researchPageHeading

The project AlPaCa (Algorithms in Patient Care) is creating an algorithm within the Medical Clinic II of the University Hospital Frankfurt for the identification of hemato-oncology patients who develop life-threatening febrile neutropenia of unknown origin (FUO). The aim is the rapid and quality-assured identification of patients in order to ensure guideline-compliant treatment and, if necessary, to initiate further diagnostic and therapeutic steps. The algorithm is based on both internal and national guidelines and is intended to speed up the routine screening of patients. The algorithm will be developed based on retrospective patient data and then evaluated and implemented with prospective cases, in order to support clinical care. The project is led by the Institute and funded by the innovation and structural development budget of the state of Hessen (Germany) as part of the application for the creation of a “Comprehensive Cancer Center (CCC) Hessen”.

The CELESTIAL study is a multicentre, retrospective study in Germany that investigates clinical experience with letermovir for CMV prophylaxis in hematology and oncology patients who underwent allogeneic hematopoietic cell transplantation between 2018 and 2021. Clinical as well as health-economic parameters are compared within a 48-week follow-up between the periods before and after the approval of letermovir in 2018. The study center in Frankfurt participated in the trial, while overall responsibility for the project was hold by the University Hospital Cologne.

This study is supported by an unrestricted research grant from MSD Sharp & Dohme GmbH (Germany).

DigiONE I3: ERDF I3-funded DIGItal Infrastructure for ONcology in Europe

The DigiONE I3 project builds on the infrastructure established in the DigiONE pilot project and expands it through the participation of additional centres into a federated European platform. It connects routine clinical care records with extended diagnostics and outcomes data. With enhanced privacy safeguards, GDPR compliance, and automation, its primary aim is to strengthen care quality and digital oncology research through pragmatic precision medicine trials and real-world evidence (RWE) generation. The project aims to achieve Europe-wide interoperability of oncology data, establish MEDOC (Minimum Essential Description of Cancer) as a common research dataset, build a GDPR-compliant infrastructure for sensitive patient data, and strengthen sustainable research capacities and business models. The project is coordinated by DigiCore and involves 15 hospitals, 8 technology vendors, and 1 non-profit organisation across 9 EU countries.

Type of participation: In Frankfurt, the institute participates in close collaboration with the University Cancer Center (UCT) and the Institute of Medical Informatics (IMI).
Funding status: Funded by the European Regional Development Fund
Project duration: November 2023 – April 2026 (30 months)

The project DigiONE (DIGItal Oncology Network for Europe) is an industry-funded pilot project aiming to build a federated digital research network. It links high-quality routine clinical data with molecular data from six leading cancer centres in Europe: Frankfurt, Leeds, Maastricht, Oslo, San Raffaele (Milan), and Saint-Luc (Brussels).

The digital infrastructure is based on the international Minimal Essential Description of Cancer (MEDOC) consensus, providing near real-time standardized descriptions of diagnoses, biomarkers, treatments, and outcomes. MEDOC covers the full patient journey from diagnosis to outcome and includes all major inclusion and exclusion criteria, creating a unique resource for high-quality real-world evidence (RWE) and care quality management.

For harmonisation and analysis, interoperability standards and tools from the Observational Health Data Sciences and Informatics (OHDSI) community are applied. Privacy-preserving analysis is performed in a federated way using Vantage6, enabling large-scale clinical research without centralising sensitive patient data.

In Frankfurt, the project is conducted in collaboration with the University Cancer Center (UCT), the Institute of Medical Informatics (IMI), and the Institute of Digital Medicine and Clinical Data Science (IDMKD).

Type of participation: Project participation (Project lead: DigiCore Europe).
Funding: Industry-funded pilot project, supported by IQVIA and Illumina.

The Dr. LLM study evaluates the diagnostic potential of large language models (LLMs) in infectious disease medicine, focusing on accuracy, reliability, and effectiveness. Anonymized, transformed clinical cases are prompted into LLMs to generate a differential diagnoses list, which is then compared to those made by physicians. The study also examines how factors such as different models, prompt parameters and case text structure influence outcomes, aiming to develop practical recommendations for LLM use in clinical settings.

The Research IT working group is working on harmonizing processes and IT systems between the German Centers for Health Research (DZG). The aim is to collect data in accordance with the FAIR principle (Findability, Accessibility, Interoperability, Reusability). In this project, the tasks and contributions of the German Center for Infection Research (DZIF) are being planned and implemented.

