NUM: Network of University Medicine

With the NUM Study Network (NUM SN), we want to develop an effective system of cooperation in the field of clinical and clinical-epidemiological studies in Germany. We work closely with existing infrastructures, networks and researchers and would like to support this process through organisational and structural measures. Study management should be relieved of administrative and operational tasks by consistently simplifying them and transferring them to study-supporting structures. This will allow more studies to be initiated in less time, more patients to be recruited for studies in a reliable period of time and the quality of data and biosample collection to be improved.

The NUM SN builds on the preliminary work of the National Pandemic Cohort Network (NAPKON), the NUM Clinical Epidemiology and Study Platform (NUKLEUS) and the NUM Data Integration Centres (NUM-DIZ). These established concepts and resulting infrastructures, organisational units and empirical values serve as the basis for a gradually growing, efficient network for the efficient implementation of clinical and clinical-epidemiological studies in Germany and will be rolled out uniformly among the participating network partners.

Centralized Contact Point for the UKR at the Zentrum für Klinische Studien (ZKS):

Dr. Marina Allgäuer
0941 944-5605
num-studiennetzwerk@ukr.de

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/platforms/num-study-network-num-sn
https://sn.netzwerk-universitaetsmedizin.de/

The Specialty Network Infectious Diseases (SNID) supports clinical and clinical-epidemiological studies in the field of infection medicine and aims to help strengthen the research landscape in Germany in the long term. It is the first specialist network in the NUM Study Network (NUM SN) and builds on the established structures of the National Pandemic Cohort Network (NAPKON) and the NUM Clinical Epidemiology and Study Platform (NUKLEUS).

Through close cooperation with university institutions, research networks and scientists, study-relevant processes in infection research will be further standardised and automated. Centralised platforms for automated feasibility analyses and patient screening are being expanded with an extended portfolio of typical queries in the field of infections. The aim is to implement studies faster and more effectively and to provide high-quality data and biosamples for research projects. This should create a scientifically sound basis for decision-making in medical care and enable new therapeutic approaches.

The Infection Network is focussing on baseline recruitment as part of a non-interventional umbrella master study protocol that bundles various infection areas under a common study design. The baseline cohort follows a broad recruitment strategy across five modules: Bloodstream Infections, Emerging Infections, Respiratory Infections, Gastrointestinal Infections and Central Nervous System (CNS) Infections. In addition, cross-sectional areas on infections in immunocompromised patients and infections in emergency and intensive care medicine are integrated. The targeted and dynamic prioritisation of certain pathogens and clinical pictures from these areas can be carried out as required by a decision of the responsible committees of the Infections Network.

The sites of the Infection Network have all the necessary organisational, personnel and technical requirements to carry out further infection studies from phase II and beyond, in accordance with the Clinical Trial and Medical Device Regulation, effectively and with quality assurance.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/specialty-network-infectious-diseases-snid

In a world in which medicine is benefiting from digital progress and ever-increasing amounts of data are being generated, routine data from medical care must be accessed efficiently, securely and in a way that promotes innovation, made available for medical research and used to answer medical questions. The NUM-DIC project builds on the preliminary work of the Medical Informatics Initiative (MII), within which Data Integration Centres (DIC) have been established at most German academic medical centres with the aim of supporting data provision and cross-site data integration and analysis. As part of the NUM-DIC project, the established DICs are continuously expanding their service portfolio and tapping into new data sources.

The academic medical centres, which were previously only involved in the MII as networking partners (and therefore had not yet established their own DIC), as well as some non-academic hospitals, are setting up the corresponding IT infrastructures and research data repositories and implementing the associated governance structures, data utilisation processes and data utilisation policies. In order to be integrated into the National Research Data Infrastructure Germany, both the already established and the newly emerging DICs connect to the central German Portal for Medical Research Data (FDPG) in order to automatically provide metadata on their data sets, answer feasibility queries and accept applications for data utilisation projects. If the DICs data are approved for a data use project by the local Use and Access Committees, the DIC make the requested patient cohorts/data sets available in harmonised FHIR format both for federated evaluations and for central analyses (if the corresponding patient consent has been obtained).

