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Frahm N, Ellenberger D, Stahmann A, Fneish F, Lüftenegger D, Salmen HC, Schirduan K, Schaak TPA, Flachenecker P, Kleinschnitz C, Paul F, Krefting D, Zettl UK, Peters M, Warnke C. Treatment switches of disease-modifying therapies in people with multiple sclerosis: long-term experience from the German MS Registry. Ther Adv Neurol Disord 2024; 17:17562864241239740. [PMID: 38560408 PMCID: PMC10981260 DOI: 10.1177/17562864241239740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 02/14/2024] [Indexed: 04/04/2024] Open
Abstract
Background The spectrum of disease-modifying therapies (DMTs) for people with multiple sclerosis (PwMS) has expanded over years, but data on treatment strategies is largely lacking. DMT switches are common clinical practice. Objective To compare switchers and non-switchers, characterize the first DMT switch and identify reasons and predictors for switching the first DMT. Methods Data on 2722 PwMS from the German MS Registry were retrospectively analyzed regarding sociodemographic/clinical differences between 1361 switchers (PwMS discontinuing the first DMT) and non-switchers matched according to age, sex, and observation period. Frequencies of first and second DMTs were calculated and switch reasons identified. Predictors for DMT switches were revealed using univariable and multivariable regression models. Results Switchers and non-switchers differed significantly regarding time to first DMT, education, calendar period of the first DMT start (2014-2017 versus 2018-2021), first DMT class used [mild-to-moderate efficacy (MME) versus high-efficacy (HE) DMT], time on first DMT, and disease activity at first DMT start or cessation/last follow-up. The majority of PwMS started with MME DMTs (77.1%), with the most common being glatiramer acetate, dimethyl/diroximel fumarate, and beta-interferon variants. Switchers changed treatment more often to HE DMTs (39.6%), most commonly sphingosine-1-phosphate receptor modulators, anti-CD20 monoclonal antibodies, and natalizumab. Fewer PwMS switched to MME DMTs (35.9%), with the most common being dimethyl/diroximel fumarate, teriflunomide, or beta-interferon. Among 1045 PwMS with sufficient data (76.8% of 1361 switchers), the most frequent reasons for discontinuing the first DMT were disease activity despite DMT (63.1%), adverse events (17.1%), and patient request (8.3%). Predictors for the first DMT switch were MME DMT as initial treatment [odds ratio (OR) = 2.83 (1.76-4.61), p < 0.001; reference: HE DMT], first DMT initiation between 2014 and 2017 [OR = 11.55 (6.93-19.94), p < 0.001; reference: 2018-2021], and shorter time on first DMT [OR = 0.22 (0.18-0.27), p < 0.001]. Conclusion The initial use of MME DMTs was among the strongest predictors of DMT discontinuation in a large German retrospective MS cohort, arguing for the need for prospective treatment strategy trials, not only but also on the initial broad use of HE DMTs in PwMS.
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Affiliation(s)
- Niklas Frahm
- German MS Registry, MS Forschungs- und Projektentwicklungs-gGmbH (MS Research and Project Development gGmbH [MSFP]), Krausenstr. 50, Hannover, Niedersachsen 30171, Germany
| | - David Ellenberger
- German MS Registry, MS Forschungs- und Projektentwicklungs-gGmbH (MS Research and Project Development gGmbH [MSFP]), Hannover, Germany
| | - Alexander Stahmann
- German MS Registry, MS Forschungs- und Projektentwicklungs-gGmbH (MS Research and Project Development gGmbH [MSFP]), Hannover, Germany
| | - Firas Fneish
- German MS Registry, MS Forschungs- und Projektentwicklungs-gGmbH (MS Research and Project Development gGmbH [MSFP]), Hannover, Germany
| | | | | | | | | | | | - Christoph Kleinschnitz
- Department of Neurology and Center of Translational and Behavioral Neurosciences (C-TNBS), University Hospital Essen, Essen, Germany
| | - Friedemann Paul
- Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Uwe K. Zettl
- Department of Neurology, Neuroimmunological Section, University Medical Center of Rostock, Rostock, Germany
| | - Melanie Peters
- German MS Registry, Gesellschaft für Versorgungsforschung mbH (Society for Health Care Research [GfV]), Hannover, Germany
| | - Clemens Warnke
- Department of Neurology, Medical Faculty, University Hospital of Cologne, Cologne, Germany
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Idrobo-Ávila E, Bognár G, Krefting D, Penzel T, Kovács P, Spicher N. Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis. IEEE Open J Eng Med Biol 2024; 5:250-260. [PMID: 38766543 PMCID: PMC11100950 DOI: 10.1109/ojemb.2024.3379733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/22/2024] [Accepted: 03/12/2024] [Indexed: 05/22/2024] Open
Abstract
Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis - which involves their joint analysis - can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. Methods: We applied widely known algorithms entitled "signal quality indicators" to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. Results: 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. Conclusions: The majority of data within VitalDB is suitable for multimodal analysis.
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Affiliation(s)
- Ennio Idrobo-Ávila
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
| | - Gergő Bognár
- Department of Numerical Analysis, Faculty of InformaticsEötvös Loránd University1117BudapestHungary
| | - Dagmar Krefting
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
| | - Thomas Penzel
- Interdisciplinary Center of Sleep MedicineCharité - Universitätsmedizin Berlin10117BerlinGermany
| | - Péter Kovács
- Department of Numerical Analysis, Faculty of InformaticsEötvös Loránd University1117BudapestHungary
| | - Nicolai Spicher
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
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Krefting D, Mutters NT, Pryss R, Sedlmayr M, Boeker M, Dieterich C, Koll C, Mueller M, Slagman A, Waltemath D, Wulf A, Zenker S. Herding Cats in Pandemic Times - Towards Technological and Organizational Convergence of Heterogeneous Solutions for Investigating and Mastering the Pandemic in University Medical Centers. Stud Health Technol Inform 2024; 310:1271-1275. [PMID: 38270019 DOI: 10.3233/shti231169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
To understand and handle the COVID-19 pandemic, digital tools and infrastructures were built in very short timeframes, resulting in stand-alone and non-interoperable solutions. To shape an interoperable, sustainable, and extensible ecosystem to advance biomedical research and healthcare during the pandemic and beyond, a short-term project called "Collaborative Data Exchange and Usage" (CODEX+) was initiated to integrate and connect multiple COVID-19 projects into a common organizational and technical framework. In this paper, we present the conceptual design, provide an overview of the results, and discuss the impact of such a project for the trade-off between innovation and sustainable infrastructures.
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Affiliation(s)
- Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Nico T Mutters
- Institute for Hygiene and Public Health, Bonn University Hospital, Germany
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, TU Dresden, Germany
| | - Martin Boeker
- Institute of Artificial Intelligence and Informatics in Medicine, Chair of Medical Informatics, Medical Center rechts der Isar, Technical University of Munich
| | | | - Carolin Koll
- Department I for Internal Medicine, University Hospital of Cologne, Germany
| | - Martina Mueller
- Department of Internal Medicine I, University Hospital Regensburg, Germany
| | - Anna Slagman
- Department of Emergency Medicine, Charité - Universitätsmedizin Berlin, Germany
| | - Dagmar Waltemath
- Medical Informatics Laboratory, University Medicine Greifswald, Germany
| | - Antje Wulf
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany
- Big Data in Medicine, Carl von Ossietzky University Oldenburg, Germany
| | - Sven Zenker
- Staff Unit for Medical & Scientific Technology Development & Coordination, Bonn University Hospital, Germany
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Tilch K, Hopff SM, Appel K, Kraus M, Lorenz-Depiereux B, Pilgram L, Anton G, Berger S, Geisler R, Haas K, Illig T, Krefting D, Lorbeer R, Mitrov L, Muenchhoff M, Nauck M, Pley C, Reese JP, Rieg S, Scherer M, Stecher M, Stellbrink C, Valentin H, Winter C, Witzenrath M, Vehreschild JJ. Ethical and coordinative challenges in setting up a national cohort study during the COVID-19 pandemic in Germany. BMC Med Ethics 2023; 24:84. [PMID: 37848886 PMCID: PMC10583323 DOI: 10.1186/s12910-023-00959-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/22/2023] [Indexed: 10/19/2023] Open
Abstract
With the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), global researchers were confronted with major challenges. The German National Pandemic Cohort Network (NAPKON) was launched in fall 2020 to effectively leverage resources and bundle research activities in the fight against the coronavirus disease 2019 (COVID-19) pandemic. We analyzed the setup phase of NAPKON as an example for multicenter studies in Germany, highlighting challenges and optimization potential in connecting 59 university and nonuniversity study sites. We examined the ethics application process of 121 ethics submissions considering durations, annotations, and outcomes. Study site activation and recruitment processes were investigated and related to the incidence of SARS-CoV-2 infections. For all initial ethics applications, the median time to a positive ethics vote was less than two weeks and 30 of these study sites (65%) joined NAPKON within less than three weeks each. Electronic instead of postal ethics submission (9.5 days (Q1: 5.75, Q3: 17) vs. 14 days (Q1: 11, Q3: 26), p value = 0.01) and adoption of the primary ethics vote significantly accelerated the ethics application process. Each study center enrolled a median of 37 patients during the 14-month observation period, with large differences depending on the health sector. We found a positive correlation between recruitment performance and COVID-19 incidence as well as hospitalization incidence. Our analysis highlighted the challenges and opportunities of the federated system in Germany. Digital ethics application tools, adoption of a primary ethics vote and standardized formal requirements lead to harmonized and thus faster study initiation processes during a pandemic.