To ensure Europe's vaccine readiness and responsiveness in the event of a pandemic, the European Vaccine Hub (EVH) is being created by pooling existing nationally funded investments in vaccine research and development into a collaborative network for end-to-end vaccine delivery. The project brings together leading EU organizations directly involved in vaccine development and responsible for pandemic preparedness in their respective countries: Biotecnopolo, Institut Pasteur, Vaccinopolis (UAntwerp), DZIF, and ZEPAI.

As part of WP3, the IDMKD is responsible for data management and setting up the technical project infrastructure. Document management, communication, and (automated) data analysis are part of our Tasks. In addition, scientists will be provided with a platform where they can independently perform and evaluate their data.

The Study Network Infectious Diseases (SNID) is the first use case within the NUM Study Network of the Network University Medicine (NUM) and is specifically designed to strengthen clinical and clinical-epidemiological research in infectious diseases.

Its aim is to establish a high-performance recruitment platform for three categories of studies:

• Since May 2025: Internal studies such as the SNID basic cohort, which recruits patients from the modules respiratory infections, bloodstream infections, gastrointestinal infections, central nervous system infections, and emerging infectious diseases, as well as the international platform study SNAP and RAPID-REVIVE

• From February 2026: Additional NUM-funded studies, including sWITCH-VO, PENGUIN, FOSFO-SNAP, CanTEN, PRÄVENTIV, and ELAPSE

• Planned: Externally funded academic and commercial studies

A key element is the development of prescreening programmes using automated tools to reliably identify patients at study sites. These programmes support feasibility assessments, increase participation rates, and enable early detection of shifts in disease patterns relevant for pandemic monitoring.

Data and biosamples collected within SNID will be made available to the scientific community upon request, providing a robust basis for further research to improve diagnostics, therapy, and prevention in infectious diseases.

Duration: July 2024 – June 2030

Funding: Funded by the Network University Medicine (NUM) and the German Federal Ministry of Education, Research, Technology and Space (BMFTR)

Duration: July 2024 – June 2030

Project Leads: Prof. Dr. Janne Vehreschild (Scientific Lead) and Dr. Margarete Scherer (Operational Lead)

Website: sn.netzwerk-universitaetsmedizin.de

The Fidaxomicin Registry is a multicentre, retrospective study in Germany comparing the use of fidaxomicin vs. vancomycin in immunocompromised patients with recurrent/refractory Clostridioides difficile infection between the years 2013 and 2023. The aim is to analyse the clinical and health economic parameters and outcomes in a 90-day follow-up. The study center in Frankfurt participated in the trial, while overall responsibility for the project was hold by the University Hospital Cologne.

This study is supported by an unrestricted research grant from Tillotts Pharma GmbH (Germany).

FREDA is an R package suite for curating and augmenting routine clinical oncology data, developed as part of the DataSHIELD framework. The software simplifies and standardizes data harmonization, curation, and augmentation processes in a federated analysis environment and is currently used in analysis projects by the Clinical Data Science Group of the Clinical Communication Platform of the German Consortium for Translational Cancer Research (DKTK).

FREDA is a comprehensive framework consisting of three R packages: dsFreda, which is used on the server side; dsFredaClient, which can be used to control the execution of server-side functions from the client side; and FredaApp, an R Shiny application that provides visual support for monitoring the initiated processes.

The long-term goal of FREDA is to establish a standard for data harmonization, curation, and augmentation in federated analysis structures that can be used for clinical data from different sources.

The project is led by Bastian Reiter.

In the GENIOBA project, the IDMKD supports the implementation of a systematic review (“Systematic Review on the Gender Gap in the Treatment of Patients with Impulse Control Disorders”) conducted by Ruhr University Bochum using multiple Large Language Models (LLMs). These models are specifically applied for title and abstract screening to efficiently process a large text corpus according to predefined inclusion and exclusion criteria. The goal is to implement LLM-assisted co-screening that complements human efforts, while also enabling the evaluation and benchmarking of various LLMs within this process.

BMFTR funding identifier: 01GN2513

The projects investigates the clinical epidemiology and stationary care of patients with cancer and HIV infection. We use clinical claims data to infere the site-specific incidence patterns and longitudinal trajectories of AIDS defining and virus-associated malignancies as well as the situation of care. Study sites are the university hospitals in Frankfurt, Freiburg and the LMU in Munich.

