[Secondary Use of Electronic Medical Record Data from Primary Health Care is Feasible: Report from RADAR Project]. / Sekundäre Nutzung von hausärztlichen Routinedaten ist machbar ­ Bericht vom RADAR Projekt.

Objectives It is difficult to obtain longitudinal 'real world' data from ambulatory medical care in Germany in a systematic way. Our vision is a large German research data repository featuring representative, anonymized patient and outpatient health care data, longitudinal, continuously updated and across different providers, offering a perspective of linking secondary care data or additional data obtained from research cohorts, for example patient reported data or biodata, and will be accessible for other researchers. Here we report specific methods and results from the RADAR project.Methods Survey of legislation, design of technical processes and organisational solutions, with a feasibility study to evaluate technical and content functionality, acceptability and performance fitness for health services research questions.Results In 2016, a multi-disciplinary scientific team initiated the development of a privacy protection and IT security concept for data exported from the electronic medical records (EMR) of physicians' practices in line with the European General Data Protection Regulation. Technical and organisational requirements for lawful research infrastructure were developed and executed for use in a specific case, namely ̒oral anticoagulation'. In 7 Lower Saxonian general practices, 100 patients were selected by their physician and their data - reduced to 40 essential data fields - extracted from EMR via a mandatory software interface after informed consent. Still in the practice, the data were split into identifying or medical data. These were encrypted and transferred either to a trusted third party (TTP) or to a data repository, respectively. 75 patients who met our inclusion criteria (minimum of one year of oral anticoagulation treatment) received a quality-of-life questionnaire via the TTP. Of the 66 returns, 63 responses were then linked to the EMR data in the repository.Conclusion Results from RADAR project proved the technical and organisational feasibility of lawful, pseudonymised data acquisition and the linkage of questionnaires to EMR data. The protecting concepts privacy by design and data minimization (Art. 25 GDPR with Recital 78) were implemented. Without informed consent, secondary use of routine data from ambulatory care which are sufficiently anonymized but still meaningful is all but impossible under current German law.

Symptomatology and symptomatic treatment in multiple sclerosis: Results from a nationwide MS registry.

BACKGROUND: Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease. Over time, symptoms accumulate leading to increased disability of patients. OBJECTIVE: The objective of this article is to analyze the prevalence of symptoms and symptomatic treatment patterns in a nationwide MS registry. METHODS: Data sets from 35,755 patients were analyzed. RESULTS: More than two-thirds of patients were women with a mean age of 46.1 (±12.8) years. Median Expanded Disability Status Score (EDSS) was 3.0. The most frequently reported symptoms were fatigue, spasticity, and voiding disorders. In patients with short disease duration, fatigue was reported most frequently. Symptomatic treatment was most common for spasticity and depression, whereas fatigue was treated only in a third of affected patients. Almost a fifth of patients with EDSS ⩽ 3.5 and neuropsychological symptoms had retired from work. CONCLUSION: Whereas treatment for spasticity and depression is common in our cohort, sexual dysfunction, dysphagia, cognitive dysfunction, and fatigue are treated to a far lesser extent. The need for psychological support, physical, and occupational therapy has to be recognized as neuropsychological symptoms have a great impact on retirement at an early stage. Overall symptomatic treatment rates for the most common symptoms have increased over the last years (p < 0.001).

[Dementia Competence Network. Results and outlook]. / Das Kompetenznetz Demenzen. Erfolge und Ausblicke.

BACKGROUND: The Dementia Competence Network (DCN) is represented by academic memory clinics and has three major aims: (1) To facilitate the development of diagnostic tools including neuropsychology, biomarkers, imaging and genetics. (2) To implement clinical trials in mild cognitive impairment and dementia and (3) to improve standard care for dementia in Germany. AIMS: This article summarizes the achievements of the DCN so far and highlights future perspectives. METHODS: The DCN has built up two multicentre cohorts. Within the first cohort, patients with mild cognitive impairment or mild dementia were examined longitudinally using multiple neuropsychological assessments and numerous different biomarkers. In a subgroup of the first cohort, patients were treated with antidementive drugs in two placebo-controlled clinical trials. The second cohort included cognitively healthy older people and examined repetitively clinical, neuropsychological and psychosocial parameters for ten years. RESULTS AND DISCUSSION: The DCN has generated a large data and biomaterial bank. Numerous publications have helped to develop further diagnostic procedures and treatment of cognitive disorders and dementia. The DCN has contributed to end stigmatisation of dementia.