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Affiliation(s)
- Katharina Tilch
- Faculty of Medicine, Department I of Internal Medicine, Center for Integrated Oncology, Aachen Bonn Cologne Duesseldorf, University of Cologne, University Hospital Cologne, Cologne, Germany.
| | - Sina M Hopff
- Faculty of Medicine, Department I of Internal Medicine, Center for Integrated Oncology, Aachen Bonn Cologne Duesseldorf, University of Cologne, University Hospital Cologne, Cologne, Germany
| | - Katharina Appel
- Department of Internal Medicine, Hematology/Oncology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Monika Kraus
- Helmholtz Center Munich, Institute of Epidemiology, Research Unit Molecular Epidemiology, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich, Munich, Germany
| | - Bettina Lorenz-Depiereux
- Helmholtz Center Munich, Institute of Epidemiology, Research Unit Molecular Epidemiology, Munich, Germany
| | - Lisa Pilgram
- Department of Internal Medicine, Hematology/Oncology, Goethe University Frankfurt, Frankfurt am Main, Germany
- Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gabi Anton
- Helmholtz Center Munich, Institute of Epidemiology, Research Unit Molecular Epidemiology, Munich, Germany
- German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Sarah Berger
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany
| | - Ramsia Geisler
- Department of Internal Medicine, Hematology/Oncology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Kirsten Haas
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Julius Maximilian University of Würzburg, Würzburg, Germany
- University Hospital Würzburg, Institute for Medical Data Science (ImDS), Josef-Schneider Straße 2, 97080, Würzburg, Germany
| | - Thomas Illig
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Roberto Lorbeer
- German Center for Cardiovascular Research (DZHK), Partner Site Munich, Munich, Germany
- Deutsches Herzzentrum der Charité, Medical Heart Center of Charité and German Heart Institute Berlin, Institute of Computer-Assisted Cardiovascular Medicine, Berlin, Germany
- Department of Radiology, University Hospital LMU Munich, Munich, Germany
| | - Lazar Mitrov
- Faculty of Medicine, Department I of Internal Medicine, Center for Integrated Oncology, Aachen Bonn Cologne Duesseldorf, University of Cologne, University Hospital Cologne, Cologne, Germany
| | - Maximilian Muenchhoff
- German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany
- Max Von Pettenkofer Institute & GeneCenter, Virology, Faculty of Medicine, Ludwig-Maximilians University, Munich, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Christina Pley
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany
| | - Jens-Peter Reese
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Julius Maximilian University of Würzburg, Würzburg, Germany
- University Hospital Würzburg, Institute for Medical Data Science (ImDS), Josef-Schneider Straße 2, 97080, Würzburg, Germany
| | - Siegbert Rieg
- Division of Infectious Diseases, Department of Medicine II, Medical Centre - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Margarete Scherer
- Department of Internal Medicine, Hematology/Oncology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Melanie Stecher
- Faculty of Medicine, Department I of Internal Medicine, Center for Integrated Oncology, Aachen Bonn Cologne Duesseldorf, University of Cologne, University Hospital Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), Partner-Site Cologne-Bonn, Cologne, Germany
| | - Christoph Stellbrink
- Bielefeld University, Medical School and University Medical Center East Westphalia-Lippe, Klinikum Bielefeld, Academic Department of Cardiology and Internal Intensive Care Medicine, Bielefeld, Germany
| | - Heike Valentin
- Trusted Third Party of the University Medicine Greifswald, Ellernholzstr. 1-2, 17475, Greifswald, Germany
| | - Christof Winter
- School of Medicine, Institute of Clinical Chemistry and Pathobiochemistry, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Martin Witzenrath
- Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany
- German Center for Lung Research (DZL), Berlin, Germany
| | - J Janne Vehreschild
- Department of Internal Medicine, Hematology/Oncology, Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Infection Research (DZIF), Partner-Site Cologne-Bonn, Cologne, Germany
- Department I for Internal Medicine, Faculty of Medicine, University Hospital of Cologne, University of Cologne, Cologne, Germany
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Dathe H, Krefting D, Spicher N. Completing the Cabrera Circle: deriving adaptable leads from ECG limb leads by combining constraints with a correction factor. Physiol Meas 2023; 44:105005. [PMID: 37673079 DOI: 10.1088/1361-6579/acf754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 09/06/2023] [Indexed: 09/08/2023]
Abstract
Objective.We present a concept for processing 6-lead electrocardiography (ECG) signals which can be applied to various use cases in quantitative electrocardiography.Approach.Our work builds upon the mathematics of the well-known Cabrera sequence which is a re-sorting of the six limb leads (I,II,III,aVR,aVL,aVF) into a clockwise and physiologically-interpretable order. By deriving correction factors for harmonizing lead strengths and choosing an appropriate basis for the leads, we extend this concept towards what we call the 'Cabrera Circle' based on a mathematically sound foundation.Main results.To demonstrate the practical effectiveness and relevance of this concept, we analyze its suitability for deriving interpolated leads between the six limb leads and a 'radial' lead which both can be useful for specific use cases. We focus on the use cases of i) determination of the electrical heart axis by proposing a novel interactive tool for reconstructing the heart's vector loop and ii) improving accuracy in time of automatic R-wave detection and T-wave delineation in 6-lead ECG. For the first use case, we derive an equation which allows projections of the 2-dimensional vector loops to arbitrary angles of the Cabrera Circle. For the second use case, we apply several state-of-the-art algorithms to a freely-available 12-lead dataset (Lobachevsky University Database). Out-of-the-box results show that the derived radial lead outperforms the other limb leads (I,II,III,aVR,aVL,aVF) by improving F1 scores of R-peak and T-peak detection by 0.61 and 2.12, respectively. Results of on- and offset computations are also improved but on a smaller scale.Significance.In summary, the Cabrera Circle offers a methodology that might be useful for quantitative electrocardiography of the 6-lead subsystem-especially in the digital age.
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Affiliation(s)
- Henning Dathe
- Department of Medical Informatics, University Medical Center, Göttingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
- Campus Institute Data Science, Georg-August-University Göttingen, Göttingen, Germany
| | - Nicolai Spicher
- Department of Medical Informatics, University Medical Center, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
- Campus Institute Data Science, Georg-August-University Göttingen, Göttingen, Germany
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Witte ML, Schoneberg A, Hanss S, Lablans M, Vehreschild J, Krefting D. Adaptability of Existing Feasibility Tools for Clinical Study Research Data Platforms. Stud Health Technol Inform 2023; 307:39-48. [PMID: 37697836 DOI: 10.3233/shti230691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
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.
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Affiliation(s)
- Marie-Louise Witte
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Anne Schoneberg
- Department of Medical Informatics, University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Germany
| | - Sabine Hanss
- Department of Medical Informatics, University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Germany
| | - Martin Lablans
- German Cancer Research Center, Heidelberg, Germany
- CDPMI, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Janne Vehreschild
- Department I of Internal Medicine, University Hospital Cologne, Germany
- German Centre for Infection Research, partner site Bonn-Cologne, Germany
- Department II for Internal Medicine, University Hospital Frankfurt, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Germany
- Campus Institute Data Science, Georg-August-University Göttingen, Germany
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Koch M, Richter J, Hauswaldt J, Krefting D. How to Make Outpatient Healthcare Data in Germany Available for Research in the Dynamic Course of Digital Transformation. Stud Health Technol Inform 2023; 307:12-21. [PMID: 37697833 DOI: 10.3233/shti230688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
INTRODUCTION There is increasing interest on re-use of outpatient healthcare data for research, as most medical diagnosis and treatment is provided in the ambulatory sector. One of the early projects to bring primary data from German ambulatory care into clinical research technically, organizationally and in compliance with legal demands has been the RADAR project, that is based on a broad consent and has used the then available practice information system's interfaces to extract and transfer data to a research repository. In course of the digital transformation of the German healthcare system, former standards are abandoned and new interoperability standards, interfaces and regulations on secondary use of patient data are defined, however with slow adoption by Health-IT systems. Therefore, it is of importance for all initiatives that aim at using ambulatory healthcare data for research, how to access this data in an efficient and effective way. METHODS Currently defined healthcare standards are compared regarding coverage of data relevant for research as defined by the RADAR project. We compare four architectural options to access ambulatory health data through different components of healthcare and health research data infrastructures along the technical, organizational and regulatory conditions, the timetable of dissemination and the researcher's perspective. RESULTS A high-level comparison showed a high degree of semantic overlap in the information models used. Electronic patient records and practice information systems are alternative data sources for ambulatory health data - but differ strongly in data richness and accessibility. CONCLUSION Considering the compared dimensions of architectural routes to access health data for secondary research use we conclude that data extraction from practice information systems is currently the most promising way due to data availability on a mid-term perspective. Integration of routine data into the national research data infrastructures might be enforced by convergence of to date different information models.
