Barriers in inflammatory bowel disease care in Central and Eastern Europe: a region-specific analysis.

Inflammatory bowel diseases (IBD), including Crohn's disease and ulcerative colitis, are chronic immune-mediated diseases with a high incidence and prevalence in Europe. Since these are diseases with associated disability, they require complex management and the availability of high-quality healthcare resources. We focused on the analysis of IBD care in selected countries of Central and Eastern Europe (Croatia, the Czech Republic, Hungary, Moldova, Poland, Romania and Slovakia) targeting the availability and reimbursement of diagnostic and therapeutic modalities, the role of IBD centers and also education and research in IBD. As part of the analysis, we created a questionnaire of 73 statements organized in three topics: (1) diagnostics, follow-up and screening, (2) medications and (3) IBD centers. The questionnaire was filled out by co-authoring IBD experts from individual countries, and then the answers and comments on the questionnaire were analyzed. We identified that despite the financial burden, which still partially persists in the region, the availability of some of the cost-saving tools (calprotectin test, therapeutic drug monitoring) differs among countries, mainly due to variable reimbursement from country to country. In most participating countries, there also remains a lack of dedicated dietary and psychological counseling, which is often replaced by recommendations offered by gastroenterologists. However, there is adequate availability of most of the currently recommended diagnostic methods and therapies in each participating country, as well as the implementation of established IBD centers in the region.

A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization.

Background and aim: COVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak, several machine learning algorithms were implemented to assess new diagnostic and therapeutic methods for this disease. The aim of this study is to assess gastrointestinal and liver-related predictive factors for SARS-CoV-2 associated risk of hospitalization. Methods: Data collection was based on a questionnaire from the COVID-19 outpatient test center and from the emergency department at the University Hospital in combination with the data from internal hospital information system and from a mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls in three different SARS-CoV-2 positive patient groups (divided based on severity of the disease). The data were visualized and analyzed in R version 4.0.5. The Chi-squared or Fisher test was applied to test the null hypothesis of independence between the factors followed, where appropriate, by the multiple comparisons with the Benjamini Hochberg adjustment. The null hypothesis of the equality of the population medians of a continuous variable was tested by the Kruskal Wallis test, followed by the Dunn multiple comparisons test. In order to assess predictive power of the gastrointestinal parameters and other measured variables for predicting an outcome of the patient group the Random Forest machine learning algorithm was trained on the data. The predictive ability was quantified by the ROC curve, constructed from the Out-of-Bag data. Matthews correlation coefficient was used as a one-number summary of the quality of binary classification. The importance of the predictors was measured using the Variable Importance. A 2D representation of the data was obtained by means of Principal Component Analysis for mixed type of data. Findings with the p-value below 0.05 were considered statistically significant. Results: A total of 710 patients were enrolled in the study. The presence of diarrhea and nausea was significantly higher in the emergency department group than in the COVID-19 outpatient test center. Among liver enzymes only aspartate transaminase (AST) has been significantly elevated in the hospitalized group compared to patients discharged home. Based on the Random Forest algorithm, AST has been identified as the most important predictor followed by age or diabetes mellitus. Diarrhea and bloating have also predictive importance, although much lower than AST. Conclusion: SARS-CoV-2 positivity is connected with isolated AST elevation and the level is linked with the severity of the disease. Furthermore, using the machine learning Random Forest algorithm, we have identified the elevated AST as the most important predictor for COVID-19 related hospitalizations.

Management of primary sclerosing cholangitis and its complications: an algorithmic approach.

Primary sclerosing cholangitis (PSC) is a rare cholestatic liver disease, characterized by multiple strictures and dilatations of the intra- and extrahepatic bile ducts, leading to progressive liver fibrosis, in 10-15% cholangiocarcinoma, and ultimately end-stage liver disease. The pathogenesis is poorly understood, but (epi-)genetic factors, mechanisms of innate and adaptive immunity, toxic effects of hydrophobic bile acids, and possibly intestinal dysbiosis appear to be involved. The strong link with inflammatory bowel disease (IBD) is associated with a markedly enhanced risk of colorectal cancer which next to cholangiocarcinoma represents the most serious diagnostic challenge in long-term PSC management. Despite extensive research, no medical treatment has been proven so far to prolong the time to liver transplantation (LTx), which remains the effective treatment in late-stage disease. Recurrence of PSC after LTx is observed in up to 20% of patients. Here, we briefly summarize actual views on PSC pathogenesis and provide an algorithmic approach to diagnostic procedures and recommendations for the management of PSC and its complications. We describe promising treatment options subject to current clinical trials.