The sample locator: A federated search tool for biosamples and associated data in Europe using HL7 FHIR.

BACKGROUND: This study outlines the development of a highly interoperable federated IT infrastructure for academic biobanks located at the major university hospital sites across Germany. High-quality biosamples linked to clinical data, stored in biobanks are essential for biomedical research. We aimed to facilitate the findability of these biosamples and their associated data. Networks of biobanks provide access to even larger pools of samples and data even from rare diseases and small disease subgroups. The German Biobank Alliance (GBA) established in 2017 under the umbrella of the German Biobank Node (GBN), has taken on the mission of a federated data discovery service to make biosamples and associated data available to researchers across Germany and Europe. METHODS: In this context, we identified the requirements of researchers seeking human biosamples from biobanks and the needs of biobanks for data sovereignty over their samples and data in conjunction with the sample donor's consent. Based on this, we developed a highly interoperable federated IT infrastructure using standards such as Fast Healthcare Interoperability Resources (HL7 FHIR) and Clinical Quality Language (CQL). RESULTS: The infrastructure comprises two major components enabling federated real-time access to biosample metadata, allowing privacy-compliant queries and subsequent project requests. It has been in use since 2019, connecting 16 German academic biobanks, with additional European biobanks joining. In production since 2019 it has run 4941 queries over the span of one year on more than 900,000 biosamples collected from more than 170,000 donors. CONCLUSION: This infrastructure enhances the visibility and accessibility of biosamples for research, addressing the growing demand for human biosamples and associated data in research. It also underscores the need for improvements in processes beyond IT infrastructure, aiming to advance biomedical research and similar infrastructure development in other fields.

Cancer Core Europe: Leveraging Institutional Synergies to Advance Oncology Research and Care Globally.

Cancer Core Europe brings together the expertise, resources, and interests of seven leading cancer institutes committed to leveraging collective innovation and collaboration in precision oncology. Through targeted efforts addressing key medical challenges in cancer and partnerships with multiple stakeholders, the consortium seeks to advance cancer research and enhance equitable patient care.

Adaptability of Existing Feasibility Tools for Clinical Study Research Data Platforms.

INTRODUCTION: The increasing need for secondary use of clinical study data requires FAIR infrastructures, i.e. provide findable, accessible, interoperable and reusable data. It is crucial for data scientists to assess the number and distribution of cohorts that meet complex combinations of criteria defined by the research question. This so-called feasibility test is increasingly offered as a self-service, where scientists can filter the available data according to specific parameters. Early feasibility tools have been developed for biosamples or image collections. They are of high interest for clinical study platforms that federate multiple studies and data types, but they pose specific requirements on the integration of data sources and data protection. METHODS: Mandatory and desired requirements for such tools were acquired from two user groups - primary users and staff managing a platform's transfer office. Open Source feasibility tools were sought by different literature search strategies and evaluated on their adaptability to the requirements. RESULTS: We identified seven feasibility tools that we evaluated based on six mandatory properties. DISCUSSION: We determined five feasibility tools to be most promising candidates for adaption to a clinical study research data platform, the Clinical Communication Platform, the German Portal for Medical Research Data, the Feasibility Explorer, Medical Controlling, and the Sample Locator.

Record linkage based patient intersection cardinality for rare disease studies using Mainzelliste and secure multi-party computation.