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Affiliation(s)
- Marius Koch
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Jendrik Richter
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Johannes Hauswaldt
- Department of General Practice, University Medical Center Göttingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Germany
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Bönisch C, Hanß S, Spicher N, Sax U, Krefting D. Reusing Biomedical Data as Agreed - Towards Structured Metadata for Data Use Agreements. Stud Health Technol Inform 2023; 307:31-38. [PMID: 37697835 DOI: 10.3233/shti230690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
INTRODUCTION With increasing availability of reusable biomedical data - from cohort studies to clinical routine data, data re-users face the problem to manage transferred data according to the heterogeneous data use agreements. While structured metadata is addressed in many contexts including informed consent, contracts are to date still unstructured text documents. In particular within collaborative and active working groups the actual usage agreement's regulations are highly relevant for the daily practice - can I share the data with colleagues from the same university or the same research network, can they be stored on a PHD student's laptop, can I store the data for further approved data usage requests? METHODS In this article, we inspect and review seven different data usage agreements. We focus on digital data that is copied and transferred to the requester's environment. RESULTS We identified 24 metadata items in the four main categories data usage, storage, and sharing, as well as publication of results. DISCUSSION While the topics are largely overlap in the data use agreements, the actual regulations of the topics are diverse. Although we do not explicitly investigate trusted research environments, where data is offered within an analytics platform, we consider them a as subgroup, where most of the practical questions from the data scientist's perspective also arise. CONCLUSION With a limited set of structured metadata items, data scientists could have information about the data use agreement at hand along with the transferred data in an easily accessible way.
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Affiliation(s)
- Caroline Bönisch
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Sabine Hanß
- Department of Medical Informatics, University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Germany
| | - Nicolai Spicher
- Department of Medical Informatics, University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Germany
- Campus Institute Data Science, Georg-August-University Göttingen, Germany
| | - Ulrich Sax
- Department of Medical Informatics, University Medical Center Göttingen, Germany
- Campus Institute Data Science, Georg-August-University Göttingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Germany
- Campus Institute Data Science, Georg-August-University Göttingen, Germany
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Yusuf KO, Chaplinskaya-Sobol I, Schoneberg A, Hanss S, Valentin H, Lorenz-Depiereux B, Hansch S, Fiedler K, Scherer M, Sikdar S, Miljukov O, Reese JP, Wagner P, Bröhl I, Geisler R, Vehreschild JJ, Blaschke S, Bellinghausen C, Milovanovic M, Krefting D. Impact of Clinical Study Implementation on Data Quality Assessments - Using Contradictions within Interdependent Health Data Items as a Pilot Indicator. Stud Health Technol Inform 2023; 307:152-158. [PMID: 37697849 DOI: 10.3233/shti230707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
INTRODUCTION Contradiction is a relevant data quality indicator to evaluate the plausibility of interdependent health data items. However, while contradiction assessment is achieved using domain-established contradictory dependencies, recent studies have shown the necessity for additional requirements to reach conclusive contradiction findings. For example, the oral or rectal methods used in measuring the body temperature will influence the thresholds of fever definition. The availability of this required information as explicit data items must be guaranteed during study design. In this work, we investigate the impact of activities related to study database implementation on contradiction assessment from two perspectives including: 1) additionally required metadata and 2) implementation of checks within electronic case report forms to prevent contradictory data entries. METHODS Relevant information (timestamps, measurement methods, units, and interdependency rules) required for contradiction checks are identified. Scores are assigned to these parameters and two different studies are evaluated based on the fulfillment of the requirements by two selected interdependent data item sets. RESULTS None of the studies have fulfilled all requirements. While timestamps and measurement units are found, missing information about measurement methods may impede conclusive contradiction assessment. Implemented checks are only found if data are directly entered. DISCUSSION Conclusive contradiction assessment typically requires metadata in the context of captured data items. Consideration during study design and implementation of data capture systems may support better data quality in studies and could be further adopted in primary health information systems to enhance clinical anamnestic documentation.
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Affiliation(s)
- Khalid O Yusuf
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | | | - Anne Schoneberg
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Sabine Hanss
- Department of Medical Informatics, University Medical Center Göttingen, Germany
- German Center for Cardiovascular Research, Partner Site Göttingen, Germany
| | - Heike Valentin
- Trusted Third Party of the University Medicine Greifswald, Germany
| | | | - Stefan Hansch
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Germany
| | - Karin Fiedler
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Frankfurt am Main, Germany
| | - Margarete Scherer
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Frankfurt am Main, Germany
| | - Shimita Sikdar
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Frankfurt am Main, Germany
| | - Olga Miljukov
- University of Würzburg, Institute for Clinical Epidemiology and Biometry
| | - Jens-Peter Reese
- University of Würzburg, Institute for Clinical Epidemiology and Biometry
| | - Patricia Wagner
- Department I for Internal Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Isabel Bröhl
- Department I for Internal Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Ramsia Geisler
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Frankfurt am Main, Germany
- Department I for Internal Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Jörg J Vehreschild
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Frankfurt am Main, Germany
- Department I for Internal Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Sabine Blaschke
- Emergency Department, University Medical Center Goettingen, Germany
| | - Carla Bellinghausen
- Goethe University Frankfurt, University Hospital Frankfurt, Medical Clinic I, Department of Respiratory Medicine / Allergology
| | - Milena Milovanovic
- Malteser Krankenhaus St. Franziskus Hospital, Medical Clinic I, Flensburg, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Germany
- Campus Institute Data Science, Georg-August-University, Göttingen, Germany
- German Center for Cardiovascular Research, Partner Site Göttingen, Germany
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10
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Tahar K, Martin T, Mou Y, Verbuecheln R, Graessner H, Krefting D. Rare Diseases in Hospital Information Systems-An Interoperable Methodology for Distributed Data Quality Assessments. Methods Inf Med 2023; 62:71-89. [PMID: 36596461 PMCID: PMC10462432 DOI: 10.1055/a-2006-1018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 11/10/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND Multisite research networks such as the project "Collaboration on Rare Diseases" connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data. OBJECTIVES The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research. METHODS We first developed a conceptual framework for DQ assessment. Using this theoretical guidance, we implemented a software framework that provides appropriate tools for calculating DQ metrics and for generating local as well as cross-institutional reports. We further applied our methodology on synthetic data distributed across multiple hospitals using Personal Health Train. Finally, we used precision and recall as metrics to validate our implementation. RESULTS Four DQ dimensions were defined and represented as disjunct ontological categories. Based on these top dimensions, 9 DQ concepts, 10 DQ indicators, and 25 DQ parameters were developed and applied to different data sets. Randomly introduced DQ issues were all identified and reported automatically. The generated reports show the resulting DQ indicators and detected DQ issues. CONCLUSION We have shown that our approach yields promising results, which can be used for local and cross-institutional DQ assessments. The developed frameworks provide useful methods for interoperable and privacy-preserving assessments of DQ that meet the specified requirements. This study has demonstrated that our methodology is capable of detecting DQ issues such as ambiguity or implausibility of coded diagnoses. It can be used for DQ benchmarking to improve the quality of RD documentation and to support clinical research on distributed data.
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Affiliation(s)
- Kais Tahar
- Department of Medical Informatics, University Medical Center Göttingen, Georg-August-University, Göttingen, Germany
| | - Tamara Martin
- Centre for Rare Diseases, University Hospital Tübingen, Tübingen, Germany
| | - Yongli Mou
- Chair of Computer Science 5, RWTH Aachen University, Aachen, Germany
| | - Raphael Verbuecheln
- Medical Data Integration Center, University Hospital Tübingen, Tübingen, Germany
| | - Holm Graessner
- Centre for Rare Diseases, University Hospital Tübingen, Tübingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Georg-August-University, Göttingen, Germany
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11
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Wolfien M, Ahmadi N, Fitzer K, Grummt S, Heine KL, Jung IC, Krefting D, Kühn A, Peng Y, Reinecke I, Scheel J, Schmidt T, Schmücker P, Schüttler C, Waltemath D, Zoch M, Sedlmayr M. Ten Topics to Get Started in Medical Informatics Research. J Med Internet Res 2023; 25:e45948. [PMID: 37486754 PMCID: PMC10407648 DOI: 10.2196/45948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 07/25/2023] Open
Abstract
The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.
