Background and Aims: Fecal biomarkers are important non-invasive markers monitoring disease activity in inflammatory bowel disease (IBD). We compared the significance of fecal eosinophil cationic protein (fECP) and fecal calprotectin (fCal). Methods: fECP and fCal were measured in patients with Crohn’s disease (CD, n = 97), ulcerative colitis (UC, n = 53), Clostridioides difficile infection (CDI, n = 9), primary food allergy (PFA, n = 11), pollen-associated food allergy (n = 25) and non-inflammatory controls (n = 78). Results were correlated with clinical and endoscopic IBD activity scores. Results: fECP was significantly elevated in CD, UC, CDI and PFA compared to controls. fCal was significantly increased in CD, UC and CDI. fECP had lower diagnostic accuracy than fCal (area under the curve (AUC) = 0.88) in differentiating between endoscopically active and inactive patients with IBD (AUC = 0.77, ROC analysis). In contrast to fCal, fECP correlated negatively with age and levels were also elevated in clinically and endoscopically inactive patients with IBD <45 years (endoscopically inactive IBD vs controls; AUC for fECP = 0.86; AUC for fCal = 0.62). However, in those patients with low inflammatory activity (fCal <250 mg/kg), high fECP indicated the need for treatment modification or surgery (fECP 600 µg/kg = 82%) at month 48 of follow-up. Conclusions: fECP is a diagnostic and prognostic marker in young patients with IBD in remission.

Introduction: The non-invasive assessment of disease severity remains pivotal in patients with chronic liver disease (CLD) as it has wide implications in predicting liver-related complications or death. Shear-wave elastography (SWE) is an emerging ultrasound-based method to non-invasively measure liver stiffness. The aim of our study was to evaluate two-dimensional (2D) and point (p) SWE to predict the presence of esophageal varices (EV) or clinically significant portal hypertension (CSPH). Methods: This was a retrospective analysis of a prospectively performed cohort study of patients with CLD treated in the outpatient clinic of the Frankfurt University Hospital. PSWE using the Hitachi HI Vision ASCENDUS system and the Siemens ACUSON S2000^TM system or 2D-SWE using the Toshiba APLIO500 system were analyzed at baseline and during follow-up to predict EV or surrogate parameters of CSPH. ROC curves were calculated for pooled liver stiffness measurements (LSMs) using a bootstrap approach. A combined model of SWE and platelet count was created and a mixed-effect logistic regression analysis using log-transformed values was performed. Results: Overall, 511 patients with CLD and 919 consecutive LSMs were included and 315 patients (61.6%) had signs of CSPH. 2D-SWE performed best to predict EV and CSPH, and the addition of platelet count to the predictive model significantly increased test results for EV (AUC 0.83, 95%-CI: 0.76–0.89; difference in AUC 0.11, 95%-CI: 0.03–0.19, p = 0.004), but only marginally for CSPH (AUC 0.75, 95%-CI: 0.64–0.85; difference in AUC 0.06, 95%-CI: 0.02–0.14, p = 0.150). LSM > 18.5 and >20 kPa were indicative of CSPH and EV, while LSM < 10 kPa and <11 kPa ruled out CSPH and EV, respectively. Conclusions: Our study found that 2D-SWE in combination with platelet count performed best (in comparison to the other SWE methods) to predict EV or CSPH in patients with CLD. Future prospective trials are needed to validate our results.

Background: Hospitalized patients with end-stage liver disease are at risk of malnutrition, reduced body function, and cognitive impairment due to HE. This combination may have an impact on in-hospital falls and mortality. The purpose of this study was to identify factors associated with the risk of falls and to analyze the consequences regarding in-hospital mortality. Methods: We performed a retrospective analysis of patients hospitalized with liver cirrhosis between 2017 and 2019 at the Department of Gastroenterology at the University Hospital Frankfurt. Clinical data, laboratory work, and follow-up data were analyzed. Factors associated with the risk of falls and in-hospital mortality were calculated using a mixed effect poisson regression model and competing risk time-to-event analyses. Results: Falls occurred with an incidence of 4% (80/1985), including 44 injurious falls with an incidence rate of 0.00005/100 patient-days (95% CI: 0.00001–0.00022). In the multivariate analysis malnutrition (incidence risk ratio: 1.77, 95% CI: 1.04–3.04) and implanted TIPS (incidence risk ratio: 20.09, 95% CI: 10.1–40.1) were independently associated with the risk of falling. In a total of 21/80 (26.25%) hospitalizations, patients with a documented fall died during their hospital stay versus 160/1905 (8.4%) deaths in hospitalizations without in-hospital fall. Multivariable analysis revealed as significant clinical predictors for in-hospital mortality a Nutritional Risk Screening ≥2 (HR 1.79, 95% CI: 1.32–2.4), a falling incident during hospitalization (HR 3.50, 95% CI: 2.04–6.0), high MELD, and admission for infections. Conclusions: Malnutrition and TIPS are associated with falls in hospitalized patients with liver cirrhosis. The in-hospital mortality rate of patients with cirrhosis with falls is high. Specific attention and measures to ameliorate these risks are warranted.