BACKGROUND: The low number of patients suffering from any given rare diseases poses a difficult problem for medical research: With the exception of some specialized biobanks and disease registries, potential study participants' information are disjoint and distributed over many medical institutions. Whenever some of those facilities are in close proximity, a significant overlap of patients can reasonably be expected, further complicating statistical study feasibility assessments and data gathering. Due to the sensitive nature of medical records and identifying data, data transfer and joint computations are often forbidden by law or associated with prohibitive amounts of effort. To alleviate this problem and to support rare disease research, we developed the Mainzelliste Secure EpiLinker (MainSEL) record linkage framework, a secure Multi-Party Computation based application using trusted-third-party-less cryptographic protocols to perform privacy-preserving record linkage with high security guarantees. In this work, we extend MainSEL to allow the record linkage based calculation of the number of common patients between institutions. This allows privacy-preserving statistical feasibility estimations for further analyses and data consolidation. Additionally, we created easy to deploy software packages using microservice containerization and continuous deployment/continuous integration. We performed tests with medical researchers using MainSEL in real-world medical IT environments, using synthetic patient data. RESULTS: We show that MainSEL achieves practical runtimes, performing 10 000 comparisons in approximately 5 minutes. Our approach proved to be feasible in a wide range of network settings and use cases. The "lessons learned" from the real-world testing show the need to explicitly support and document the usage and deployment for both analysis pipeline integration and researcher driven ad-hoc analysis use cases, thus clarifying the wide applicability of our software. MainSEL is freely available under: https://github.com/medicalinformatics/MainSEL CONCLUSIONS: MainSEL performs well in real-world settings and is a useful tool not only for rare disease research, but medical research in general. It achieves practical runtimes, improved security guarantees compared to existing solutions, and is simple to deploy in strict clinical IT environments. Based on the "lessons learned" from the real-word testing, we hope to enable a wide range of medical researchers to meet their needs and requirements using modern privacy-preserving technologies.

ToolPool Gesundheitsforschung - A Repository for Software and Services Focused on Supporting Clinical and Epidemiological Research.

The academic research environment is characterized by self-developed, innovative, customized solutions, which are often free to use for third parties with open-source code and open licenses. On the other hand, they are maintained only to a very limited extent after the end of project funding. The ToolPool Gesundheitsforschung addresses the problem of finding ready to use solutions by building a registry of proven and supported tools, services, concepts and consulting offers. The goal is to provide an up-to-date selection of "relevant" solutions for a given domain that are immediately usable and that are actually used by third parties, rather than aiming at a complete list of all solutions which belong to that domain. Proof of relevance and usage must be provided, for example, by concrete application scenarios, experience reports by uninvolved third parties, references in publications or workshops held. Quality assurance is carried out for new entries by an agreed list of admission criteria, for existing entries at least once a year by a special task force. Currently, 79 solutions are represented, this number is to be significantly expanded by involving of new editors from current national funding initiatives in Germany.

Understanding the Nature of Metadata: Systematic Review.

BACKGROUND: Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term "metadata" and its use is not always unambiguous. OBJECTIVE: This study aimed to understand the definition of metadata and the challenges resulting from metadata reuse. METHODS: A systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used. RESULTS: The harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas. CONCLUSIONS: Metadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context.

Pre-medication with oral anticoagulants is associated with better outcomes in a large multinational COVID-19 cohort with cardiovascular comorbidities.

AIMS: Coagulopathy and venous thromboembolism are common findings in coronavirus disease 2019 (COVID-19) and are associated with poor outcome. Timely initiation of anticoagulation after hospital admission was shown to be beneficial. In this study we aim to examine the association of pre-existing oral anticoagulation (OAC) with outcome among a cohort of SARS-CoV-2 infected patients. METHODS AND RESULTS: We analysed the data from the large multi-national Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) from March to August 2020. Patients with SARS-CoV-2 infection were eligible for inclusion. We retrospectively analysed the association of pre-existing OAC with all-cause mortality. Secondary outcome measures included COVID-19-related mortality, recovery and composite endpoints combining death and/or thrombotic event and death and/or bleeding event. We restricted bleeding events to intracerebral bleeding in this analysis to ensure clinical relevance and to limit reporting errors. A total of 1 433 SARS-CoV-2 infected patients were analysed, while 334 patients (23.3%) had an existing premedication with OAC and 1 099 patients (79.7%) had no OAC. After risk adjustment for comorbidities, pre-existing OAC showed a protective influence on the endpoint death (OR 0.62, P = 0.013) as well as the secondary endpoints COVID-19-related death (OR 0.64, P = 0.023) and non-recovery (OR 0.66, P = 0.014). The combined endpoint death or thrombotic event tended to be less frequent in patients on OAC (OR 0.71, P = 0.056). CONCLUSIONS: Pre-existing OAC is protective in COVID-19, irrespective of anticoagulation regime during hospital stay and independent of the stage and course of disease.