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Affiliation(s)
- Markus Wolfien
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
| | - Najia Ahmadi
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kai Fitzer
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
| | - Sophia Grummt
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kilian-Ludwig Heine
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ian-C Jung
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center, Goettingen, Germany
| | - Andreas Kühn
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Yuan Peng
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ines Reinecke
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Tobias Schmidt
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Paul Schmücker
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Christina Schüttler
- Central Biobank Erlangen, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dagmar Waltemath
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Michele Zoch
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
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12
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Tahar K, Verbuecheln R, Martin T, Graessner H, Krefting D. Local Data Quality Assessments on EHR-Based Real-World Data for Rare Diseases. Stud Health Technol Inform 2023; 302:292-296. [PMID: 37203665 DOI: 10.3233/shti230121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
The project "Collaboration on Rare Diseases" CORD-MI connects various university hospitals in Germany to collect sufficient harmonized electronic health record (EHR) data for supporting clinical research in the field of rare diseases (RDs). However, the integration and transformation of heterogeneous data into an interoperable standard through Extract-Transform-Load (ETL) processes is a complex task that may influence the data quality (DQ). Local DQ assessments and control processes are needed to ensure and improve the quality of RD data. We therefore aim to investigate the impact of ETL processes on the quality of transformed RD data. Seven DQ indicators for three independent DQ dimensions were evaluated. The resulting reports show the correctness of calculated DQ metrics and detected DQ issues. Our study provides the first comparison results between the DQ of RD data before and after ETL processes. We found that ETL processes are challenging tasks that influence the quality of RD data. We have demonstrated that our methodology is useful and capable of evaluating the quality of real-world data stored in different formats and structures. Our methodology can therefore be used to improve the quality of RD documentation and to support clinical research.
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Affiliation(s)
- Kais Tahar
- Institute of Medical Informatics, University Medical Center Göttingen, Germany
| | | | - Tamara Martin
- Centre for Rare Diseases, University Hospital Tübingen, Germany
| | - Holm Graessner
- Centre for Rare Diseases, University Hospital Tübingen, Germany
| | - Dagmar Krefting
- Institute of Medical Informatics, University Medical Center Göttingen, Germany
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13
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Yusuf KO, Hanss S, Krefting D. Towards a Consistent Representation of Contradictions Within Health Data for Efficient Implementation of Data Quality Assessments. Stud Health Technol Inform 2023; 302:302-306. [PMID: 37203667 DOI: 10.3233/shti230123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Contradictions as a data quality indicator are typically understood as impossible combinations of values in interdependent data items. While the handling of a single dependency between two data items is well established, for more complex interdependencies, there is not yet a common notation or structured evaluation method established to our knowledge. For the definition of such contradictions, specific biomedical domain knowledge is required, while informatics domain knowledge is responsible for the efficient implementation in assessment tools. We propose a notation of contradiction patterns that reflects the provided and required information by the different domains. We consider three parameters (α, β, θ): the number of interdependent items as α, the number of contradictory dependencies defined by domain experts as β, and the minimal number of required Boolean rules to assess these contradictions as θ. Inspection of the contradiction patterns in existing R packages for data quality assessments shows that all six examined packages implement the (2,1,1) class. We investigate more complex contradiction patterns in the biobank and COVID-19 domains showing that the minimum number of Boolean rules might be significantly lower than the number of described contradictions. While there might be a different number of contradictions formulated by the domain experts, we are confident that such a notation and structured analysis of the contradiction patterns helps to handle the complexity of multidimensional interdependencies within health data sets. A structured classification of contradiction checks will allow scoping of different contradiction patterns across multiple domains and effectively support the implementation of a generalized contradiction assessment framework.
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Affiliation(s)
- Khalid O Yusuf
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Sabine Hanss
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
- German Center for Cardiovascular Research, Partner Site Göttingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
- Campus Institute Data Science (CIDAS), Georg-August-University, Göttingen, Germany
- German Center for Cardiovascular Research, Partner Site Göttingen, Germany
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14
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Krefting D, Anton G, Chaplinskaya-Sobol I, Hanss S, Hoffmann W, Hopff SM, Kraus M, Lorbeer R, Lorenz-Depiereux B, Illig T, Schäfer C, Schaller J, Stahl D, Valentin H, Heuschmann P, Vehreschild J. The Importance of Being FAIR and FAST - The Clinical Epidemiology and Study Platform of the German Network University Medicine (NUKLEUS). Stud Health Technol Inform 2023; 302:93-97. [PMID: 37203616 DOI: 10.3233/shti230071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
The COVID-19 pandemic has urged the need to set up, conduct and analyze high-quality epidemiological studies within a very short time-scale to provide timely evidence on influential factors on the pandemic, e.g. COVID-19 severity and disease course. The comprehensive research infrastructure developed to run the German National Pandemic Cohort Network within the Network University Medicine is now maintained within a generic clinical epidemiology and study platform NUKLEUS. It is operated and subsequently extended to allow efficient joint planning, execution and evaluation of clinical and clinical-epidemiological studies. We aim to provide high-quality biomedical data and biospecimens and make its results widely available to the scientific community by implementing findability, accessibility, interoperability and reusability - i.e. following the FAIR guiding principles. Thus, NUKLEUS might serve as role model for FAIR and fast implementation of clinical epidemiological studies within the setting of University Medical Centers and beyond.
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Affiliation(s)
- Dagmar Krefting
- Dpt. of Medical Informatics, University Medical Center Göttingen, German Center for Cardiovascular Research (DZHK) partner site Göttingen, Germany
- Campus Institute Data Science (CIDAS), Georg-August-University Göttingen, Germany
| | - Gabi Anton
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Irina Chaplinskaya-Sobol
- Dpt. of Medical Informatics, University Medical Center Göttingen, German Center for Cardiovascular Research (DZHK) partner site Göttingen, Germany
| | - Sabine Hanss
- Dpt. of Medical Informatics, University Medical Center Göttingen, German Center for Cardiovascular Research (DZHK) partner site Göttingen, Germany
| | - Wolfgang Hoffmann
- Institute for Community Medicine, University Medicine Greifswald, Germany
| | - Sina M Hopff
- Faculty of Medicine, University of Cologne, Department I of Internal Medicine, University Hospital Cologne, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Cologne, Germany
| | - Monika Kraus
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Roberto Lorbeer
- Medical Heart Center and Institute of Computer-assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Germany
| | - Bettina Lorenz-Depiereux
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Illig
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
| | - Christian Schäfer
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Germany
| | - Jens Schaller
- Medical Heart Center and Institute of Computer-assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Germany
| | - Dana Stahl
- Independent Trusted Third Party of the University Medicine Greifswald, Germany
| | - Heike Valentin
- Independent Trusted Third Party of the University Medicine Greifswald, Germany
| | - Peter Heuschmann
- Institute of Clinical Epidemiology and Biometry, University of Würzburg; Clinical Trial Center, University Hospital Würzburg, Germany
| | - Janne Vehreschild
- Faculty of Medicine, University of Cologne, Department I of Internal Medicine, University Hospital Cologne, Germany
- German Centre for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Department II for Internal Medicine, Hematology/Oncology, University Hospital Frankfurt, Frankfurt am Main, Germany
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15
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Michaelis L, Poyraz RA, Muzoora MR, Gierend K, Bartschke A, Dieterich C, Johann T, Krefting D, Waltemath D, Thun S. Insights into the FAIRness of the German Network University Medicine: A Survey. Stud Health Technol Inform 2023; 302:741-742. [PMID: 37203481 DOI: 10.3233/shti230251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
The need to harness large amounts of data, possibly within a short period of time, became apparent during the Covid-19 pandemic outbreak. In 2022, the Corona Data Exchange Platform (CODEX), which had been developed within the German Network University Medicine (NUM), was extended by a number of common components, including a section on FAIR science. The FAIR principles enable research networks to evaluate how well they comply with current standards in open and reproducible science. To be more transparent, but also to guide scientists on how to improve data and software reusability, we disseminated an online survey within the NUM. Here we present the outcomes and lessons learnt.
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Affiliation(s)
- Lea Michaelis
- Data Integration Center, University Medicine Greifswald, Germany
| | - Rasim Atakan Poyraz
- Core Facility Digital Medicine & Interoperability, BIH at Charité, Berlin, Germany
| | | | - Kerstin Gierend
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Alexander Bartschke
- Core Facility Digital Medicine & Interoperability, BIH at Charité, Berlin, Germany
| | - Christoph Dieterich
- K. Tschira Institute for Integrative Computational Cardiology, Heidelberg, Germany
- German Centre for Cardiovascular Research (DZHK), Heidelberg, Germany
| | - Tim Johann
- K. Tschira Institute for Integrative Computational Cardiology, Heidelberg, Germany
| | - Dagmar Krefting
- Dpt. Of Medical Informatics, University Medical Center Göttingen, Germany
| | - Dagmar Waltemath
- Data Integration Center, University Medicine Greifswald, Germany
- Medical Informatics Laboratory, University Medicine Greifswald, Germany
| | - Sylvia Thun
- Core Facility Digital Medicine & Interoperability, BIH at Charité, Berlin, Germany
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16
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Bender T, Gemke P, Idrobo-Avila E, Dathe H, Krefting D, Spicher N. Benchmarking the Impact of Noise on Deep Learning-Based Classification of Atrial Fibrillation in 12-Lead ECG. Stud Health Technol Inform 2023; 302:977-981. [PMID: 37203548 DOI: 10.3233/shti230321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Electrocardiography analysis is widely used in various clinical applications and Deep Learning models for classification tasks are currently in the focus of research. Due to their data-driven character, they bear the potential to handle signal noise efficiently, but its influence on the accuracy of these methods is still unclear. Therefore, we benchmark the influence of four types of noise on the accuracy of a Deep Learning-based method for atrial fibrillation detection in 12-lead electrocardiograms. We use a subset of a publicly available dataset (PTB-XL) and use the metadata provided by human experts regarding noise for assigning a signal quality to each electrocardiogram. Furthermore, we compute a quantitative signal-to-noise ratio for each electrocardiogram. We analyze the accuracy of the Deep Learning model with respect to both metrics and observe that the method can robustly identify atrial fibrillation, even in cases signals are labelled by human experts as being noisy on multiple leads. False positive and false negative rates are slightly worse for data being labelled as noisy. Interestingly, data annotated as showing baseline drift noise results in an accuracy very similar to data without. We conclude that the issue of processing noisy electrocardiography data can be addressed successfully by Deep Learning methods that might not need preprocessing as many conventional methods do.