The aim of this project is to present the Institute for Digital Medicine and Clinical Data Sciences as an independent online presence at https://idmcd.de. The website is also part of the Centre for Digital Health and is integrated into the overall offerings of Goethe University Frankfurt (Faculty of Medicine, Department 16) through this institution >> https://www.uni-frankfurt.de

The content focuses on the IDMCD team, research projects, and scientific publications – in addition, the institute will be presented from both a structural perspective (organisational structure) and in terms of its history, philosophy, vision/mission, and values.

Infektiopedia, an initiative of the German Society for Infectiology (DGI), is a web-based, independent knowledge database designed to provide a clear and comprehensive overview of infectious disease guidelines. Over 100 volunteer authors contribute quality-assured, open-access content, compiling extensive information. Infektiopedia aims to empower physicians to make informed, evidence-based, and optimized decisions regarding patient care, particularly concerning the treatment of infectious diseases. The website is accessible at https://infektiopedia.de.

Infektiopedia is sponsored by the German Society for Infectious Diseases (DGI).

The isavuconazole registry is a multicentre, retrospective study in Germany investigating the use of isavuconazole vs. voriconazole and/or liposomal amphotericin B in possible, probable and proven invasive aspergillosis (IA) and mucormycosis (IM). Patients with underlying haematological/oncological diseases who were treated for IA or IM between 2016 and 2021 were included. The aim is to analyse epidemiological factors as well as health economic aspects in the real-world setting. The study center in Frankfurt participated in the trial, while overall responsibility for the project was hold by the University Hospital Cologne.

This study is supported by an unrestricted research grant from Pfizer Pharma GmbH (Germany).

The study “Prevalence and distribution of comorbidities in patients with colorectal cancer: A multicenter, retrospective observational study” is conducted with support of the DKTK Clinical Data Science Group in collaboration with the Clinical Communication Platform (CCP) of the German Cancer Consortium (DKTK). Four German university hospitals participate (Frankfurt, Hanover, Munich LMU and Mannheim).

The aim of the study is to investigate the distribution, burden, and profiles of comorbidities in patients with colorectal cancer (CRC) and their impact on therapies and outcomes (especially overall survival). Although comorbidities are common in patients with CRC, there is little data available in Germany. To carry out the project, two different data sources (tumor documentation data and service billing data according to §21 KEntG) are linked and fed into a federated analysis via the CCP infrastructure. The analysis will be stratified along age groups, sex and tumor stage at the time of diagnosis.

The study is led by Dr. Daniel Maier (Frankfurt) together with PD Dr. Karin Berger-Thürmel (Munich LMU). Other participating site partners are Selina Becht (Munich LMU), Prof. Dr. Tianzuo Zhan (Mannheim), and Dr. Fabian Ecke (Hanover).

The aim of the National Post-COVID Data Set (NAPCODE) is to summarize the extensive data collected during the COVID-19 pandemic, which is helpful for research into the post-COVID condition (PCC), and to make it available for research. NAPCODE is developing a specially tailored data set from existing study data for this purpose and can thus accelerate PCC research. The project is based on data from the National Pandemic Cohort Network (NAPKON), a Germany-wide cohort study with three study populations. In a first step, relevant variables are identified through a literature search and an exchange with experts, affected persons and their relatives. Subsequently, a pseudonymized data set will be created that can be applied for via a use and access procedure. In addition, two publicly available variants will be developed: an anonymized and a synthetic version.

BMFTR grant No.: 01EQ2406A

Project duration: 01.01.2025-31.12.2026

Das Nationale Pandemie Kohorten Netz (NAPKON) schafft gemeinsam mit weiteren Komponenten des Netzwerks Universitätsmedizin (NUM) grundlegende Infrastrukturen für das erfolgreiche Verständnis und damit für die Bekämpfung von Pandemien am Beispiel der Coronavirus-Krankheit-2019 (COVID-19).

https://napkon.de/

The Dynamic Use & Access Coordination Unit (DUA) is a work package of the infrastructure NUM Clinical Epidemiology and Study Platform (NUKLEUS) within the Network University Medicine (NUM) and is funded by the Federal Ministry of Research, Technology and Space (BMFTR). 