Mainzelliste SecureEpiLinker (MainSEL): privacy-preserving record linkage using secure multi-party computation.

MOTIVATION: Record Linkage has versatile applications in real-world data analysis contexts, where several datasets need to be linked on the record level in the absence of any exact identifier connecting related records. An example are medical databases of patients, spread across institutions, that have to be linked on personally identifiable entries like name, date of birth or ZIP code. At the same time, privacy laws may prohibit the exchange of this personally identifiable information (PII) across institutional boundaries, ruling out the outsourcing of the record linkage task to a trusted third party. We propose to employ privacy-preserving record linkage (PPRL) techniques that prevent, to various degrees, the leakage of PII while still allowing for the linkage of related records. RESULTS: We develop a framework for fault-tolerant PPRL using secure multi-party computation with the medical record keeping software Mainzelliste as the data source. Our solution does not rely on any trusted third party and all PII is guaranteed to not leak under common cryptographic security assumptions. Benchmarks show the feasibility of our approach in realistic networking settings: linkage of a patient record against a database of 10 000 records can be done in 48 s over a heavily delayed (100 ms) network connection, or 3.9 s with a low-latency connection. AVAILABILITY AND IMPLEMENTATION: The source code of the sMPC node is freely available on Github at https://github.com/medicalinformatics/SecureEpilinker subject to the AGPLv3 license. The source code of the modified Mainzelliste is available at https://github.com/medicalinformatics/MainzellisteSEL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The journey to establishing an IT-infrastructure within the German Biobank Alliance.

BACKGROUND: Biobanks ensure the long-term storage and accessibility of biospecimens and corresponding data sets. Thus, they form the foundation for many research projects which may contribute to improving medical care. With the establishment of the German Biobank Node and Alliance, expertise in biobanking is bundled and strengthened. An important component within this research infrastructure is the set-up of an information technology (IT) network for allowing feasibility requests across individual biobanks. OBJECTIVE: We aim to describe relevant aspects that have shaped the journey to interconnect biobanks, to enhance their visibility within the research-community, to harmonize data, and to enable feasibility searches to support access to available data and biosamples. METHODS: To achieve this task, we resorted to a wide variety of methods: we ran a requirement analysis, decided on the mode of operation for the federated team of IT-developers and on the development approach itself, took related national and international initiatives into account, and concluded with evaluations of the developed software artefacts and the operation of the entire chain of applications. RESULTS: We drew an IT framework including all heterogeneous data aspects derived from our requirement analysis and developed a comprehensive IT infrastructure. The successful implementation benefited from a smooth interaction of a federated IT team distributed across all participating sites that was even able to manage a major technology change mid-project. Authentication and project management services from associated partners could be integrated and the graphic user interface for an intuitive search tool for biospecimens was designed iteratively. The developed code is open source to ensure sustainability and the local implementation is concluded and functioning. The evaluation of the components was positive. CONCLUSIONS: The entire project had given ample opportunity for challenges, predictable and unpredictable-from the mode of operation to changing some of the initial ideas. We learned our lessons concerning personnel, budget planning and technical as well as manual monitoring as well as some requirements arising only during the process of the project. Nevertheless, we can here report a success story of a network infrastructure, highly agile and much easier in local installation than initially anticipated.

ADT2FHIR - A Tool for Converting ADT/GEKID Oncology Data to HL7 FHIR Resources.