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Affiliation(s)
- Theresa Bender
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| | - Philip Gemke
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Ennio Idrobo-Avila
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Henning Dathe
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| | - Nicolai Spicher
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
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17
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Bender T, Beinecke JM, Krefting D, Muller C, Dathe H, Seidler T, Spicher N, Hauschild AC. Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria. IEEE J Biomed Health Inform 2023; PP:1-12. [PMID: 37126621 DOI: 10.1109/jbhi.2023.3271858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Despite their remarkable performance, deep neural networks remain unadopted in clinical practice, which is considered to be partially due to their lack of explainability. In this work, we apply explainable attribution methods to a pre-trained deep neural network for abnormality classification in 12-lead electrocardiography to open this "black box" and understand the relationship between model prediction and learned features. We classify data from two public databases (CPSC 2018, PTB-XL) and the attribution methods assign a "relevance score" to each sample of the classified signals. This allows analyzing what the network learned during training, for which we propose quantitative methods: average relevance scores over a) classes, b) leads, and c) average beats. The analyses of relevance scores for atrial fibrillation and left bundle branch block compared to healthy controls show that their mean values a) increase with higher classification probability and correspond to false classifications when around zero, and b) correspond to clinical recommendations regarding which lead to consider. Furthermore, c) visible P-waves and concordant T-waves result in clearly negative relevance scores in atrial fibrillation and left bundle branch block classification, respectively. Results are similar across both databases despite differences in study population and hardware. In summary, our analysis suggests that the DNN learned features similar to cardiology textbook knowledge.
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18
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Yusuf KO, Miljukov O, Schoneberg A, Hanß S, Wiesenfeldt M, Stecher M, Mitrov L, Hopff SM, Steinbrecher S, Kurth F, Bahmer T, Schreiber S, Pape D, Hofmann AL, Kohls M, Störk S, Stubbe HC, Tebbe JJ, Hellmuth JC, Erber J, Krist L, Rieg S, Pilgram L, Vehreschild JJ, Reese JP, Krefting D. Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections. Methods Inf Med 2023. [PMID: 36596462 DOI: 10.1055/a-2006-1086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies. OBJECTIVES The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models. METHODS All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source. RESULTS Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%. CONCLUSION An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.
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Affiliation(s)
- Khalid O Yusuf
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Olga Miljukov
- Institute for Clinical Epidemiology and Biometry (ICE-B), University of Würzburg, Würzburg, Germany
| | - Anne Schoneberg
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Sabine Hanß
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Martin Wiesenfeldt
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Melanie Stecher
- Department I for Internal Medicine, University Hospital Cologne, Cologne, Germany.,German Centre for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany
| | - Lazar Mitrov
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Cologne, Germany
| | - Sina Marie Hopff
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Cologne, Germany
| | - Sarah Steinbrecher
- Department of Infectious Diseases and Respiratory Medicine, Charité-Universitaetsmedizin Berlin, Berlin, Germany
| | - Florian Kurth
- Department of Infectious Diseases and Respiratory Medicine, Charité-Universitaetsmedizin Berlin, Berlin, Germany
| | - Thomas Bahmer
- Internal Medicine Department I, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany.,Airway Research Center North (ARCN), German Center for Lung Research (DZL), Wöhrendamm Großhansdorf, Germany
| | - Stefan Schreiber
- Internal Medicine Department I, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Daniel Pape
- Internal Medicine Department I, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Anna-Lena Hofmann
- Institute for Clinical Epidemiology and Biometry (ICE-B), University of Würzburg, Würzburg, Germany
| | - Mirjam Kohls
- Institute for Clinical Epidemiology and Biometry (ICE-B), University of Würzburg, Würzburg, Germany
| | - Stefan Störk
- Department Clinical Research & Epidemiology, University Hospital Würzburg, Comprehensive Heart Failure Center, and Department Internal Medicine I, Würzburg, Germany
| | | | - Johannes J Tebbe
- Department of Gastroenterology and Infectious Diseases, University Medical Center East Westphalia-Lippe, Klinikum Lippe, Lemgo, Germany
| | - Johannes C Hellmuth
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Johanna Erber
- Department II of Internal Medicine, Technical University of Munich, School of Medicine, Germany
| | - Lilian Krist
- Institute of Social Medicine, Epidemiology and Health Economics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Siegbert Rieg
- Department of Medicine II, Division of Infectious Diseases, Medical Centre - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lisa Pilgram
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Frankfurt am Main, Germany.,Department of Nephrology and Medical Intensive Care, Charité - Universitaetsmedizin Berlin, Berlin, Germany
| | - Jörg J Vehreschild
- Department I for Internal Medicine, University Hospital Cologne, Cologne, Germany.,German Centre for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany.,Department of Nephrology and Medical Intensive Care, Charité - Universitaetsmedizin Berlin, Berlin, Germany
| | - Jens-Peter Reese
- Institute for Clinical Epidemiology and Biometry (ICE-B), University of Würzburg, Würzburg, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany.,Campus Institute Data Science (CIDAS), Georg-August-University, Göttingen, Germany
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19
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Reinoso Schiller N, Wiesenfeldt M, Loderstädt U, Kaba H, Krefting D, Scheithauer S. Information Technology Systems for Infection Control in German University Hospitals-Results of a Structured Survey a Year into the Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic. Methods Inf Med 2023. [PMID: 36623833 DOI: 10.1055/s-0042-1760222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
BACKGROUND Digitalization is playing a major role in mastering the current coronavirus 2019 (COVID-19) pandemic. However, several outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in German hospitals last year have shown that many of the surveillance and warning mechanisms related to infection control (IC) in hospitals need to be updated. OBJECTIVES The main objective of the following work was to assess the state of information technology (IT) systems supporting IC and surveillance in German university hospitals in March 2021, almost a year into the SARS-CoV-2 pandemic. METHODS As part of the National Research Network for Applied Surveillance and Testing project within the Network University Medicine, a cross-sectional survey was conducted to assess the situation of IC IT systems in 36 university hospitals in Germany. RESULTS Among the most prominent findings were the lack of standardization of IC IT systems and the predominant use of commercial IC IT systems, while the vast majority of hospitals reported inadequacies in the features their IC IT systems provide for their daily work. However, as the pandemic has shown that there is a need for systems that can help improve health care, several German university hospitals have already started this upgrade independently. CONCLUSIONS The deep challenges faced by the German health care sector regarding the integration and interoperability of IT systems designed for IC and surveillance are unlikely to be solved through punctual interventions and require collaboration between educational, medical, and administrative institutions.