Background: With the COVID-19 pandemic, a rapid adoption of telemedicine became necessary. Data regarding its implementation in specialized hepatology/IBD care remain limited. This study evaluated telemedicine’s effectiveness and safety during the pandemic at a German tertiary center and explored its integration into future hybrid care models. Methods: In a retrospective study, we analyzed 3147 patient encounters at the outpatient clinic of the Department for Gastroenterology and Hepatology at the University Hospital Frankfurt between March and June 2020. We assessed patient characteristics, appointment adherence, and outcomes across the three specialized clinics: hepatology (n = 1963), liver transplant (n = 594), and IBD (n = 590). Multivariate regression analysis identified predictors of successful telemedicine utilization. Results: Out of all appointments, 1112 (35.3%) were conducted via telemedicine, with significantly different adoption rates across clinics (hepatology, 40.4%; liver transplant, 32.8%; IBD, 21.0%, p < 0.01). Adherence rates were comparable between telemedicine (91.3%) and in-person visits (90.5%). Multivariate analysis identified age (OR 1.009, 95%CI 1.004–1.014, p < 0.001), metabolic-associated steatotic liver disease (OR 1.737, 95%CI 1.400–2.155, p < 0.001), and post-liver transplant status (OR 1.281, 95%CI 1.001–1.641, p = 0.049) as independent predictors of successful telemedicine utilization. HBV/HDV coinfection (OR 0.370, 95%CI 0.192–0.711, p = 0.003) and required endoscopy (OR 0.464, 95%CI 0.342–0.630, p < 0.001) were associated with in-person care. Hospitalization rates were low and comparable across modalities, confirming telemedicine’s safety. Conclusions: This study demonstrates that telemedicine can be successfully implemented in specialized gastroenterology and hepatology care, with high compliance rates comparable to in-person visits. Patient characteristics and disease-specific factors influence the suitability for telemedicine, supporting a stratified approach to hybrid care models, which can optimize resource utilization while maintaining quality of care. Particularly stable MASLD patients, well-controlled post-transplant recipients beyond one year, and IBD patients in sustained remission can be properly managed through telemedicine with annual in-person assessments.

Artificial Intelligence (AI) is rapidly transforming medical practice, with successful integration critically depending on healthcare professionals’ self-reported perceived knowledge, attitudes, and perceived barriers to adoption. Despite rapid technological advances, comprehensive assessments of healthcare professionals’ AI readiness remain limited, particularly regarding the relationship between enthusiasm and self-reported competency. We conducted a cross-sectional online survey of 148 healthcare professionals. The survey assessed demographics, AI knowledge/attitudes (12 Likert-scale items), institutional readiness, learning preferences, and perceived barriers. Inferential statistics including correlation analyses, Kruskal-Wallis H tests, and Mann-Whitney U tests were performed to examine relationships between attitudes and contextual factors. Participants (mean age 36.7 ± 8.1 years, 61.5% male, 77.7% from Germany) demonstrated a significant knowledge-enthusiasm gap. While 86.5% believed AI will transform medical practice and 76.4% expressed excitement about AI changes, only 20.3% felt well-informed about healthcare AI and 38.6% had medical AI experience. Correlation analysis revealed strong positive associations among enthusiasm measures (r = 0.63-0.88, p < 0.001) but weak correlations between knowledge and enthusiasm (r < 0.20), providing evidence consistent with the knowledge-enthusiasm gap. Institutional AI stance significantly affected individual knowledge levels (Kruskal-Wallis H(3) = 28.11, p < 0.001), but not enthusiasm. Primary barriers included knowledge deficits among leadership (62.8% institutional level), infrastructure limitations (52.0%), and system integration challenges (57.4%). Healthcare professionals, particularly in a German healthcare context, demonstrate strong enthusiasm for AI integration but face significant knowledge gaps and institutional barriers. While these findings might be most directly applicable to similar healthcare contexts, the identified knowledge-enthusiasm gap represents a critical target for educational interventions in similar well-resourced European healthcare systems. Successful AI implementation requires multi-level strategies addressing leadership education, infrastructure development, and hands-on training programs. These findings provide evidence-based guidance for healthcare institutions, educators, and policymakers developing AI adoption strategies.