Introduction

Since 2029 national curricula for sedation and emergency management in endoscopy has established for endoscopy staff. NAPS are trained in three-day courses. After a baseline assessment in 2013, the effect of the training was evaluated in a survey in 2018.

Results & Findings

The questionnaire was completed by 356 endoscopy departments (95% in hospitals, 6% in private practice).

• Most departments operate in a multidisciplinary manner (85% gastroenterology, 59% TEE, 13% cardiology, 1.12% urology).
• In 52% of departments, 100% of staff have been trained, while 30% have trained 75-100% of staff. Refresher courses are completed every 2-5 years by 77% of units, while annual resuscitation courses are established in 93% od units.
• 98% have examination rooms equipped with BB, pulse oximeter, ECG, and oxygen supply. The second suction is available in 80%, and capnography in 9%. 83% have their own recovery room, 16% use other recovery rooms.
• ASA classification is used in 80% of unit for risk assessment, while Mallampati score is used in 7% only. 38% developed department specific scores. 63% perform the sign-in for all patients, 16% for 75-100% of patients. Only 10% of departments never perform a sign-in.
• 35% of units perform team-time-out (TTO) for all patients, 11% for 75-100% of patients. Only 26% of departments never use TTO.
• In 47% of departments, a dedicated person for patient monitoring and sedation is available. 30% have this person in 75-100% of patients, and 14% have this person in 25-50% of cases.
• Propofol mono sedation by bolus application is used in 88% of units, TCI in 17% of units, combination Propofol and Midazolam is used in 27%.
• In 77% of units, inpatients and outpatient are equally monitored in the recovery area while 21% of units only use the recovery area for outpatients.
• In 73% of units, physicians discharge the patient, in 12% nurses discharge patients.
• In 80% of departments, patients are only allowed to leave the endoscopy with an accompanying person.
• 70% of departments document all complications, but only 42% evaluate the complication rate statistically.
• 42% of units reported to have no problems to establish all requirements for sedation and patient monitoring. 37% reported about staff shortage. 12% could not find new staff, 25% reported about financial problems. 44% reported about resistance in their own department or with hospital management to establish improvements.

Summary & Discussion

• Comparing data from 2013, 2018 and 2024, improvements could be identified in the establishment of riss assessment by using ASA Score and individual checklists, Malampati score is still not well-known. The establishment of TTO increased from 20 to 35% in all patients, but still needs to be improved.
• The availability of a sedating person remains unchanged with 40 to 47% in all patients and 30% in 75-100% of patients. The recovery situation remains unchanged.

Conclusion

Staff shortage and resistance within the own team are the main challenges when establishing safe sedation and patient monitoring in Endoscopy.

Disclosure

no conflict of interest

Introduction

Lymphocytes inhibit the progression of hepatocellular carcinoma (HCC) through immune surveillance within an immunosuppressive microenvironment. Reduced lymphocyte counts, often associated with systemic inflammation, have been implicated in facilitating tumor progression. In contrast, elevated monocyte counts have been reported to be associated with microvascular invasion and poor prognosis in patients with HCC. Therefore, a high lymphocyte-to-monocyte ratio (LMR), calculated by dividing the lymphocyte count by the monocyte count, is considered a favorable prognostic indicator and is associated with improved clinical outcomes in patients with HCC.

Aims & Methods

We assessed the utility of the LMR in predicting the prognosis of patients with unresectable hepatocellular carcinoma who were treated with atezolizumab plus bevacizumab (Atezo/Bev). The study involved 941 patients who received Atezo/Bev treatment between September 2020 and February 2025. The median age was 74.0 years, and 754 were male. Performance status (PS) was distributed as follows: 741 patients with PS 0, 169 with PS 1, and 31 with PS ≥2. The liver background conditions were as follows: 149 patients with hepatitis B, 282 with hepatitis C, and 449 with non-hepatitis B or C (NBNC). Child-Pugh scores were distributed as follows: 522 patients with a score of 5, 254 with a score of 6, 67 with a score of 7, and 37 with a score of ≥8. The Barcelona Clinic Liver Cancer (BCLC) stages were as follows: 77 patients with stage ≤A, 317 with stage B, and 547 with stage ≥C. The median values of key biomarkers were as follows: LMR: 3.33, NLR: 2.63, AFP: 34.1 ng/mL. The median observation period was 14.3 months. NLR was included in Model 1, and LMR was incorporated in Model 2 of the multivariate analysis.