The scientific lead is Prof. Dr. Janne Vehreschild, while Shimita Raquib coordinates the operational level. The team is further supported by Patricia Wagner, and Tom-Robin Raja.

Originally developed as a subunit of the NUKLEUS work package Interaction Core Unit (ICU) in the second funding phase of NUM (grant no. 01KX2121), the DUA has been continued and expanded as an independent work package since the beginning of the NUM 3.0 funding phase in 07/25.

The aim of NUKLEUS DUA is to establish a quality-assured and transparent process to ensure access to data, biosamples, imaging data and other data types within NUM through a digital platform solution. The coordination unit is responsible for the central communication and coordination tasks, while decision-making entities, consisting of representatives of the data-providing projects, the organ specific work-groups (FOSA), methodological and ethical experts, patient representatives, and further members of the NUM Community are dynamically involved.

The procedure ensures that data are made available for research in a sustainable and efficient way. At the same time, it supports the implementation of the FAIR principles, particularly regarding Accessibility and Reusability.

The Resource Utilization and Performance Assessment Unit (RPA) represents one of the work packages of the Network University Medicine (NUM) subproject NUM Clinical Epidemiology and Study Plattform (NUKLEUS) and has been funded by the Federal Ministry of Research, Technology  and Space (BMFTR) since July 2025. Prof. Dr. Janne Vehreschild is the scientific lead of RPA and Dr. Susana Nunes de Miranda coordinates the operational level, where the RPA has been divided into two teams. The Accounting Team is led by Dr. Susana Nunes de Miranda, with Chin ‘Branson’ Huang Lee, Markus Katharina Brechtel, and Patricia Wagner beeing part of the team. The Lighthouse Team is led by Dr. Meta Bönniger, with Samia Goraya, and ‘Branson’ Chin Huang Lee belonging to the team.

During the current NUM 3.0 funding phase, the NUKLEUS RPA continues and optimizes the tasks of the NUKLEUS 2.0 Interaction Unit (ICU) Accounting Unit. These include 1) transparent cost calculation of case fees and infrastructure services using our new developed software tool NUM Study Planner, in order to enable a more efficient planning of new studies, 2) timely execution of productive or virtual accounting runs using our developed generic Accounting Tool, enabling an accurate validation of current study achievements, 3) transparent measurement of the quality and performance attained by the participating study centres, based on defined quality and performance indicators, and 4) display of the accounting and performance results using our new developed web interface NUM Lighthouse, in order to ensure a timely insight and regular monitoring of study achievement results by the respective study centres and project coordination.

In work package 5 of the NUM 3.0 Accompanying Project, the successful components of the NUKLEUS 2.0 Interaction Core Unit will be set in the context of NUM. These include the established platforms NUM Hub and NUM Community Platform, as well as the Cross-Sectional Concept for Use & Access, and the Software Platform and Development Concept for Resource Utilization and Performance Assessment, both of which are to be further developed conceptually.

The NUM Community consists of clinicians, scientists, and patients who volunteer their expertise for the NUM. The "Fach- und Organspezifischen Arbeitsgruppen" (FOSA), specialty and organ specific work-groups, are the heart of the NUM Community Platform. They vote in the "Fachbeirat", an expert advisory committee that consults sub-projects within the network, develops interdisciplinary research ideas, and partakes in central governing bodies of the NUM.

The staff of the NUM Community Platform supports this important work through administration, project management, and strategic work, such as the preparation of and follow-up for meetings, the organization of elections and events, and the further development of processes for community participation within the NUM.

The cross-sectional area Methods Hub and Biosamples Hub (NUM MB) is a consulting and support unit within the University Medicine Network (NUM) and provides methodological advice across all NUM research infrastructures (FIS). The aim is to generate high-quality data and biosamples for the scientific NUM community in accordance with legal requirements and ethical principles. Within NUM MB, IDMKD provides expertise in clinical data science and offers support on issues relating to the use of machine learning in the analysis of data from routine clinical care. NUM MB is funded by the University Medicine Network.

The NUM Study Network is a research infrastructure within the Network of University Medicine (NUM), dedicated to enhancing clinical and clinical-epidemiological research across Germany. It was established to foster collaboration among university and non-university sites. The central aim of the NUM Study Network consists in creating a standardized, efficient environment for conducting high-quality clinical studies, also through its specialty networks that cover various research fields.