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Affiliation(s)
- Nicolás Reinoso Schiller
- Institute of Infection Control and Infectious Diseases, University Medical Center Göttingen, Gottingen, Niedersachsen, Germany
| | - Martin Wiesenfeldt
- Department of Medical Informatics, University Medical Center Göttingen, Gottingen, Niedersachsen, Germany
| | - Ulrike Loderstädt
- Institute of Infection Control and Infectious Diseases, University Medical Center Göttingen, Gottingen, Niedersachsen, Germany
| | - Hani Kaba
- Institute of Infection Control and Infectious Diseases, University Medical Center Göttingen, Gottingen, Niedersachsen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Gottingen, Niedersachsen, Germany
| | - Simone Scheithauer
- Institute of Infection Control and Infectious Diseases, University Medical Center Göttingen, Gottingen, Niedersachsen, Germany
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20
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Bahmer T, Borzikowsky C, Lieb W, Horn A, Krist L, Fricke J, Scheibenbogen C, Rabe KF, Maetzler W, Maetzler C, Laudien M, Frank D, Ballhausen S, Hermes A, Miljukov O, Haeusler KG, Mokhtari NEE, Witzenrath M, Vehreschild JJ, Krefting D, Pape D, Montellano FA, Kohls M, Morbach C, Störk S, Reese JP, Keil T, Heuschmann P, Krawczak M, Schreiber S. Severity, predictors and clinical correlates of Post-COVID syndrome (PCS) in Germany: A prospective, multi-centre, population-based cohort study. EClinicalMedicine 2022; 51:101549. [PMID: 35875815 PMCID: PMC9289961 DOI: 10.1016/j.eclinm.2022.101549] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/16/2022] [Accepted: 06/20/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Post-COVID syndrome (PCS) is an important sequela of COVID-19, characterised by symptom persistence for >3 months, post-acute symptom development, and worsening of pre-existing comorbidities. The causes and public health impact of PCS are still unclear, not least for the lack of efficient means to assess the presence and severity of PCS. METHODS COVIDOM is a population-based cohort study of polymerase chain reaction (PCR) confirmed cases of SARS-CoV-2 infection, recruited through public health authorities in three German regions (Kiel, Berlin, Würzburg) between November 15, 2020 and September 29, 2021. Main inclusion criteria were (i) a PCR confirmed SARS-CoV-2 infection and (ii) a period of at least 6 months between the infection and the visit to the COVIDOM study site. Other inclusion criteria were written informed consent and age ≥18 years. Key exclusion criterion was an acute reinfection with SARS-CoV-2. Study site visits included standardised interviews, in-depth examination, and biomaterial procurement. In sub-cohort Kiel-I, a PCS (severity) score was developed based upon 12 long-term symptom complexes. Two validation sub-cohorts (Würzburg/Berlin, Kiel-II) were used for PCS score replication and identification of clinically meaningful predictors. This study is registered at clinicaltrials.gov (NCT04679584) and at the German Registry for Clinical Studies (DRKS, DRKS00023742). FINDINGS In Kiel-I (n = 667, 57% women), 90% of participants had received outpatient treatment for acute COVID-19. Neurological ailments (61·5%), fatigue (57·1%), and sleep disturbance (57·0%) were the most frequent persisting symptoms at 6-12 months after infection. Across sub-cohorts (Würzburg/Berlin, n = 316, 52% women; Kiel-II, n = 459, 56% women), higher PCS scores were associated with lower health-related quality of life (EQ-5D-5L-VAS/-index: r = -0·54/ -0·56, all p < 0·0001). Severe, moderate, and mild/no PCS according to the individual participant's PCS score occurred in 18·8%, 48·2%, and 32·9%, respectively, of the Kiel-I sub-cohort. In both validation sub-cohorts, statistically significant predictors of the PCS score included the intensity of acute phase symptoms and the level of personal resilience. INTERPRETATION PCS severity can be quantified by an easy-to-use symptom-based score reflecting acute phase disease burden and general psychological predisposition. The PCS score thus holds promise to facilitate the clinical diagnosis of PCS, scientific studies of its natural course, and the development of therapeutic interventions. FUNDING The COVIDOM study is funded by the Network University Medicine (NUM) as part of the National Pandemic Cohort Network (NAPKON).
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Affiliation(s)
- Thomas Bahmer
- Internal Medicine Department I, University Medical Center Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany
- Airway Research Center North (ARCN), German Center for Lung Research (DZL), Wöhrendamm 80, 22927 Großhansdorf, Germany
- Corresponding authors at: Internal Medicine Department I, University Medical Center Schleswig-Holstein, Campus Kiel, Arnold-Heller-Straße 3, 24103 Kiel, Germany.
| | - Christoph Borzikowsky
- Institute of Medical Informatics and Statistics, Kiel University, University Medical Center Schleswig-Holstein, Brunswiker Straße 10, 24105 Kiel, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, Kiel University, University Medical Center Schleswig-Holstein, Niemannsweg 11, 24105 Kiel, Germany
| | - Anna Horn
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
| | - Lilian Krist
- Institute of Social Medicine, Epidemiology and Health Economics, Charité – Universitätsmedizin Berlin, Luisenstr. 57, 10117 Berlin, Germany
| | - Julia Fricke
- Institute of Social Medicine, Epidemiology and Health Economics, Charité – Universitätsmedizin Berlin, Luisenstr. 57, 10117 Berlin, Germany
| | - Carmen Scheibenbogen
- Institute of Medical Immunology, Charité – Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Klaus F. Rabe
- Airway Research Center North (ARCN), German Center for Lung Research (DZL), Wöhrendamm 80, 22927 Großhansdorf, Germany
- LungenClinic Grosshansdorf, Pneumology, Wöhrendamm 80, 22927 Großhansdorf, Germany
| | - Walter Maetzler
- Neurology Department, University Medical Center Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Corina Maetzler
- Neurology Department, University Medical Center Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Martin Laudien
- ENT Department, University Medical Center Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Derk Frank
- Internal Medicine Department III, University Medical Center Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Sabrina Ballhausen
- Internal Medicine Department I, University Medical Center Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Anne Hermes
- Institute of Epidemiology, Kiel University, University Medical Center Schleswig-Holstein, Niemannsweg 11, 24105 Kiel, Germany
| | - Olga Miljukov
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
| | - Karl Georg Haeusler
- Department of Neurology, University Hospital Würzburg, Josef-Schneider-Straße 11, 97080 Würzburg, Germany
| | | | - Martin Witzenrath
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Jörg Janne Vehreschild
- Medical Department 2, Hematology/ Oncology and Infectious Diseases, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department I for Internal Medicine, Kerpener Straße 62, 50937 Cologne, Germany
- German Center for Infection Research (DZIF), Partner Site Bonn-Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Dagmar Krefting
- Institute for Medical Informatics, University Medical Center Göttingen, Von-Siebold-Straße 3, 37075 Göttingen, Germany
| | - Daniel Pape
- Internal Medicine Department I, University Medical Center Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Felipe A. Montellano
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
- Department of Neurology, University Hospital Würzburg, Josef-Schneider-Straße 11, 97080 Würzburg, Germany
- Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, 97080 Würzburg, Germany
| | - Mirjam Kohls
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
| | - Caroline Morbach
- Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, 97080 Würzburg, Germany
- Department of Internal Medicine I, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Stefan Störk
- Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, 97080 Würzburg, Germany
- Department of Internal Medicine I, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Jens-Peter Reese
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
| | - Thomas Keil
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
- Institute of Social Medicine, Epidemiology and Health Economics, Charité – Universitätsmedizin Berlin, Luisenstr. 57, 10117 Berlin, Germany
- State Institute of Health, Bavarian Health and Food Safety Authority, Eggenreuther Weg 43, 91058 Erlangen, Germany
| | - Peter Heuschmann
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
- Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, 97080 Würzburg, Germany
- Clinical Trial Center Würzburg (CTC/ZKS), University Hospital Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
| | - Michael Krawczak
- Institute of Medical Informatics and Statistics, Kiel University, University Medical Center Schleswig-Holstein, Brunswiker Straße 10, 24105 Kiel, Germany
| | - Stefan Schreiber
- Internal Medicine Department I, University Medical Center Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany
- Corresponding authors at: Internal Medicine Department I, University Medical Center Schleswig-Holstein, Campus Kiel, Arnold-Heller-Straße 3, 24103 Kiel, Germany.
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21
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Yusuf K, Tahar K, Sax U, Hoffmann W, Krefting D. Assessment of the Consistency of Categorical Features Within the DZHK Biobanking Basic Set. Stud Health Technol Inform 2022; 296:98-106. [PMID: 36073494 DOI: 10.3233/shti220809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Data quality in health research encompasses a broad range of aspects and indicators. While some indicators are generic and can be calculated without domain knowledge, others require information about a specific data element. Even more complex are indicators addressing contradictions, that stem from implausible combinations of multiple data elements. In this paper, we investigate how contradictions within interdependent categorical data can be identified and if they give additional information about possible quality issues, their cause, and mitigation options. The 19 data elements that represent four biosample types including their pre-analytic states within the DZHK Biobanking basic set are exported to the CDISC Operational Data Model (ODM), transformed and loaded into a tranSMART instance. Through the implementation of a data quality assessment workflow as a SmartR plug-in, statistical information about the domain-specific consistency of interdependent values are retrieved, assessed, and visualized. Data quality indicators have been selected for the assessment according to common recommendations found in the literature. Different contradictions could be discovered in the dataset including mismatch of interdependent values in the pre-analytic states of blood and urine samples, as well as primary and aliquoted samples. The overall assessment rating shows that 99.61% of the interdependent values are free of contradictions. However, measures within the EDC design to avoid contradictions may result in overestimated missing rates in automatic, item-based quality assessment checks. Through consistency checks on interdependent categorical features, we demonstrated that consistency flaws can be found in the categorical data of biobanking metadata and that they can help to detect issues in the data entry process. Our approach underscores the importance of domain knowledge in the definition of the consistency rules but also knowledge about the EDC implementation of such consistency rules to consider the impact on item-based quality indicators.