Harmonized and interoperable data management is a core requirement for federated infrastructures in clinical research. Institutions participating in such infrastructures often have to invest large degrees of time and resources in implementing necessary data integration processes to convert their local data to the required target structure. If the data is already available in an alternative shared data structure, the transformation from source to the desired target structure can be implemented once and then be distributed to all participants to reduce effort and harmonize results. The HL7® FHIR® standard is used as a basis for the shared data model of several medical consortia like DKTK and GBA. It is based on so-called resources which can be represented in XML. Oncological data in German university hospitals is commonly available in the ADT/GEKID format. From this common basis we conceptualized and implemented a transformation which accepts ADT/GEKID XML files and returns FHIR resources. We identified several problems with using the general ADT/GEKID structure in federated research infrastructures, as well as some possible pitfalls relating to the FHIR need for resource ids and focus on semantic coding which differs from the approach in the ADT/GEKID standard. To facilitate participation in federated infrastructures, we propose the ADT2FHIR transformation tool for partners with oncological data in the ADT/GEKID format.

MAGICPL: A Generic Process Description Language for Distributed Pseudonymization Scenarios.

OBJECTIVES: Pseudonymization is an important aspect of projects dealing with sensitive patient data. Most projects build their own specialized, hard-coded, solutions. However, these overlap in many aspects of their functionality. As any re-implementation binds resources, we would like to propose a solution that facilitates and encourages the reuse of existing components. METHODS: We analyzed already-established data protection concepts to gain an insight into their common features and the ways in which their components were linked together. We found that we could represent these pseudonymization processes with a simple descriptive language, which we have called MAGICPL, plus a relatively small set of components. We designed MAGICPL as an XML-based language, to make it human-readable and accessible to nonprogrammers. Additionally, a prototype implementation of the components was written in Java. MAGICPL makes it possible to reference the components using their class names, making it easy to extend or exchange the component set. Furthermore, there is a simple HTTP application programming interface (API) that runs the tasks and allows other systems to communicate with the pseudonymization process. RESULTS: MAGICPL has been used in at least three projects, including the re-implementation of the pseudonymization process of the German Cancer Consortium, clinical data flows in a large-scale translational research network (National Network Genomic Medicine), and for our own institute's pseudonymization service. CONCLUSIONS: Putting our solution into productive use at both our own institute and at our partner sites facilitated a reduction in the time and effort required to build pseudonymization pipelines in medical research.

BBMRI-ERIC Negotiator: Implementing Efficient Access to Biobanks.

Various biological resources, such as biobanks and disease-specific registries, have become indispensable resources to better understand the epidemiology and biological mechanisms of disease and are fundamental for advancing medical research. Nevertheless, biobanks and similar resources still face significant challenges to become more findable and accessible by users on both national and global scales. One of the main challenges for users is to find relevant resources using cataloging and search services such as the BBMRI-ERIC Directory, operated by European Research Infrastructure on Biobanking and Biomolecular Resources (BBMRI-ERIC), as these often do not contain the information needed by the researchers to decide if the resource has relevant material/data; these resources are only weakly characterized. Hence, the researcher is typically left with too many resources to explore and investigate. In addition, resources often have complex procedures for accessing holdings, particularly for depletable biological materials. This article focuses on designing a system for effective negotiation of access to holdings, in which a researcher can approach many resources simultaneously, while giving each resource team the ability to implement their own mechanisms to check if the material/data are available and to decide if access should be provided. The BBMRI-ERIC has developed and implemented an access and negotiation tool called the BBMRI-ERIC Negotiator. The Negotiator enables access negotiation to more than 600 biobanks from the BBMRI-ERIC Directory and other discovery services such as GBA/BBMRI-ERIC Locator or RD-Connect Finder. This article summarizes the principles that guided the design of the tool, the terminology used and underlying data model, request workflows, authentication and authorization mechanism(s), and the mechanisms and monitoring processes to stimulate the desired behavior of the resources: to effectively deliver access to biological material and data.

Oncology on FHIR: A Data Model for Distributed Cancer Research.