The establishment of the NUM Study Network and its first specialty network, the Specialty Network Infectious Diseases, was supported as part of the NUM 2.0 funding period, enabled by the German Federal Ministry of Research, Technology and Space (BMFTR). This initial pilot phase ran from July 2024 to July 2025, during which the basic network structure was developed and also tested in preparation for the network’s long-term implementation.

A subsequent five-year funding period under NUM 3.0 was granted and started in August 2025. Since then, the NUM Study Network and the associated current and future specialty networks are funded under separate project descriptions, reflecting their specific roles and structures within the overall NUM framework.

This funding enables the NUM Study Network to establish a sustainable and competitive national infrastructure for clinical and clinical-epidemiological research in Germany.

NUM 2.0 grant No.: 01KX2121
NUM 3.0 grant No.: 01KX2524

{DE} Krebsforschungsdatenzentrum - KI-gestützte Evidenzgenerierung aus versorgungsnahen Daten Klinischer Krebsregister, GKV-Routinedaten, Klinikdaten und deren Linkage (onkoFDZ)

Im Projekt wird geprüft, wie Evidenzlücken unter Nutzung versorgungsnaher Daten (VeDa) von Klinischen Krebsregistern (KKR), durch die Deutsche Krebsgesellschaft zertifizierten Zentren, onkologischen Spitzenzentren und gesetzlichen Krankenkassen geschlossen werden können. Der Fokus liegt auf der Weiterentwicklung und Pilotierung von Verfahren zum datenschutzkonformen Linkage sowie der Nutzung und Auswertung von verlinkten Daten mit statistischen und KI-gestützten Methoden.

Das Projekt wird gefördert vom Bundesministerium für Gesundheit im Handlungsfeld „Digitalisierung“, Forschungsschwerpunkt „Krebsregisterdaten zusammenführen und intelligent nutzen“ (Förderkennzeichen: ZMI5-2522DAT14A-O).

Das IDMKD ist beteiligt als Leitung der Arbeitsarbeitsgruppe KI. Die Projektleitung übernehmen Prof. Dr. med. Monika Klinkhammer-Schalke, Arbeitsgemeinschaft Deutscher Tumorzentren e.V., und Prof. Dr. med. Jochen Schmitt, MPH, Universitätsklinikum und Medizinische Fakultät Carl Gustav Carus an der TU Dresden, Zentrum für Evidenzbasierte Gesundheitsversorgung (ZEGV). {/DE}

{EN} This project aims to close evidence gaps by using real-world data (RWD), known in German as versorgungsnahe Daten (VeDa), from Clinical Cancer Registries (KKR), certified centers of the German Cancer Society, comprehensive cancer centers, and statutory health insurance providers. The project focuses on advancing and piloting procedures for privacy-preserving data linkage, in addition to the application and analysis of linked data through statistical and AI-driven methods.

The Cancer Research Data Center project is funded by the German Federal Ministry of Health (Bundesministerium für Gesundheit) under the thematic field "Digitalization" and the research priority "Consolidating and Intelligently Using Cancer Registry Data" (Grant number: ZMI5-2522DAT14A-O).

The IDMKD participates by leading the working group on AI. The project is co-directed by Prof. Dr. med. Monika Klinkhammer-Schalke from the Association of German Cancer Centers (Arbeitsgemeinschaft Deutscher Tumorzentren e.V.) and Prof. Dr. med. Jochen Schmitt, MPH, from the University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Center for Evidence-Based Healthcare (ZEGV). {EN}

ORCHESTRA is an international research project aimed at tackling the Coronavirus pandemic, led by the University of Verona and involving 26 partners from 15 countries. The project was funded by the European Union’s Horizon 2020 research and innovation programme and was successfully concluded. We are continuing to contribute to the IT infrastructure, particularly hosting and maintaining the website, for activities related to the dissemination and exploitation of the ORCHESTRA results. The sustainable follow-up of ORCHESTRA is the Data Portal: available at https://orchestra-cohort.eu/.

EU Horizon 2020 Grant Agreement number: 101016167

Keine Beschreibung verfügbar.

PILGRIM is a comprehensive, multinational, multi-centre clinical study aiming to assess the impact of inappropriate antibacterial prescription on intestinal domination by EPE or VRE or infection with C. difficile. To achieve this goal, the study will closely follow the progression from first acquisition of drug-resistant organisms to infection with these bacteria at an individual patient level.