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Affiliation(s)
- Khalid Yusuf
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Kais Tahar
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Ulrich Sax
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
- Campus Institute Data Science (CIDAS), Georg August-University, Göttingen, Germany
| | - Wolfgang Hoffmann
- DZHK (German Centre for Cardiovascular Research)
- Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
- Campus Institute Data Science (CIDAS), Georg August-University, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research)
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22
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Schons M, Pilgram L, Reese JP, Stecher M, Anton G, Appel KS, Bahmer T, Bartschke A, Bellinghausen C, Bernemann I, Brechtel M, Brinkmann F, Brünn C, Dhillon C, Fiessler C, Geisler R, Hamelmann E, Hansch S, Hanses F, Hanß S, Herold S, Heyder R, Hofmann AL, Hopff SM, Horn A, Jakob C, Jiru-Hillmann S, Keil T, Khodamoradi Y, Kohls M, Kraus M, Krefting D, Kunze S, Kurth F, Lieb W, Lippert LJ, Lorbeer R, Lorenz-Depiereux B, Maetzler C, Miljukov O, Nauck M, Pape D, Püntmann V, Reinke L, Römmele C, Rudolph S, Sass J, Schäfer C, Schaller J, Schattschneider M, Scheer C, Scherer M, Schmidt S, Schmidt J, Seibel K, Stahl D, Steinbeis F, Störk S, Tauchert M, Tebbe JJ, Thibeault C, Toepfner N, Ungethüm K, Vadasz I, Valentin H, Wiedmann S, Zoller T, Nagel E, Krawczak M, von Kalle C, Illig T, Schreiber S, Witzenrath M, Heuschmann P, Vehreschild JJ. The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics. Eur J Epidemiol 2022; 37:849-870. [PMID: 35904671 PMCID: PMC9336157 DOI: 10.1007/s10654-022-00896-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 06/22/2022] [Indexed: 11/25/2022]
Abstract
The German government initiated the Network University Medicine (NUM) in early 2020 to improve national research activities on the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. To this end, 36 German Academic Medical Centers started to collaborate on 13 projects, with the largest being the National Pandemic Cohort Network (NAPKON). The NAPKON’s goal is creating the most comprehensive Coronavirus Disease 2019 (COVID-19) cohort in Germany. Within NAPKON, adult and pediatric patients are observed in three complementary cohort platforms (Cross-Sectoral, High-Resolution and Population-Based) from the initial infection until up to three years of follow-up. Study procedures comprise comprehensive clinical and imaging diagnostics, quality-of-life assessment, patient-reported outcomes and biosampling. The three cohort platforms build on four infrastructure core units (Interaction, Biosampling, Epidemiology, and Integration) and collaborations with NUM projects. Key components of the data capture, regulatory, and data privacy are based on the German Centre for Cardiovascular Research. By April 01, 2022, 34 university and 40 non-university hospitals have enrolled 5298 patients with local data quality reviews performed on 4727 (89%). 47% were female, the median age was 52 (IQR 36–62-) and 50 pediatric cases were included. 44% of patients were hospitalized, 15% admitted to an intensive care unit, and 12% of patients deceased while enrolled. 8845 visits with biosampling in 4349 patients were conducted by April 03, 2022. In this overview article, we summarize NAPKON’s design, relevant milestones including first study population characteristics, and outline the potential of NAPKON for German and international research activities. Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT04768998.https://clinicaltrials.gov/ct2/show/NCT04747366.https://clinicaltrials.gov/ct2/show/NCT04679584
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Affiliation(s)
- Maximilian Schons
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lisa Pilgram
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Germany
| | - Jens-Peter Reese
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Melanie Stecher
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), Partner-Site Cologne-Bonn, Cologne, Germany
| | - Gabriele Anton
- Institute of Epidemiology, Helmholtz Center Munich, Munich, Germany
- German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Katharina S. Appel
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Germany
| | - Thomas Bahmer
- Internal Medicine Department I, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
- Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany
| | - Alexander Bartschke
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Carla Bellinghausen
- Department of Respiratory Medicine and Allergology, Medical Clinic 1, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Inga Bernemann
- Hannover Medical School, Hannover Unified Biobank, Hannover, Germany
| | - Markus Brechtel
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Folke Brinkmann
- Department of Paediatric Pneumology, Allergy and CF- Centre, University Children’s Hospital, Ruhr- University Bochum, Bochum, Germany
| | - Clara Brünn
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Christine Dhillon
- COVID-19 Task Force, University Hospital Augsburg, Augsburg, Germany
| | - Cornelia Fiessler
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Ramsia Geisler
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Germany
| | - Eckard Hamelmann
- Department of Pediatrics, Children’s Center Bethel, University Hospital East Westphalia, University Bielefeld, Bielefeld, Germany
| | - Stefan Hansch
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Regensburg, Germany
| | - Frank Hanses
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Regensburg, Germany
- Emergency Department, University Hospital Regensburg, Regensburg, Germany
| | - Sabine Hanß
- University Medical Center Göttingen (UMG), Göttingen, Germany
- German Center for Cardiovascular Diseases (DZHK), Berlin, Germany
| | - Susanne Herold
- Department of Internal Medicine V, University Hospital Giessen and Marburg, Justus-Liebig-University Giessen, Giessen, Germany
- Department of Internal Medicine, German Center for Lung Research (DZL), Universities of Giessen and Marburg Lung Center (UGMLC), Justus Liebig University Giessen, Giessen, Germany
- Institute for Lung Health (ILH), Giessen, Germany
| | - Ralf Heyder
- NUM Coordination Office, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Anna-Lena Hofmann
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Sina Marie Hopff
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Horn
- Insitute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Carolin Jakob
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Steffi Jiru-Hillmann
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Thomas Keil
- Insitute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- State Institute of Health, Bavarian Health and Food Safety Authority, Bad Kissingen, Germany
| | - Yascha Khodamoradi
- Department of Infectious Diseases, Medical Clinic 2, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt, Germany
| | - Mirjam Kohls
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Monika Kraus
- Institute of Epidemiology, Helmholtz Center Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich, Munich, Germany
| | - Dagmar Krefting
- University Medical Center Göttingen (UMG), Göttingen, Germany
- German Center for Cardiovascular Diseases (DZHK), Berlin, Germany
| | - Sonja Kunze
- Institute of Epidemiology, Helmholtz Center Munich, Munich, Germany
| | - Florian Kurth
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine, and Department of Medicine I, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, Kiel University, Kiel, Germany
| | - Lena Johanna Lippert
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Roberto Lorbeer
- Department of Radiology, University Hospital, LMU, Munich, Germany
- Medical Heart Center of Charité and German Heart Institute Berlin, Institute of Computer-Assisted Cardiovascular Medicine, Berlin, Germany
| | - Bettina Lorenz-Depiereux
- Institute of Epidemiology, Helmholtz Center Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich, Munich, Germany
| | - Corina Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel University, Kiel, Germany
| | - Olga Miljukov
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Daniel Pape
- Department I of Internal Medicine, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Valentina Püntmann
- German Center for Cardiovascular Diseases (DZHK), Berlin, Germany
- Institute for Experimental and Translational Cardiovascular Imaging, University Hospital Frankfurt am Main, Frankfurt, Germany
| | - Lennart Reinke
- Department of Internal Medicine I, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Christoph Römmele
- COVID-19 Task Force, University Hospital Augsburg, Augsburg, Germany
| | - Stefanie Rudolph
- Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Joint Charité and BIH Clinical Study Center, Charitéplatz 1, 10117 Berlin, Germany
| | - Julian Sass
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Schäfer
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK e.V. (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Jens Schaller
- Medical Heart Center of Charité and German Heart Institute Berlin, Institute of Computer-Assisted Cardiovascular Medicine, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt – Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Mario Schattschneider
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Christian Scheer
- Department of Anesthesiology and Intensive Care Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Margarete Scherer
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Germany
| | - Sein Schmidt
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Clinical Study Center, Charitéplatz 1, 10117 Berlin, Germany
| | - Julia Schmidt
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Kristina Seibel
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Dana Stahl
- German Center for Cardiovascular Diseases (DZHK), Berlin, Germany
- University Medicine Greifswald, Greifswald, Germany
| | - Fridolin Steinbeis
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Stefan Störk
- Comprehensive Heart Failure Center, University and University Hospital Würzburg, Würzburg, Germany
- Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany
| | - Maike Tauchert
- Institute of Epidemiology, Helmholtz Center Munich, Munich, Germany
| | - Johannes Josef Tebbe
- Department of Gastroenterology and Infectious Disease, University Medical Center East Westphalia-Lippe, Klinikum Lippe, Detmold, Germany
| | - Charlotte Thibeault
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Nicole Toepfner
- Department of Pediatrics, Carl Gustav Carus University Hospital, TU Dresden, Dresden, Germany
| | - Kathrin Ungethüm
- Insitute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Istvan Vadasz
- Institute for Lung Health (ILH), Giessen, Germany
- Department of Internal Medicine, University Hospital Giessen and Marburg, Justus Liebig University Giessen, Giessen, Germany
- Universities of Giessen and Marburg Lung Center (UGMLC), German Center for Lung Research (DZL), Frankfurt, Germany
| | - Heike Valentin
- German Center for Cardiovascular Diseases (DZHK), Berlin, Germany
- University Medicine Greifswald, Greifswald, Germany
| | - Silke Wiedmann
- NUM Coordination Office, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Thomas Zoller
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Eike Nagel
- German Center for Cardiovascular Diseases (DZHK), Berlin, Germany
- Institute for Experimental and Translational Cardiovascular Imaging, University Hospital Frankfurt am Main, Frankfurt, Germany
| | - Michael Krawczak
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Christof von Kalle
- Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Joint Charité and BIH Clinical Study Center, Charitéplatz 1, 10117 Berlin, Germany
| | - Thomas Illig
- Hannover Medical School, Hannover Unified Biobank, Hannover, Germany
| | - Stefan Schreiber
- Department of Internal Medicine I, University Hospital Schleswig Holstein, Kiel University, Kiel, Germany
| | - Martin Witzenrath
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
- German Center for Lung Research (DZL), Frankfurt, Germany
| | - Peter Heuschmann
- Insitute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
- Clinical Trial Center Würzburg, University Hospital Würzburg, Würzburg, Germany
| | - Jörg Janne Vehreschild
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt,, Germany
- German Centre for Infection Research (DZIF), Partner Site Bonn-Cologne, Cologne, Germany
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23
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Wulff A, Biermann P, von Landesberger T, Baumgartl T, Schmidt C, Alhaji AY, Schick K, Waldstein P, Zhu Y, Krefting D, Scheithauer S, Marschollek M. Tracing COVID-19 Infection Chains Within Healthcare Institutions - Another Brick in the Wall Against SARS-CoV-2. Stud Health Technol Inform 2022; 290:699-703. [PMID: 35673107 DOI: 10.3233/shti220168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Early anticipation of COVID-19 infection chains within hospitals is of high importance for initiating suitable measures at the right time. Infection control specialists can be supported by application systems able of consolidating and analyzing heterogeneous, up-to-now non-standardized and distributed data needed for tracking COVID-19 infections and infected patients' hospital contacts. We developed a system, Co-Surv-SmICS, assisting in infection chain detection, in an open and standards-based way to ensure reusability of the system across institutions. Data is modelled in alignment to various national modelling initiatives and consensus data definitions, queried in a standardized way by the use of OpenEHR as information modelling standard and its associated model-based query language, analyzed and interactively visualized in the application. A first version has been published and will be enhanced with further features and evaluated in detail with regard to its potentials to support specialists during their work against SARS-CoV-2.