Results

The progression-free survival (PFS) was 6.8 months, and the overall survival (OS) was 23.6 months. The optimal cutoff for LMR, determined via time-dependent receiver operating characteristic (ROC) analysis based on median OS, was 3.6. The objective response rate (ORR) and disease control rate (DCR) were 27.3% and 76.5%, respectively. In the multivariate analysis of PFS, Model 1 identified the following factors as significant: BMI (HR: 0.98), Child-Pugh score ≥7 (HR: 1.34), BCLC stage C or higher (HR: 1.23), Log AFP (HR: 1.16), second or later treatment line (HR: 1.19). In Model 2, LMR was included as a significant factor (HR: 0.94). For OS, the following factors were identified as significant: age (HR: 1.01), Child-Pugh score ≥7 (HR: 2.09), BCLC stage C or higher (HR: 1.24), Log AFP (HR: 1.28), second or later treatment line (HR: 1.24), and NLR (HR: 1.03). The integrated discrimination improvement (IDI) for NLR and LMR at PFS (at 3 and 6 months) was 0.012 (95% CI: 0.012). The net reclassification improvement (NRI) was 0.016 (95% CI: 0.006-0.030, p<0.001) at 3 months, 0.023 (95% CI: 0.006-0.030, p<0.001) at 6 months, and 0.023 (95% CI: 0.004-0.024, p<0.001) at 12 months. The IDI for NLR and LMR at OS (12 and 24 months) was 0.027 (95% CI: 0.011-0.049, p=0.002) and 0.034 (95% CI: 0.012-0.064, p<0.001), with LMR demonstrating superior performance. The NRI for OS at 12 and 24 months was 0.036 (95% CI: 0.016-0.062, p<0.001) and 0.053 (95% CI: 0.020-0.091, p<0.001), respectively, both favoring LMR. In the time-dependent ROC analysis, the area under the curve (AUROC) was higher for LMR compared to NLR throughout the study period, with the exception of PFS at 3 months and OS at 18 months.

Conclusion

The LMR serves as a valuable biomarker for predicting prognosis in patients receiving Atezo/Bev.

Disclosure

Toshifumi Tada: lecture fees from AbbVie, AstraZeneca, Eisai, and Chugai
Atsushi Hiraoka: lecture fees from Eisai, Bayer, Eli Lilly, and Otsuka
Hidenori Toyoda: lecture fees from AbbVie, Eisai, Gilead, Terumo, and Bayer

Introduction

Inflammatory Bowel Disease (IBD)^1,2 requires close monitoring to detect flare ups early and guide treatment. While endoscopy is the gold standard, it is invasive, costly, and uncomfortable^1,2. Intestinal Ultrasound (IUS) is a non-invasive alternative with strong correlation to endoscopic findings—especially bowel wall thickness (BWT), a marker of disease activity³. Nevertheless, IUS remains limited due to operator dependence and a steep learning curve⁴.
AI has improved consistency in other domains, e.g. endoscopic evaluation, but its application to IUS in IBD remains underexplored. Prior models lack interpretability and require manual image cropping, or are trained on ideal, selected data, limiting clinical use^5,6.

Aims & Methods

We aim to develop a deep-learning model that automatically identifies and paints the bowel wall and measures BWT directly from raw clinical IUS images.
A training dataset of 570 images from 144 IUS videos and a testing dataset of 55 images from 55 separate exams were created. All images were extracted from previously performed IUS examinations reflecting real-world variation, including e.g. unclear boundaries and artefacts.
All images were annotated by International Bowel Ultrasound Group (IBUS) certified experts, including outline-paintings of the inner and outer bowel wall and two BWT measurements.
The AI consists of a combination of Convolutional neural networks and other image processing algorithms.
Evaluation included BWT error against the expert mean, classification accuracy using the standard IBUS 3 mm threshold³, and a leave-one-out comparison with individual doctors.