Countries participating: Germany, Sweden, Norway, Israel, Canada, Latvia

Study coordination: Frankfurt/Cologne

Personalized medicine for oncology (PM⁴Onco) aims to support molecular tumor boards in finding the best available treatment options for oncological patients. To achieve this and to make informed treatment planning decisions, physicians and researchers require access to a large amounts of data. While the required data often scattered across various sources in different formats, the digitalization presents an opportunity to integrate, harmonize and use the required data. 

A total of 25 project partners from across Germany have joined the PM⁴Onco consortium, including 22 university medical centers, to collaboratively develop a viable and usable solution. PM⁴Onco aims to establish an infrastructure for the use and exchange of data from clinical and biomedical research.

The project consortium is led by Prof. Dr. Dr. Melanie Börries (University Medical Center Freiburg), Prof. Dr. Benedikt Brors (German Cancer Research Center, Heidelberg), and Prof. Dr. Oliver Kohlbacher (University of Tübingen). At the Frankfurt site, our institute is involved in four of the project’s work packages, in collaboration with the University Cancer Center (UCT, Director: Prof. Dr. Christian Brandts), the Institute of Medical Informatics (Director: Prof. Dr. Holger Storf), and the Dr. Senckenberg Institute of Pathology (Director: Prof. Dr. Peter Wild).

Treatment guidelines typically provide clear recommendations for first- and second-line therapies across most cancer types. However, these guidelines are often lacking recommendations for third- or later-line treatments. This also holds for non-small cell lung cancer (NSCLC) and colorectal cancer (CRC), where no standardized recommendations are currently available for later lines of therapy.

The study aim is to use machine learning to predict overall survival and disease progression (including the development of metastases) in NSCLC and CRC patients undergoing third-line or later systemic therapy. The focus is on systemic treatments. The study will also explore the heterogeneity of therapies used in later lines and targets to identify common treatments sequences and patterns to predict their impact on survival.

The study will use nationwide cancer registry data from the center for cancer registry data, which includes comprehensive information on cancer diagnoses, staging, therapies and outcomes. This data will be analyzed in Frankfurt, with the aim of deriving guideline recommendations and thereby contributing to improved clinical care in the context of later line systemic anti-cancer treatment.

The retrospective, multicenter study on the risk of diesease recurrence in patients with localized renal cell carcinoma is conducted with support of the Clinical Data Science Group using the infrastructure of the German Cancer Consortium's (DKTK) Clinical Communication Platform (CCP). Six university medical centers participate in this project (Essen, Dresden, Frankfurt, Freiburg, Mainz, TU Munich).

The study focuses on patients with localized renal cell carcinoma. The aim of the study is to estimate the risk of tumor recurrence in this patient group based on routine clinical data. To determine the risk of recurrence, disease-free survival and overall survival from the time of surgery to tumor recurrence or death are used. The analyses are differentiated according to the morphology of the tumor tissue, the tumor stage at diagnosis, and the outcome of the resection. Therefore, data from the tumor documentation departments of the participating sites are fed into a federated data analysis.

Prof. Dr. Viktor Grünwald (Essen) is the principal investigator, while the Frankfurt team is involved in the implementation, particularly the analyses. Other site partners are Dr. Sherif Mehralivand (Dresden), Dr. Markus Grabbert (Freiburg), Dr. Rene Mager (Mainz), and Dr. Lilly Schmalbrock (TU Munich).

The aim of the RISK Prediction for Risk-stratified INfection Control and PrEvention (Risk Principe) project is automated surveillance and data-driven risk analysis & prediction with the endpoint of individualized, risk-stratified infection control and prevention.

• Implementation of the newly created prediction models & visualization in an interoperable application (extension of SmICS “Smart Infection Control System”) that reuses the routine data sets of the Medical Data Integration Centers (MeDICs) at each participating site.

• Development of an application that uses standardized interfaces for data access and is based on standardized and agreed interoperable data models (MII core dataset).

Main tasks of the institute:

• Requirements analysis for medical microbiology and identification of primary sources at DIC sites

• Implementation of a specific prediction model

• Support of the roll-out process

Projektlaufzeit: 01.07.2023-30.06.2027

The Research Project Suite (RPS), as comes from its name, is a collection of tools for managing research projects. The goal is to provide a collection of practical tools to give a level-up for your research environment. Although our solution is highly customizable, it is already pre-configured by default and all you need to start using it is to deploy it with a few simple steps.