Multiple sclerosis registries in Europe - results of a systematic survey.

BACKGROUND: Identification of MS registries and databases that are currently in use in Europe as well as a detailed knowledge of their content and structure is important in order to facilitate comprehensive analysis and comparison of data. METHODS: National MS registries or databases were identified by literature search, from the results of the MS Barometer 2011 and by asking 33 national MS societies. A standardized questionnaire was developed and sent to the registries' leaders, followed by telephone interviews with them. RESULTS: Twenty registries were identified, with 13 completing the questionnaire and seven being interviewed by telephone. These registries differed widely for objectives, structure, collected data, and for patients and centres included. Despite this heterogeneity, common objectives of the registries were epidemiology (n=10), long-term therapy outcome (n=8), healthcare research (n=9) and support/basis for clinical trials (n=8). While physician-based outcome measures (EDSS) are used in all registries, data from patients' perspectives were only collected in six registries. CONCLUSIONS: The detailed information on a large number of national MS registries in Europe is a prerequisite to facilitating harmonized integration of existing data from MS registries and databases, as well as comprehensive analyses and comparison across European populations.

Genetic interaction of PICALM and APOE is associated with brain atrophy and cognitive impairment in Alzheimer's disease.

BACKGROUND: Evidence has emerged indicating that the ε4 allele of APOE and PICALM interact in conferring risk of Alzheimer's disease (AD). The biologic basis of this interaction is unclear, but it is likely to have phenotypic relevance and contribute to the structural and clinical heterogeneity of AD. METHODS: The aim of this study was to investigate interaction effects of the APOE ε4 allele and the alleles at the single-nucleotide polymorphism rs3851179 located in the PICALM locus. We analyzed brain volumes and cognitive phenotypes of 165 patients with early AD dementia. RESULTS: There was a synergistic adverse effect of homozygosity for the PICALM risk allele G in rs3851179 and APOE ε4 on volume in prefrontal and performance on the Trail Making Test A, which is sensitive to processing speed and working memory function. CONCLUSIONS: The data suggest a neural mechanism for APOE-PICALM interactions in patients with manifest AD and indicate that the PICALM genotype modulates both brain atrophy and cognitive performance in APOE ε4 carriers.

A "meta"-perspective on "bit rot" of biomedical research data.

Research data management (RDM) is an important topic for biomedical research due to the issue of "bit rot". RDM aims to implement access to reliable digital data for local and distributed research groups. A key aspect for the understanding of data is the use of metadata. This understanding has been investigated on the basis of two use cases of the DFG project LABIMI/F: RDM for genome data and biomedical image data. The results show that metadata can improve research not only for others but also for the researcher himself. However, RDM is still far from integrating all biomedical data. In addition, RDM is not (yet) a valid approach for clinical trial data management.

Reaching for the cloud: on the lessons learned from grid computing technology transfer process to the biomedical community.

Natural scientists such as physicists pioneered the sharing of computing resources, which led to the creation of the Grid. The inter domain transfer process of this technology has hitherto been an intuitive process without in depth analysis. Some difficulties facing the life science community in this transfer can be understood using the Bozeman's "Effectiveness Model of Technology Transfer". Bozeman's and classical technology transfer approaches deal with technologies which have achieved certain stability. Grid and Cloud solutions are technologies, which are still in flux. We show how Grid computing creates new difficulties in the transfer process that are not considered in Bozeman's model. We show why the success of healthgrids should be measured by the qualified scientific human capital and the opportunities created, and not primarily by the market impact. We conclude with recommendations that can help improve the adoption of Grid and Cloud solutions into the biomedical community. These results give a more concise explanation of the difficulties many life science IT projects are facing in the late funding periods, and show leveraging steps that can help overcoming the "vale of tears".