Results

The model produced predictions on 54 of 55 test images.
The model deviated from the gold standard mean by 0.98 mm (SD 1.1 mm) per image on the regression task. The average distance to expert-defined bounds was 0.44 mm (SD 0.89 mm), with 59% of predictions inside this range.
For classification (using a 3 mm threshold), the model reached an accuracy of 0.77, sensitivity of 0.69, specificity of 0.94, and Cohen’s Kappa of 0.56.
In the leave-one-out analysis, expert performance ranged from 0.89–0.93 accuracy and 0.79–0.83 Kappa. Depending on which expert was excluded from the gold standard, the model achieved 0.74–0.80 accuracy and 0.49–0.60 Kappa. Experts stayed within expert-defined bounds in 72–81% of cases, while the model did so in 46–55%.

Conclusion

Clinically, the model performed well. An error of 0.5–1 mm is negligible in practice and matches typical variation in manual measurements. Many real-world test images had BWT values near the 3 mm threshold, so small deviations led to misclassifications. In practice, a 2.5 mm reading could still raise concerns based on symptoms³.
While the system alone cannot provide expert-level BWT measurements, it could provide assistance to experts as well as non-expert and junior doctors, especially in locating the bowel, which is an essential part of IUS.
The model works on unprocessed clinical data, with the only selection criteria being that there is an identifiable bowel segment, measurable by an IBUS-certified doctor. Allowing the images to reflect realistic conditions
In summary, we developed an AI model that identifies the bowel and measures BWT with acceptable accuracy. We have already started the processes of collecting video data, to extend the AI’s functionality to a fully clinical setting.

References

1. Torres J, Mehandru S, Colombel JF, Peyrin-Biroulet L. Crohn’s disease. The Lancet. 2017;389(10080):1741-1755. doi:10.1016/S0140-6736(16)31711-1
2. Ungaro R, Mehandru S, Allen PB, Peyrin-Biroulet L, Colombel JF. Ulcerative colitis. Lancet. 2017;389(10080):1756-1770. doi:10.1016/S0140-6736(16)32126-2
3. Novak KL, Group I, Nylund K, et al. Expert Consensus on Optimal Acquisition and Development of the International Bowel Ultrasound Segmental Activity Score [IBUS-SAS]: A Reliability and Inter-rater Variability Study on Intestinal Ultrasonography in Crohn’s Disease. J Crohns Colitis. 2021;15(4):609-616. doi:10.1093/ECCO-JCC/JJAA216
4 Bove L, Meyer J, Collins M, Frampas E, Bourreille A, Le Berre C. Understanding the learning curve of intestinal ultrasound in inflammatory bowel disease: A comparative study between novice, regular, and expert. Clin Res Hepatol Gastroenterol. 2025;49(3):102548. doi:10.1016/J.CLINRE.2025.102548
5. Kumaralingam L, Le May K, Dang VB, Alavi J, Huynh HQ, Le LH. Artificial intelligence-assisted approach to assessing bowel wall thickness in pediatric inflammatory bowel disease using intestinal ultrasound images. J Crohns Colitis. 2025;19(4). doi:10.1093/ECCO-JCC/JJAF037
6. Carter D, Albshesh A, Shimon C, et al. Automatized Detection of Crohn’s Disease in Intestinal Ultrasound Using Convolutional Neural Network. Inflamm Bowel Dis. February 2023:1-6. doi:10.1093/IBD/IZAD014

Introduction

Current therapies for inflammatory bowel disease (IBD) suppress inflammation rather than inhibiting underlying microbial drivers. Treatment failures of frontline interventions, including corticosteroids, can lead to dose escalation and risk of adverse effects.^1,2 Bacteria with pro-inflammatory potential, like adherent-invasive Escherichia coli (AIEC), have been postulated as a possible microbial driver in Crohn’s disease (CD), however, targeting these bacteria with precision in IBD remains challenging.^3,4 Most antibiotics have broad-spectrum activity and disturb the background microbiome, which may exacerbate inflammation.⁵ Attention has shifted towards bacteriophages, yet their underlying mechanisms of treating IBD, and how they may work as an adjunctive therapy, remains elusive.