Repository: https://gitlab.com/idcohorts/rps/rps-portal

Project objectives: development of a service that provides multiple endpoints that are designed to serve separate independent web apps:

• RPS Groups Interface (Groups) App

• RPS Header App

• RPS Infosite App

Advertised title: RPS UI Packages
Type of participation (coordination/management versus participation; if participation: name of project leader): ?
Hierarchy of funding bodies: ?

Link: https://gitlab.com/idcohorts/rps/rps-astro-packages

This study investigates the feasibility of using Large Language Models (LLMs) to automate systematic reviews. Based on a published systematic review on COVID-19 prognostic scores, it pursues two main objectives: first, to compare the results of an LLM-based replication, including inclusion decision and data extraction, with human reference data. Second, an LLM-based framework will be developed in accordance with established guidelines to facilitate access and use of LLMs for researchers. The study aims to demonstrate  that LLMs obtain the potential of to reduce manual effort and improve the efficiency of systematic reviews.

This project investigates systemic anticancer therapies in older adults through a transatlantic comparison between Germany and the United States. The aim is to analyse treatment sequences for curative and palliative therapies in the ten most incident solid cancers (lung, prostate, breast, colorectal, bladder, pancreatic, kidney, gastric, oesophageal, and endometrial) using real-world data (RWD). Differences and similarities in treatment patterns, drug regimens, and clinical outcomes will be examined to generate insights for optimising care strategies for ageing populations.

In Germany, a multicentre approach is applied: tumour documentation data are accessed via the Clinical Communication Platform (CCP) of the German Cancer Consortium (DKTK). Analyses are conducted with the federated software DataSHIELD, ensuring that patient data remain within each centre. Only aggregated results are compared across countries.

Type of participation: Overall coordination and project lead in Germany

Funding status: No external funding.

The concept of lines of therapy (LoT) is frequently used term in oncology which is of critical importance when decisions in tumor boards are discussed for further treatment. However, there is no uniform, interdisciplinary definition. The complexity and heterogeneity of malignant diseases and treatment modalities contribute to an inconsistent understanding of LoT. In a qualitative study, the understanding of the concept from the perspective of physicians from various oncological disciplines was examined by means of expert interviews. Most respondents agreed that there is no uniform definition of LoT and stated that they had already encountered misunderstandings. There was notable disagreement about the role of maintenance therapy, treatment intent, change of drug regimen, and treatment breaks. The majority of respondents considered the same criteria to be decisive for the definition as for a change in treatment lines (e.g., the occurrence of progression or tumor recurrence). Based on the findings of the qualitative study, a quantitative survey was conducted to examine the understanding, relevance, and potential events and measures for a change in LoT in a larger sample of physicians. Sixty physicians responded to the survey, with most respondents indicating that they were permanent members of a tumor board. Progression, recurrence, the occurrence of metastases, or severe side effects were considered indicators for a change in treatment line. The consideration of local therapeutic interventions, such as surgery, as an independent treatment line was controversial among the respondents.

The TIARA study is a national, multi-centre, hospital-based, prospective, observational study. Patient recruitment will take place at eight German university hospitals. Throughout the study, a cohort of 1,200 complex surgical patients undergoing open pancreaticoduodenectomy or open rectum/colon resection will be established including in-depth data collection and biomaterial sampling from different body sites at different timepoints pre- and post-surgery. 
Moreover, a national Antimicrobial Stewardship (AMS) Board, consisting of AMS experts and/or Infectious Disease (ID) specialists from all partner sites, will rate the quality of diagnostic and therapeutic measures related to the anti-infective treatments of study patients. 
Long-term outcome parameters will be assessed up to two years following the surgery. As an optional substudy, the quality of life will be assessed in included patients at different time points.

The TI BBD is the Translational Infrastructure (TI) for Bioresources, Biodata and Digital Health (BBD) of the German Center for Infection Research (DZIF), which is establishing a comprehensive biomedical service structure at the DZIF. The aim is to standardize biometric data and make database systems interoperable in order to enable translational research at the DZIF to be even more resource-efficient, scientifically high-quality and efficient.

Focus:

• Data and IT infrastructure expansion for improved information flow

• Support for DZIF scientists in data analysis

• Teaching key skills and capacity building through training and consulting

• Development and provision of digital tools and services through special projects