entities in patients of acute pancreatitis (AP). IAH develops in 30% to 60% and ACS in 15% to 30% of all AP patients and they are markers of severe disease with high morbidity and mortality. The detrimental effect of increased IAP has been recognized in several organ systems, including the central nervous system, cardiovascular, respiratory, renal and gastrointestinal systems. The pathophysiology of IAH/ACS development in patients with AP is multifactorial. Pathogenetic mechanisms include over-zealous fluid management, visceral edema, ileus, peripancreatic fluid collections, ascites and retroperitoneal edema. Laboratory and imaging markers are neither sensitive nor specific enough to detect IAH/ACS and intra-abdominal pressure (IAP) monitoring is vital for early diagnosis and the management of patients of AP with IAH/ACS. The treatment of IAH/ACS requires a multi-modality approach with both medical and surgical attention. Medical management consists of nasogastric/rectal decompression, prokinetics, fluid management and diuretics or hemodialysis. If conservative management is not effective, percutaneous drainage of fluid collection or ascites is necessary. Despite medical management, if IAP worsens, surgical decompression is warranted. The review discusses the relevance of IAH/ACS in patients of AP and its management.

Controversies in EUS-guided treatment of walled-off necrosis

This review gives an overview of different techniques in the treatment of post-acute complications of acute pancreatitis. The endoscopic treatment of those complications is currently standard of care. EUS opened up the broad implementation of internal drainage methods to make them safe and effective. Due to different endoscopic approaches worldwide, controversies have arisen that are pointed out in this paper. The main focus was placed on weighing up evidence to find the optimal approach. However, if no evidence can be provided, the authors, experienced in the field, give their personal advice.

Application Effect of Intra-Abdominal Pressure Monitoring System in Early Enteral Nutrition Nursing of ICU Patients

In order to study and explore the application effect of intra-abdominal pressure monitoring in guiding ICU patients to implement early enteral nutrition therapy, we selected patients admitted to the emergency department of a hospital and classified them into groups A (n = 105) and B (n = 98). Among them, the A group gave early enteral nutrition treatment by monitoring the residual gastric mass, and the B group gave early enteral nutrition treatment by monitoring the stomach. It has been established that, compared to gastric residue monitoring, intra-abdominal pressure monitoring in ICU patients with early enteral nutrition therapy has an obvious advantage, it helps to improve the prognosis of patients, and intra-abdominal pressure combined with gastric residual monitoring scheme can effectively reduce the early enteral nutrition in the ICU patients, the incidence of abdominal distension, vomiting, and make it reach the goal as soon as possible. Early enteral nutrition in patients with increased tolerance is of great significance.

Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.