The IT-infrastructure of a biobank for an academic medical center.

For high quality research in biomedicine an operable biobank is essential. In order to make optimal use of the material and the huge amount of data a sustainable IT-infrastructure is indispensable. Therefore, we developed a concept for the IT-infrastructure of a biobank for an academic medical center. The conclusions for this concept are deduced from our experience with the biobank and IT-infrastructure of a clinical research unit. Our results indicate that the IT-infrastructure plays a pivotal role in successfully establishing a biobank. Several aspects of the IT-infrastructure are similarly found in other areas as, e.g. data protection and storage and quality management. Finally, we conclude that although a research database is not required for operating a biobank, the need for it will definitely emerge, especially with regard to personalized medicine and high-throughput gene expression analysis.

On transferring the grid technology to the biomedical community.

Natural scientists such as physicists pioneered the sharing of computing resources, which resulted in the Grid. The inter domain transfer process of this technology has been an intuitive process. Some difficulties facing the life science community can be understood using the Bozeman's "Effectiveness Model of Technology Transfer". Bozeman's and classical technology transfer approaches deal with technologies that have achieved certain stability. Grid and Cloud solutions are technologies that are still in flux. We illustrate how Grid computing creates new difficulties for the technology transfer process that are not considered in Bozeman's model. We show why the success of health Grids should be measured by the qualified scientific human capital and opportunities created, and not primarily by the market impact. With two examples we show how the Grid technology transfer theory corresponds to the reality. We conclude with recommendations that can help improve the adoption of Grid solutions into the biomedical community. These results give a more concise explanation of the difficulties most life science IT projects are facing in the late funding periods, and show some leveraging steps which can help to overcome the "vale of tears".

Key components and IT assistance of participant management in clinical research: a scoping review.

OBJECTIVES: Managing participants and their data are fundamental for the success of a clinical trial. Our review identifies and describes processes that deal with management of trial participants and highlights information technology (IT) assistance for clinical research in the context of participant management. METHODS: A scoping literature review design, based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement, was used to identify literature on trial participant-related proceedings, work procedures, or workflows, and assisting electronic systems. RESULTS: The literature search identified 1329 articles of which 111 were included for analysis. Participant-related procedures were categorized into 4 major trial processes: recruitment, obtaining informed consent, managing identities, and managing administrative data. Our results demonstrated that management of trial participants is considered in nearly every step of clinical trials, and that IT was successfully introduced to all participant-related areas of a clinical trial to facilitate processes. DISCUSSION: There is no precise definition of participant management, so a broad search strategy was necessary, resulting in a high number of articles that had to be excluded. Nevertheless, this review provides a comprehensive overview of participant management-related components, which was lacking so far. The review contributes to a better understanding of how computer-assisted management of participants in clinical trials is possible.

Digital teaching as an instrument for cross-location teaching networks in medical informatics: opportunities and challenges.

The increasingly digitized healthcare system requires new skills from all those involved. In order to impart these competencies, appropriate courses must be developed at educational institutions. In view of the rapid development of new aspects of digitization, this presents a challenge; suitable teaching formats must be developed successively. The establishment of cross-location teaching networks is one way to better meet training needs and to make the necessary spectrum of educational content available. As part of the Medical Informatics Initiative, the HiGHmed consortium is establishing such a teaching network, in the field of medical informatics, which covers many topics related to the digitization of the health care system. Various problem areas in the German education system were identified that hinder the development of the teaching network. These problem areas were prioritized firstly according to the urgency of the solution from the point of view of the HiGHmed consortium and secondly according to existing competencies in the participating societies. A workshop on the four most relevant topics was organized with experts from the German Society for Medical Informatics, Biometry and Epidemiology (GMDS), the Society for Medical Education (GMA) and the HiGHmed consortium. These are: recognition of exam results from teaching modules that are offered digitally and across locations, and their integration into existing curricula; recognition of digital, cross-location teaching in the teachers' teaching load; nationwide uniform competencies for teachers, in order to be able to conduct digital teaching effectively and with comparable quality; technical infrastructure to efficiently and securely communicate and manage the recognition of exam results between educational institutions. For all subject areas, existing preliminary work was identified on the basis of working questions, and short- and long-term needs for action were formulated. Finally, a need for the redesign of a technologically supported syntactic and semantic interoperability of learning performance recording was identified.