Aims & Methods

We aimed to assess whether and how phage therapy, specific for a CD-associated bacterium, reduces colitis severity using gnotobiotic mouse models.
In vitro kill curves and biofilm challenges were used to identify lytic phage candidates. Adult germ-free C57BL/6 mice were colonized with altered Schaedler-flora (ASF) and E. coli NRG857c (NRG), a CD-associated isolate. After 3 weeks, mice were treated with an NRG-specific lytic phage (1x10⁹ PFU/dose; daily or TIW) or vehicle (PBS) for 2 weeks (n=6/group) and then exposed to 1 cycle of dextran sulfate sodium (2%; DSS) in drinking water. PBS-treated mice (n=6) not exposed to DSS served as controls. Phage treatment was also tested in a spontaneous model of colitis using C57BL/6NTac-Il10^em8Tac (IL-10^-/-) mice, treated weekly. Mice were monitored daily for weight, stool consistency, and occult blood. Fecal contents were cultured to determine bacterial load, and the expression of NRG fimS was quantified using qPCR as an assessment of virulence. At endpoint, colon tissues were collected for histological and immunohistochemistry analysis to quantify colitis severity and NRG infiltration, respectively. Additional C57BL/6 mice were colonized and treated as previously described. During colitis induction, mice received a low-dose of budesonide (2μg/day)⁶ (n=5) or vehicle (n=5) until endpoint. Mice were monitored daily for disease activity and colon tissues were collected for histological analysis. HPLC was used to determine active budesonide levels.

Results

HER259 was identified as a lytic phage against CD-associated AIEC NRG. In ASF and NRG colonized mice, HER259 reduced clinical symptoms (p< 0.001, p< 0.001) and histological scores (p< 0.0001, p< 0.001) in chemical induced and spontaneous colitis, respectively. A 1-log reduction in NRG bacterial load was observed in HER259-treated mice (p< 0.001). Reisolated NRG exhibited a reduced FimS ON/OFF ratio (p-value < 0.01). Immunohistochemistry revealed reduced infiltration of NRG into the lamina propria following HER259 treatment, with greater accumulation in the epithelium. HER259 treatment increased the efficacy of a sub-therapeutic dosing regimen of budesonide (p< 0.001), which was independent of microbial-based drug metabolism as determined by HPLC.

Conclusion

Our results demonstrate that lytic phage targeting a CD-associated bacterium attenuates colitis in two gnotobiotic mouse models. The intervention did not lead to complete eradication of the disease-driving agent, but instead modulated bacterial virulence mechanisms and enhanced responses to corticosteroid therapy. The data suggests that alternative mechanisms should be considered when translating phage therapy to treat IBD in clinical trials.

References

1. Yan, Y. et al. Commensal bacteria promote azathioprine therapy failure in inflammatory bowel disease via decreasing 6-mercaptopurine bioavailability. Cell Rep Med 4, 101153 (2023).
2. Nagayama, M., Gogokhia, L. & Longman, R. S. Precision microbiota therapy for IBD: premise and promise. Gut Microbes 17, 2489067 (2025).
3. Darfeuille-Michaud, A. et al. High prevalence of adherent-invasive Escherichia coli associated with ileal mucosa in Crohn’s disease. Gastroenterology 127, 412–421 (2004).
4. A, D.-M. et al. Presence of adherent Escherichia coli strains in ileal mucosa of patients with Crohn’s disease. Gastroenterology 115, 1405–1413 (1998).
5. Nelson, R. L., Suda, K. J. & Evans, C. T. Antibiotic treatment for Clostridium difficile-associated diarrhoea in adults. Cochrane Database Syst Rev 3, (2017).
6. Bercik, P. et al. Chronic gastrointestinal inflammation induces anxiety-like behavior and alters central nervous system biochemistry in mice. Gastroenterology 139, (2010).