Further information:
Clinical Data Integration Centre at UKR
https://www.netzwerk-universitaetsmedizin.de/en/research/num-diz

RACOON is an infrastructure of the Network University Medicine (NUM) and was established to make medical image data quickly and efficiently usable for research. The aim is to create a platform that serves as a user-friendly and intuitive tool for radiologists and researchers from all over Germany. This will allow mono- or multi-centre studies with medical image data to be planned, carried out and evaluated more quickly.

The researchers are pursuing the vision of developing RACOON into the largest joint research platform for image-based medicine in Germany.

To this end, university institutions, state research institutes and external partners are working closely together: Together, they are developing solutions that process routine clinical data as well as specifically collected research data in such a way that they can be optimally utilised for modern analysis methods - in particular the use of Artificial Intelligence (AI).

RACOON thus supports the central goal of the NUM: a networked, nationwide Reseach Infrastructure that enables medical issues to be tackled jointly and effectively.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/platforms/racoon
https://racoon.network

How many patients come to the emergency departments every day? How urgently do they need to be treated and with what complaints did they visit the emergency departments? Unfortunately, this data is not yet available nationwide in Germany. With the AKTIN platform and the AKTIN Emergency Department Data Registry (EDDR), which are the result of a joint research project between Magdeburg University Hospital and the Institute of Medical Informatics at RWTH Aachen University Hospital, this information can now be recorded and made available in a decentralised manner in the participating hospitals. AKTIN stands for "Alliance for information and communication technology in intensive care and emergency medicine".

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/platforms/aktin
https://aktin.org/

The National Autopsy Network (NATON) brings together the expertise of university and non-university specialists in Germany who deal with autopsies and the analysis of post-mortem samples. The aim of NATON is to promote and support autopsy research in a variety of areas and to act as a platform for pandemic preparedness. Postmortem examinations have long been an important tool for quality assurance in medicine and can improve our understanding of the pathophysiology of infectious diseases, among other things. The added value of post-mortems became particularly clear during the COVID-19 pandemic. For example, autopsy-based research has shown that pulmonary (micro)vascular thromboembolism, systemic viral spread and the complex interplay between viruses and the immune system play a crucial role in severe COVID-19 disease and fatal outcomes.

NATON has been running as a sub-project of the research line since the start of the 2nd funding phase as a follow-up project to DEFEAT PANDEMIcs. The NATON infrastructure was established in January 2023 (as the "NATON 2.0" project).

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/platforms/naton
https://naton.network/

The "NUM Platform for Surveillance and Rapid Response" (NUM-SAR) strengthens the infrastructure of university medicine for pandemic-related research through knowledge creation, standardised data systems and comprehensive pathogen monitoring. The Reseach Infrastructure at NUM provides important information for pandemic prevention and complements existing laboratory structures of the Public Health Service (ÖGD). This forms the necessary basis for effective control and management of future pandemics. NUM SAR works together with the Robert Koch-Institute (RKI), public health structures and other NUM Reseach Infrastructures, among others.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/platforms/num-sar

The NUM Routine Data Platform (NUM-RDP) project aims to provide a generic routine data platform. "Routine data" here means clinical routine documentation data from patient care. In the first funding period, the NUM added the option of centralised, cross-institutional data consolidation, storage and output to the existing structures of the Medical Informatics Initiative (MII) for federated data storage and analysis. In future, this central data infrastructure will be expanded to include a data management centre in collaboration with the MII partners.

This enables the implementation of collaborative research projects in the medium term by receiving data on all core data set modules of NUM-RDP and the MII from the Data Integration Centres (DICs) and making it available for broad-consent-based* research projects. In addition, the NUM dashboard supports pandemic management with real-time centralised tracking of care expenditure and patient characteristics.

*Broad consent: Consent-based procedure in which patients are asked to consent to the subsequent use of their clinical data during their stay at the University Hospital on the basis of explanatory patient information.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/num-rdp

Clinical studies on infectious diseases:

•           sWITCH-VO (Early Switch to Oral Therapy in Vertebral Osteomyelitis)

•           PENGUIN (Penicillin-allergy delabeling in German University hospital Inpatients: A prospective multicenter study)

•           FOSFO-SNAP (Adjunctive fosfomycin therapy in patients with Staphylococcus aureus bacteraemia at high risk of complications or relapse)

•           CanTEN (Shortening treatment duration in candidaemia)

