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The German Emergency Department Data Registry (GEDD-registry, AKTIN) provides an infrastructure for collecting and querying up-to-date medical records in a distributed manner. Within this framework, a benchmark report on cross-institutional comparison using the program R is prepared using routine data of participating hospitals. Currently, 16 emergency departments (EDs) routinely transfer data of 1,200 to approximately 5,000 patients per month to a federated GEDD-registry datawarehouse. Using various packages in the R environment, hospitals receive a monthly visual report on their data among all participating hospitals. Graphical representations are implemented using column diagrams and box plots. Reports currently contain 25 tables and 40 graphs. Benchmark reports are created in R-Studio and exported using Portable Document Format, PDF. Quarterly expert meetings with the heads of participating EDs are currently performed for further improvements. Preparation of external benchmarking reports with R enables a detailed data presentation for participating hospitals and ED managers. [ABSTRACT FROM AUTHOR]

The complexity of emergency cases and the number of emergency patients have increased dramatically. Due to a reduced or even missing specialist medical staff in the emergency departments (EDs), medical knowledge is often used without professional supervision for the diagnosis. The result is a failure in diagnosis and treatment, even death in the worst case. Secondary: high expenditure of time and high costs. Using accurate patient data from the German national registry of the medical emergency departments (AKTIN-registry, Home - Notaufnahmeregister (aktin.org)), the most 20 frequent diagnoses were selected for creating explainable artificial intelligence (XAI) models as part of the ENSURE project (ENSURE (umg.eu)). 137.152 samples and 51 features (vital signs and symptoms) were analyzed. The XAI models achieved a mean area under the curve (AUC) one-vs-rest of 0.98 for logistic regression (LR) and 0.99 for the random forest (RF), and predictive accuracies of 0.927 (LR) and 0.99 (RF). Based on its grade of explainability and performance, the best model will be incorporated into a portable CDSS to improve diagnoses and outcomes of ED treatment and reduce cost. The CDSS will be tested in a clinical pilot study at EDs of selected hospitals in Germany.

Competing Interests: Disclosure The authors report no conflicts of interest in this work.

The German Emergency Department Data Registry (GEDD-registry, AKTIN) provides an infrastructure for collecting and querying up-to-date medical records in a distributed manner. Within this framework, a benchmark report on cross-institutional comparison using the program R is prepared using routine data of participating hospitals. Currently, 16 emergency departments (EDs) routinely transfer data of 1,200 to approximately 5,000 patients per month to a federated GEDD-registry datawarehouse. Using various packages in the R environment, hospitals receive a monthly visual report on their data among all participating hospitals. Graphical representations are implemented using column diagrams and box plots. Reports currently contain 25 tables and 40 graphs. Benchmark reports are created in R-Studio and exported using Portable Document Format, PDF. Quarterly expert meetings with the heads of participating EDs are currently performed for further improvements. Preparation of external benchmarking reports with R enables a detailed data presentation for participating hospitals and ED managers.

Digitalization and standardization of documentation in medicine are increasingly progressing. In the decision of the Federal Joint Committee (G-BA) for a staged system of emergency structures in hospitals and in the report of the expert committee for development in the healthcare system on needs-oriented guidance of healthcare, the actuality of the topic is underlined with concrete naming of future tasks. The section on emergency admission protocols of the German Interdisciplinary Association of Intensive and Emergency Care Medicine (DIVI) has been working for years on this topic and has repeatedly reported on the progress of the topic in clinical emergency care. Standardization and digitalization represent the foundation for health services research spread across locations as well as the possibility for benchmarking. Digitalization makes the secondary use of primary clinical routine data possible. Digitalization decreases redundancies of data transmission by avoiding manual data input in, for example registers.

Aim: To evaluate the frequency of Helicobacter pylori ( H. pylori ) CagA antibodies in H. pylori infected subjects and to identify potential histopathological and bacterial factors related to H. pylori CagA-immune response., Methods: Systematic data to H. pylori isolates, blood samples, gastric biopsies for histological and molecular analyses were available from 99 prospectively recruited subjects. Serological profile (anti- H. pylori , anti-CagA) was correlated with H. pylori isolates ( cagA , EPIYA, vacA s/m genotype), histology (Sydney classification) and mucosal interleukin-8 (IL-8) mRNA and protein expression. Selected H. pylori strains were assessed for H. pylori CagA protein expression and IL-8 induction in co-cultivation model with AGS cells., Results: Thirty point three percent of microbiologically confirmed H. pylori infected patients were seropositive for CagA. Majority of H. pylori isolates were cagA gene positive (93.9%) with following vacA polymorphisms: 42.4% vacA s1m1 , 23.2% s1m2 and 34.3% s2m2 . Anti-CagA-IgG seropositivity was strongly associated with atrophic gastritis, increased mucosal inflammation according to the Sydney score, IL-8 and cagA mRNA expression. VacA s and m polymorphisms were the major determinants for positive ( vacA s1m1) or negative ( vacA s2m2) anti-CagA serological immune response, which also correlated with the in vitro inflammatory potential in AGS cells. In vitro co-cultivation of representative H. pylori strains with AGS cells confirmed functional CagA translocation, which showed only partial correlation with CagA seropositivity in patients, supporting vacA as major co-determinant of the immune response., Conclusion: Serological immune response to H. pylori cagA + strain in H. pylori infected patients is strongly associated with vacA polymorphism, suggesting the crucial role of bacterial factors in immune and clinical phenotype of the infection., Competing Interests: Conflict-of-interest statement: The authors have no conflicts to declare.