In the field of oncology, a close integration of cancer research and patient care is indispensable. Although an exchange of data between health care providers and other institutions such as cancer registries has already been established in Germany, it does not take advantage of internationally coordinated health data standards. Translational cancer research would also benefit from such standards in the context of secondary data use. This paper employs use cases from the German Cancer Consortium (DKTK) to show how this gap can be closed using a harmonised FHIR-based data model, and how to apply it to an existing federated data platform.

Optimization of the Mainzelliste software for fast privacy-preserving record linkage.

BACKGROUND: Data analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person. Due to the lack of unique personal identifiers across these sources, record linkage relies on the similarity of personal data such as first and last names or birth dates. However, the exchange of such identifying data with a third party, as is the case in record linkage, is generally subject to strict privacy requirements. This problem is addressed by privacy-preserving record linkage (PPRL) and pseudonymization services. Mainzelliste is an open-source record linkage and pseudonymization service used to carry out PPRL processes in real-world use cases. METHODS: We evaluate the linkage quality and performance of the linkage process using several real and near-real datasets with different properties w.r.t. size and error-rate of matching records. We conduct a comparison between (plaintext) record linkage and PPRL based on encoded records (Bloom filters). Furthermore, since the Mainzelliste software offers no blocking mechanism, we extend it by phonetic blocking as well as novel blocking schemes based on locality-sensitive hashing (LSH) to improve runtime for both standard and privacy-preserving record linkage. RESULTS: The Mainzelliste achieves high linkage quality for PPRL using field-level Bloom filters due to the use of an error-tolerant matching algorithm that can handle variances in names, in particular missing or transposed name compounds. However, due to the absence of blocking, the runtimes are unacceptable for real use cases with larger datasets. The newly implemented blocking approaches improve runtimes by orders of magnitude while retaining high linkage quality. CONCLUSION: We conduct the first comprehensive evaluation of the record linkage facilities of the Mainzelliste software and extend it with blocking methods to improve its runtime. We observed a very high linkage quality for both plaintext as well as encoded data even in the presence of errors. The provided blocking methods provide order of magnitude improvements regarding runtime performance thus facilitating the use in research projects with large datasets and many participants.

A Federated Online Search Tool for Biospecimens (Sample Locator): Usability Study.

BACKGROUND: The German Biobank Alliance (GBA) aims to establish a cross-site biobank network. For this endeavor, the so-called Sample Locator, a federated search tool for biospecimens and related data, has been developed, forming the heart of its information technology (IT) infrastructure. OBJECTIVE: To ensure the sustainable use of such a tool, we included researchers as participants in an end user-based usability evaluation. METHODS: To develop a prototype ready for evaluation, we needed input from GBA IT experts. Thus, we conducted a 2-day workshop with 8 GBA IT team members. The focus was on the respective steps of a user-centered design process. With the acquired knowledge, the participants designed low-fidelity mock-ups. The main ideas of these mock-ups were discussed, extracted, and summarized into a comprehensive prototype using Microsoft PowerPoint. Furthermore, we created a questionnaire concerning the usability of the prototype, including the System Usability Scale (SUS), questions on negative and positive aspects, and typical tasks to be fulfilled with the tool. Subsequently, the prototype was pretested on the basis of this questionnaire with researchers who have a biobank background. Based on this preliminary work, the usability analysis was ultimately carried out with researchers and the results were evaluated. RESULTS: Altogether, 27 researchers familiar with sample requests evaluated the prototype. The analysis of the feedback certified a good usability, given that the Sample Locator prototype was seen as intuitive and user-friendly by 74% (20/27) of the participants. The total SUS score by the 25 persons that completed the questionnaire was 80.4, indicating good system usability. Still, the evaluation provided useful advice on optimization potential (eg, offering a help function). CONCLUSIONS: The findings of this usability analysis indicate that the considerations regarding a user-friendly application that have been made in the development process so far strongly coincide with the perception of the study participants. Nevertheless, it was important to engage prospective end users to ensure that the previous development is going in the desired direction and that the Sample Locator will be used in the future. The user comments and suggestions for improvement will be considered in upcoming iterations for refinement.