Early and differential diagnosis of dementia and mild cognitive impairment: design and cohort baseline characteristics of the German Dementia Competence Network.

BACKGROUND: The German Dementia Competence Network (DCN) has established procedures for standardized multicenter acquisition of clinical, biological and imaging data, for centralized data management, and for the evaluation of new treatments. METHODS: A longitudinal cohort study was set up for patients with mild cognitive impairment (MCI), patients with mild dementia and control subjects. The aims were to establish the diagnostic, differential diagnostic and prognostic power of a range of clinical, laboratory and imaging methods. Furthermore, 2 clinical trials were conducted with patients suffering from MCI and mild to moderate Alzheimer's Disease (AD). These trials aimed at evaluating the efficacy and safety of the combination of galantamine and memantine versus galantamine alone. RESULTS: Here, we report on the scope and projects of the DCN, the methods that were employed, the composition and flow within the diverse groups of patients and control persons and on the clinical and neuropsychological baseline characteristics of the group of 2,113 subjects who participated in the observational and clinical trials. CONCLUSION: These data have an impact on the procedures for the early and differential clinical diagnosis of dementias, the current standard treatment of AD as well as on future clinical trials in AD.

HiGHmed - An Open Platform Approach to Enhance Care and Research across Institutional Boundaries.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. HiGHmed brings together 24 partners from academia and industry, aiming at improvements in care provision, biomedical research and epidemiology. By establishing a shared information governance framework, data integration centers and an open platform architecture in cooperation with independent healthcare providers, the meaningful reuse of data will be facilitated. Complementary, HiGHmed integrates a total of seven Medical Informatics curricula to develop collaborative structures and processes to train medical informatics professionals, physicians and researchers in new forms of data analytics. GOVERNANCE AND POLICIES: We describe governance structures and policies that have proven effective during the conceptual phase. These were further adapted to take into account the specific needs of the development and networking phase, such as roll-out, carerelated aspects and our focus on curricula development in Medical Inform atics. ARCHITECTURAL FRAMEWORK AND METHODOLOGY: To address the challenges of organizational, technical and semantic interoperability, a concept for a scalable platform architecture, the HiGHmed Platform, was developed. We outline the basic principles and design goals of the open platform approach as well as the roles of standards and specifications such as IHE XDS, openEHR, SNOMED CT and HL7 FHIR. A shared governance framework provides the semantic artifacts which are needed to establish semantic interoperability. USE CASES: Three use cases in the fields of oncology, cardiology and infection control will demonstrate the capabilities of the HiGHmed approach. Each of the use cases entails diverse challenges in terms of data protection, privacy and security, including clinical use of genome sequencing data (oncology), continuous longitudinal monitoring of physical activity (cardiology) and cross-site analysis of patient movement data (infection control). DISCUSSION: Besides the need for a shared governance framework and a technical infrastructure, backing from clinical leaders is a crucial factor. Moreover, firm and sustainable commitment by participating organizations to collaborate in further development of their information system architectures is needed. Other challenges including topics such as data quality, privacy regulations, and patient consent will be addressed throughout the project.

Shortcomings of current grid middlewares regarding privacy in HealthGrids.

Although grid computing middlewares are in research use since many years, they lack of particular security features for biomedical applications. The analysis of the common Globus middleware reveals several security-related shortcomings. As a result, extended security measures for HealthGrids have been identified. They include tools for auditing, tracking, fine grained access control for structured documents, trust and trust delegation. The German MediGRID project is facing this with an "Enhanced Security" package intending to bridge the gap between current legal, data protection as well as data security requirements and the available grid technology.

Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer's dementia.

