The four seasons of pancreatitis - Etiology of acute pancreatitis during the course of the year.

BACKGROUND/OBJECTIVES: Whether seasonality is a factor that influences the incidence of acute pancreatitis (AP) is an under-investigated area. If seasonal incidence peaks can be detected, specifically with regard to biliary pancreatitis, has so far been answered in contradictory ways in the literature. METHODS: All AP cases from two tertiary German referral centers were identified between 2016 and 2022 based on ICD-10 discharge codes. The χ2 test for goodness of fit was applied to test significant differences in monthly and seasonal distributions of AP admissions. RESULTS: In total, 3597 AP cases were included. We observed significantly more idiopathic and biliary cases in May to July (p-values 0.041 and 0.027, respectively). Furthermore, most drug-induced APs were identified during the winter months (p-value 0.006). Moreover, there was a significant peak of alcohol-induced pancreatitis in summer and fall (p-value 0.038). CONCLUSIONS: Our data indicate a seasonal impact on AP incidences for certain etiologies.

Impact of tumor size and location on endoscopic ultrasound-guided sampling of pancreatic neuroendocrine tumors: A recursive partitioning analysis.

BACKGROUND: Current guidelines provide weak recommendations to treat small (<2 cm) non-functional pancreatic neuroendocrine tumors with low Ki-67 proliferation index either by resection or clinical follow-up. However, there is a lack of consensus regarding the minimal size of pNET, which allows EUS-guided biopsy with high enough diagnostic accuracy for stratification. METHODS: We conducted a retrospective, bicentric analysis of patients who had undergone EUS-guided pNET sampling in two tertiary care Endoscopy Units in Germany and Poland. Using a recursive partitioning of the tree-aided model, we aimed to stratify the probability of successful EUS-guided biopsy of pNET lesions according to their size and location. RESULTS: In our pNET cohort, successful histological confirmation of a pNET diagnosis was achieved in 59/69 (85.5%) cases at the initial EUS-guided biopsy. In 41 patients with a pNET size less than 18.5 mm, the EUS-guided first biopsy was successful in 90.2%. In 16 of these patients with smaller lesions, EUS-guided sampling was 100% in very small (less than 11 mm) and extremely small lesions (less than 8 mm). The biopsy success rate was 100% in tail lesions in the size range between ≥5.95 and <8.1 mm but only 33.3% independent of the investigator in pancreatic head or body, with an error rate of 11.2% CONCLUSION: Using a recursive partitioning of the tree-aided stratification model, we demonstrate for the first time that in balancing risks and benefits, very small pNETs (<1 cm) in the tail of the pancreas should be sampled under EUS-guidance.

Severity of gallstone, sludge or microlithiasis induced pancreatitis - all of the same?

BACKGROUND/AIM: Severity of microlithiasis and sludge-induced pancreatitis in comparison to gallstone-induced pancreatitis has never been studied for a lack of definition. In order to understand whether bile duct obstruction or other mechanisms contribute to biliary pancreatitis severity we performed a monocentric, retrospective cohort study. METHODS: In this retrospective cohort study 263 patients with acute biliary pancreatitis treated at a tertiary care center from 2005 to 2021 were stratified according to the recent consensus definition for microlithiasis and sludge. The gallstone-pancreatitis cohort was compared to microlithiasis, sludge and suspected stone passage pancreatitis cohorts in terms of pancreatitis outcome, liver function and EUS/ERCP results using one-way ANOVA and Chi2 test. Multinomial logistic regression analysis was performed to correct for bias. RESULTS: Microlithiasis and sludge-induced pancreatitis classified according to the revised Atlanta classification, did not present with a milder course than gallstone-induced pancreatitis (p = 0.62). Microlithiasis and sludge showed an increase in bilirubin on the day of admission to hospital, which was not significantly different from gallstone-induced pancreatitis (p = 0.36). The likelihood of detecting biliary disease on EUS resulting in bile duct clearance was highest on the day of admission and day 1, respectively. CONCLUSION: Microlithiasis and sludge induce gallstone-equivalent impaired liver function tests and induce pancreatitis with similar severity compared with gallstone-induced acute biliary pancreatitis.

Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology.

BACKGROUND: Biliary microlithiasis/sludge is detected in approximately 30% of patients with idiopathic acute pancreatitis (IAP). As recurrent biliary pancreatitis can be prevented, the underlying aetiology of IAP should be established. AIM: To develop a machine learning (ML) based decision tool for the use of endosonography (EUS) in pancreatitis patients to detect sludge and microlithiasis. METHODS: We retrospectively used routinely recorded clinical and laboratory parameters of 218 consecutive patients with confirmed AP admitted to our tertiary care hospital between 2015 and 2020. Patients who did not receive EUS as part of the diagnostic work-up and whose pancreatitis episode could be adequately explained by other causes than biliary sludge and microlithiasis were excluded. We trained supervised ML classifiers using H2O.ai automatically selecting the best suitable predictor model to predict microlithiasis/sludge. The predictor model was further validated in two independent retrospective cohorts from two tertiary care centers (117 patients). RESULTS: Twenty-eight categorized patients' variables recorded at admission were identified to compute the predictor model with an accuracy of 0.84 [95% confidence interval (CI): 0.791-0.9185], positive predictive value of 0.84, and negative predictive value of 0.80 in the identification cohort (218 patients). In the validation cohort, the robustness of the prediction model was confirmed with an accuracy of 0.76 (95%CI: 0.673-0.8347), positive predictive value of 0.76, and negative predictive value of 0.78 (117 patients). CONCLUSION: We present a robust and validated ML-based predictor model consisting of routinely recorded parameters at admission that can predict biliary sludge and microlithiasis as the cause of AP.