A Smart Mapping Editor for Standardised Data Transformation.

The integration of heterogeneous healthcare data sources is a necessary process to enable the secondary use valuable information in clinical research. Data integration is time-consuming for data stewards. The transformation using predefined rules for data harmonization can reduce the time-consuming and error-prone work and ease the data integration at various sites. In our study, we examined various script(ing) languages to find the most suitable candidate for definition of transformation rules and implement a smart editor which supports the data stewards in selecting rules reusing them. Thereby, it also provides an automatic and seamless documentation to strengthen the reliability of the defined transformation rules.

Modern Information Technology for Cancer Research: What's in IT for Me? An Overview of Technologies and Approaches.

Information technology (IT) can enhance or change many scenarios in cancer research for the better. In this paper, we introduce several examples, starting with clinical data reuse and collaboration including data sharing in research networks. Key challenges are semantic interoperability and data access (including data privacy). We deal with gathering and analyzing genomic information, where cloud computing, uncertainties and reproducibility challenge researchers. Also, new sources for additional phenotypical data are shown in patient-reported outcome and machine learning in imaging. Last, we focus on therapy assistance, introducing tools used in molecular tumor boards and techniques for computer-assisted surgery. We discuss the need for metadata to aggregate and analyze data sets reliably. We conclude with an outlook towards a learning health care system in oncology, which connects bench and bedside by employing modern IT solutions.

Towards a Federation of Metadata Repositories: Addressing Technical Interoperability.

The utilisation of metadata repositories increasingly promotes secondary use of routinely collected data. However, this has not yet solved the problem of data exchange across organisational boundaries. The local description of a metadata set must also be exchangeable for flawless data exchange. In previous work, a metadata exchange language QL4MDR was developed. This work aimed to examine the applicability of this exchange language. For this purpose, existing MDR implementations were identified and systematically inspected and roughly divided into two categories to distinguish between data integration and query integration. It has been shown that all the implementations can be adapted to QL4MDR. The integration of metadata is an important first step; it enables the exchange of information, which is so urgently needed for the further processing of instance data, from the metadata mappings to the transformation rules.

Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC.

BACKGROUND: High-quality clinical data and biological specimens are key for medical research and personalized medicine. The Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) aims to facilitate access to such biological resources. The accompanying ADOPT BBMRI-ERIC project kick-started BBMRI-ERIC by collecting colorectal cancer data from European biobanks. OBJECTIVES: To transform these data into a common representation, a uniform approach for data integration and harmonization had to be developed. This article describes the design and the implementation of a toolset for this task. METHODS: Based on the semantics of a metadata repository, we developed a lexical bag-of-words matcher, capable of semiautomatically mapping local biobank terms to the central ADOPT BBMRI-ERIC terminology. Its algorithm supports fuzzy matching, utilization of synonyms, and sentiment tagging. To process the anonymized instance data based on these mappings, we also developed a data transformation application. RESULTS: The implementation was used to process the data from 10 European biobanks. The lexical matcher automatically and correctly mapped 78.48% of the 1,492 local biobank terms, and human experts were able to complete the remaining mappings. We used the expert-curated mappings to successfully process 147,608 data records from 3,415 patients. CONCLUSION: A generic harmonization approach was created and successfully used for cross-institutional data harmonization across 10 European biobanks. The software tools were made available as open source.

Automated Reports for Monitoring and Improving Data Quality in a Translational Research Network.

Standardised, automated quality reports were generated at three pilot locations of the decentralized translational research network DKTK with separated local data warehouses (LDW), for assessing syntactic conformity against common data element definitions deposited in a central metadata repository (MDR). Deviations in the LDW were categorised, and locally corrected. Comparisons of reports from two time points confirm a major improvement in data quality in terms of syntactic conformity, an essential prerequisite for network-wide data integration.