•           PREVENT (Prevention of bloodstream-infections with Vancomycin resistant Enterococci)
 

Establishment of clinical registries:

•           eLEVATE (Leveraging Autopsies to Advance Medical Research)
 

Use cases for the RACOON infrastructure:

•           RACOON-COMPARE (Comprehensive Multi-Center Analysis for Predictive Body Composition Reference Establishment)

•           RACOON-INCLUDED (Imaging Network for Childhood Acute Lymphoblastic Leukemia Using Data with Extended Diagnostics)

•           RACOON-LCS (Quality Assurance and Multicenter Evaluation of Lung Cancer Screening in Germany)

•           RACOON-MARDER (MRI and AI-assisted personalized HCC risk stratification in intermediate-stage liver disease for early intensified screening)

•           RACOON-PAIN (Precision Abdominal Imaging Network)

The aim of the COMPASS project was to establish a platform for the sustainable coordination of pandemic apps and to provide specific methods and tools for their implementation in line with the latest scientific, technical and legislative developments.

The nationwide approach of partners from science and industry contributes to sustainably anchoring the development and use of digital solutions in pandemic management through the coordinated collection, processing and evaluation of pandemic apps and the creation of recommendations for action.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/compass

Autopsies are an important medical tool for understanding infectious diseases such as COVID-19 and the gold standard for post-mortem diagnostics. This enabled important insights into the course of severe COVID-19 diseases to be uncovered early on during the pandemic.

The aim of DEFEAT PANDEMIcs was to establish a Germany-wide post-mortem network for the pandemic in order to collect and collate data, biomaterials and findings quickly, systematically and in a standardised manner as completely, comprehensively and promptly as possible and to make them available to the network partners for evaluation.

With the establishment of the DEFEAT PANDEMIcs platform, data, biosamples and findings were utilised for the management of the COVID-19 pandemic. The results have helped to achieve, among other things, a precise understanding of the body's pathological reactions in infectious diseases and improved diagnosis and treatment options. The unique networking of most pathological, neuropathological and forensic institutes of German university hospitals as well as non-university partners has contributed to a better understanding of Covid-19 research and patient care by establishing a permanent structure.

On this basis, after the end of the term, an organisational structure for autopsies was created that is prepared for future pandemics and can react quickly and in a coordinated manner across the board - the National Autopsy Network (NATON), which will be continued within the NUM from 2022.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/defeat-pandemics

With the National Pandemic Cohort Network (NAPKON), a sustainable network has been established that can collaborate quickly across disciplines and locations in the event of future medical challenges.

NAPKON has created a Reseach Infrastructure and three complementary cohort platforms that systematically record the acute and chronic course of COVID-19 in a modular and representative manner in subgroups. By working together with the infrastructure components, these platforms enable the consolidation of regionally collected scientific data, image data and biosample collections at a national level. This has enabled harmonised, prospective observation of COVID cases representative of the whole of Germany since autumn 2020.

In this way, a harmonised, expandable and national network has been established that offers the opportunity to support the fight against the current COVID-19 pandemic and its consequences as well as future pandemics.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/napkon-10
www.napkon.de

NAPKON is a nationwide research project that uses patient data (clinical data, image data, biosamples) of all degrees of severity and manifestations to map the acute and long-term COVID-19 disease (Post-COVID syndrome). The data, image data and biosamples are collected centrally in collaboration with the NUM infrastructure project NUKLEUS and made available worldwide for scientific research purposes. The aim is to generate treatment recommendations and prognosis findings from the analysis results, with a particular focus on the long-term consequences of COVID-19 disease.

NAPKON has laid the foundation for a national recruitment infrastructure that will facilitate, coordinate and promote coordinated collaboration between doctors and scientists, and in particular the leading care centres in Germany, even beyond the pandemic. The basis for this is the joint organisational form that has been developed, which includes a joint data protection concept, coordinated study protocols and standardised work instructions for data and biosample collection.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/napkon-20
www.napkon.de

Development of a nationwide standardised, data protection-compliant infrastructure for the storage and provision of COVID-19 research datasets. Among other things, a comprehensive database, data collection tools, use & access procedures and a trust centre are planned.