BACKGROUND: The progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta1-42 (Aß42), amyloid-beta1-40 (Aß40) levels, the ratio of Aß42/Aß40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia. METHODS: We used 115 complete datasets from MCI patients of the "Dementia Competence Network", a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%. RESULTS: Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aß40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80-0.83, and the four-parameter combination from AUC 0.81-0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone. CONCLUSION: A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.

Finite-element-method (FEM) model generation of time-resolved 3D echocardiographic geometry data for mitral-valve volumetry.

INTRODUCTION: Mitral Valve (MV) 3D structural data can be easily obtained using standard transesophageal echocardiography (TEE) devices but quantitative pre- and intraoperative volume analysis of the MV is presently not feasible in the cardiac operation room (OR). Finite element method (FEM) modelling is necessary to carry out precise and individual volume analysis and in the future will form the basis for simulation of cardiac interventions. METHOD: With the present retrospective pilot study we describe a method to transfer MV geometric data to 3D Slicer 2 software, an open-source medical visualization and analysis software package. A newly developed software program (ROIExtract) allowed selection of a region-of-interest (ROI) from the TEE data and data transformation for use in 3D Slicer. FEM models for quantitative volumetric studies were generated. RESULTS: ROI selection permitted the visualization and calculations required to create a sequence of volume rendered models of the MV allowing time-based visualization of regional deformation. Quantitation of tissue volume, especially important in myxomatous degeneration can be carried out. Rendered volumes are shown in 3D as well as in time-resolved 4D animations. CONCLUSION: The visualization of the segmented MV may significantly enhance clinical interpretation. This method provides an infrastructure for the study of image guided assessment of clinical findings and surgical planning. For complete pre- and intraoperative 3D MV FEM analysis, three input elements are necessary: 1. time-gated, reality-based structural information, 2. continuous MV pressure and 3. instantaneous tissue elastance. The present process makes the first of these elements available. Volume defect analysis is essential to fully understand functional and geometrical dysfunction of but not limited to the valve. 3D Slicer was used for semi-automatic valve border detection and volume-rendering of clinical 3D echocardiographic data. FEM based models were also calculated. METHOD: A Philips/HP Sonos 5500 ultrasound device stores volume data as time-resolved 4D volume data sets. Data sets for three subjects were used. Since 3D Slicer does not process time-resolved data sets, we employed a standard movie maker to animate the individual time-based models and visualizations. Calculation time and model size were minimized. Pressures were also easily available. We speculate that calculation of instantaneous elastance may be possible using instantaneous pressure values and tissue deformation data derived from the animated FEM.

Changing requirements and resulting needs for IT-infrastructure for longitudinal research in the neurosciences.

The observation of growing "difficulties" in IT-infrastructures in neuroscience research during the last years led to a search for reasons and an analysis on how this phenomenon is reflected in the scientific literature. With a retrospective analysis of nine examples of multicenter research projects in the neurosciences and a literature review the observation was systematically analyzed. Results show that the rise in complexity mainly stems from two reasons: (1) more and more need for information on quality and context of research data (metadata) and (2) long-term requirements to handle the consent and identity/pseudonyms of study participants and biomaterials in relation to legal requirements. The combination of these two aspects together with very long study times and data evaluation periods are components of the subjectively perceived "difficulties". A direct consequence of this result is that big multicenter trials are becoming part of integrated research data environments and are not standing alone for themselves anymore. This drives up the resource needs regarding the IT-infrastructure in neuroscience research. In contrast to these findings, literature on this development is scarce and the problem probably underestimated.

Architecture of a Biomedical Informatics Research Data Management Pipeline.

In University Medical Centers, heterogeneous data are generated that cannot always be clearly attributed to patient care or biomedical research. Each data set has to adhere to distinct intrinsic and operational quality standards. However, only if high-quality data, tools to work with the data, and most importantly guidelines and rules of how to work with the data are addressed adequately, an infrastructure can be sustainable. Here, we present the IT Research Architecture of the University Medical Center Göttingen and describe our ten years' experience and lessons learned with infrastructures in networked medical research.