The infrastructure will be able to map complex COVID-19 research datasets, including clinical data, image data and data on biosamples, in a multi-centre, patient-related and pseudonymised manner and make them available to researchers.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/codex

In 2022, the CODEX+ project, which has already been completed, added technical and organisational aspects to the CODEX platform from the first funding phase, which has now been transferred to the NUM Routine Data Platform (RDP) infrastructure. This means that the successful solutions from various NUM projects can now be operated and used in a joint infrastructure of the university hospitals. CODEX+ developed generic components and concepts in order to be able to react quickly to new requirements in the future in terms of "pandemic preparedness". In addition, a consulting infrastructure was created for network partners who want to develop applications based on healthcare data and implement them in the network.

The UKR was involved in three specific use cases:

• Intensive Care (Prof. Dr. Dr. h. c. Martina Müller-Schilling)
• MII Consent in the Emergency Department (Prof. Dr. Frank Hanses)
• mHealth and eHealth (Prof. Dr. Winfried Schlee)

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/codex-1

Pandemic resilience requires efficient, interconnected and stringently regulated infrastructures for the continuous monitoring, assessment and anticipation of the pandemic situation. Coordinated infrastructures for structured, consensual data collection and modelling of the course of the pandemic, coordinated and rapid evidence synthesis and the direct and rapid subsequent derivation of multi-perspective recommendations are essential for this.

The overarching goal of PREPARED is to develop a concept for a comprehensively realisable, cooperative, adaptable and sustainable infrastructure for pandemic management and preparation within the NUM. This will enable a coordinated, rapid, targeted and evidence-based action and response to threats to patient care and public health due to a pandemic situation.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/prepared

The main goal of the already completed RACOON-COMBINE project was to develop and implement a pipeline for the extraction of COVID-specific, predictive and prognostic quantitative imaging biomarkers (C-QIBs) to enable comprehensive phenotyping not only of the disease but also of the patient, i.e. their physical condition and comorbidities. The predictive and prognostic information provided by C-QIBs will not only improve (i.e. individualise) patient treatment, but also improve our understanding of the different COVID-19 disease patterns and disease-specific organ crosstalk.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/racoon-combine

The RACOON-RESCUE study is a research project with the aim of improving the imaging staging of paediatric non-Hodgkin's lymphomas (NHL) by means of radiological diagnostics, avoiding invasive interventions to clarify residual structures and developing new image-based biomarkers.

The reference assessment of diagnosis and therapy according to standardised criteria for the 160 children with NHL each year ensures a high standard of care. In addition to invasive diagnostics such as biopsies and lumbar and bone marrow punctures, radiological procedures are an essential part of the diagnosis of the spread of the disease and essential for assessing the response. For paediatric NHL, however, these procedures have not yet been standardised or quantified and there is no reference assessment.

In RACOON-RESCUE, existing image data will be analysed in a structured manner in order to improve the staging of NHL, to better assess the vitality of residual structures under therapy and to identify and evaluate new image-based biomarkers. The project is based on image data already collected as part of routine clinical practice and the structured data basis of the NHL-BFM registry, which has been collecting clinical data on all paediatric patients with NHL in Germany since 2012.

Measurements are taken using the image data sets, radiological findings are subsequently collected and recorded in a structured form. Methods of image data analysis ("radiomics") and Artificial Intelligence (AI), including automated segmentation procedures, are used for this purpose. Based on these data sets, the stage is reclassified, the predictive value of the image parameters for the course of the disease is analysed and the image data is correlated with other clinical parameters. The parameters and models identified as relevant in this way can be made available via the RACOON infrastructure for future research projects and the further development of AI-supported diagnostic procedures. The project thus aims to create the prerequisites for establishing a structured reference radiological assessment of paediatric NHL and prospectively using new image-based biomarkers for all children and adolescents with NHL. In future, this could allow radiological diagnostics to be used in a more targeted manner, better classify previously unclear findings and reduce the use of invasive examination procedures.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/racoon-rescue

The aim of the project is to overcome the heterogeneity of the current telemedical infrastructure of German university hospitals and to create a standardised, telemedical collection of research data on COVID-19, with a focus on semantic and syntactic interoperability. In addition, an evidence-based guideline for telemedical care is to be developed.

Further information:
https://www.netzwerk-universitaetsmedizin.de/en/research/utn
www.utn-num.de