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Affiliation(s)
- Antje Wulff
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany
| | - Pascal Biermann
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany
| | | | | | | | - Alan Yussef Alhaji
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany
- University of Rostock, Rostock
| | - Kristina Schick
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany
| | - Paul Waldstein
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany
- Institute of Medical Informatics, University Medical Center Göttingen (UMG), Georg-August University Göttingen, Göttingen, Germany
| | - Yufei Zhu
- Institute of Medical Informatics, University Medical Center Göttingen (UMG), Georg-August University Göttingen, Göttingen, Germany
| | - Dagmar Krefting
- Institute of Medical Informatics, University Medical Center Göttingen (UMG), Georg-August University Göttingen, Göttingen, Germany
| | - Simone Scheithauer
- Institute of Infection Control and Infectious Diseases, University Medical Center Göttingen (UMG), Georg-August University Göttingen, Germany
| | - Michael Marschollek
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany
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24
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Vogel S, Krefting D. Towards a Generic Description Schema for Clinical Decision Support Systems. Stud Health Technol Inform 2022; 294:119-120. [PMID: 35612029 DOI: 10.3233/shti220409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Many clinical decision support systems (CDSS) have shown good performance in the research context, but only a few have been brought into routine care. Analysis of success or failure factors in the design of CDSS may support translation from development to routine care by guiding CDSS design and development along these factors. In this work, we propose a schema to describe CDSS designs in a consistent way. We focus on design criteria with the aim to investigate the observed translation gap in CDSS. Existing description models on different aspects relevant for CDSS are combined to a comprehensive schema that allows description and comparison of CDSS without limitation to the domain or architecture.
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Affiliation(s)
- Stefan Vogel
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Germany
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25
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Ritter Z, Vogel S, Schultze F, Pischek-Koch K, Schirrmeister W, Walcher F, Röhrig R, Kesztyüs T, Krefting D, Blaschke S. Using Explainable Artificial Intelligence Models (ML) to Predict Suspected Diagnoses as Clinical Decision Support. Stud Health Technol Inform 2022; 294:573-574. [PMID: 35612150 DOI: 10.3233/shti220529] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The complexity of emergency cases and the number of emergency patients have increased dramatically. Due to a reduced or even missing specialist medical staff in the emergency departments (EDs), medical knowledge is often used without professional supervision for the diagnosis. The result is a failure in diagnosis and treatment, even death in the worst case. Secondary: high expenditure of time and high costs. Using accurate patient data from the German national registry of the medical emergency departments (AKTIN-registry, Home - Notaufnahmeregister (aktin.org)), the most 20 frequent diagnoses were selected for creating explainable artificial intelligence (XAI) models as part of the ENSURE project (ENSURE (umg.eu)). 137.152 samples and 51 features (vital signs and symptoms) were analyzed. The XAI models achieved a mean area under the curve (AUC) one-vs-rest of 0.98 for logistic regression (LR) and 0.99 for the random forest (RF), and predictive accuracies of 0.927 (LR) and 0.99 (RF). Based on its grade of explainability and performance, the best model will be incorporated into a portable CDSS to improve diagnoses and outcomes of ED treatment and reduce cost. The CDSS will be tested in a clinical pilot study at EDs of selected hospitals in Germany.
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Affiliation(s)
- Zully Ritter
- Institute of Medical Informatics, University Medicine Göttingen, Georg-August University, Göttingen, Germany
| | - Stefan Vogel
- Institute of Medical Informatics, University Medicine Göttingen, Georg-August University, Göttingen, Germany
| | - Frank Schultze
- Central Emergency Department, University Medicine Göttingen, Georg-August University, Göttingen, Germany
| | - Kerstin Pischek-Koch
- Institute of Medical Informatics, University Medicine Göttingen, Georg-August University, Göttingen, Germany
| | - Wiebke Schirrmeister
- Department of Trauma Surgery, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Felix Walcher
- Department of Trauma Surgery, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.,Institute of Medical Informatics, Medical Faculty of RWTH Aachen University, Aachen, Germany
| | - Rainer Röhrig
- AKTIN-Research Group, Germany.,Institute of Medical Informatics, Medical Faculty of RWTH Aachen University, Aachen, Germany
| | - Tibor Kesztyüs
- Institute of Medical Informatics, University Medicine Göttingen, Georg-August University, Göttingen, Germany
| | - Dagmar Krefting
- Institute of Medical Informatics, University Medicine Göttingen, Georg-August University, Göttingen, Germany
| | - Sabine Blaschke
- Central Emergency Department, University Medicine Göttingen, Georg-August University, Göttingen, Germany.,Institute of Medical Informatics, Medical Faculty of RWTH Aachen University, Aachen, Germany
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26
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Prokosch HU, Bahls T, Bialke M, Eils J, Fegeler C, Gruendner J, Haarbrandt B, Hampf C, Hoffmann W, Hund H, Kampf M, Kapsner LA, Kasprzak P, Kohlbacher O, Krefting D, Mang JM, Marschollek M, Mate S, Müller A, Prasser F, Sass J, Semler S, Stenzhorn H, Thun S, Zenker S, Eils R. The COVID-19 Data Exchange Platform of the German University Medicine. Stud Health Technol Inform 2022; 294:674-678. [PMID: 35612174 DOI: 10.3233/shti220554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
COVID-19 has challenged the healthcare systems worldwide. To quickly identify successful diagnostic and therapeutic approaches large data sharing approaches are inevitable. Though organizational clinical data are abundant, many of them are available only in isolated silos and largely inaccessible to external researchers. To overcome and tackle this challenge the university medicine network (comprising all 36 German university hospitals) has been founded in April 2020 to coordinate COVID-19 action plans, diagnostic and therapeutic strategies and collaborative research activities. 13 projects were initiated from which the CODEX project, aiming at the development of a Germany-wide Covid-19 Data Exchange Platform, is presented in this publication. We illustrate the conceptual design, the stepwise development and deployment, first results and the current status.
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Affiliation(s)
- Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Thomas Bahls
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Martin Bialke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Jürgen Eils
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Fegeler
- GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany
| | - Julian Gruendner
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Birger Haarbrandt
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany
| | - Christopher Hampf
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Wolfgang Hoffmann
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Hauke Hund
- GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany
| | - Marvin Kampf
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Lorenz A Kapsner
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Piotr Kasprzak
- Gesellschaft für wissenschaftliche Datenverarbeitung mbH, Göttingen, Germany
| | - Oliver Kohlbacher
- Institute for Translational Bioinformatics, University Medical Center, Tübingen, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.,Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Jonathan M Mang
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Michael Marschollek
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany
| | - Sebastian Mate
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Armin Müller
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Fabian Prasser
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Julian Sass
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Semler
- TMF - Technology, Methods, and Infrastructure for Networked Medical Research, Berlin, Germany
| | - Holger Stenzhorn
- Institute for Translational Bioinformatics, University Medical Center, Tübingen, Germany.,Institute for Medical Biometry, Epidemiology und Medical Informatics, Saarland University Medical Center, Homburg, Germany
| | - Sylvia Thun
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sven Zenker
- Staff Unit for Scientific & Medical Technology Development & Coordination (MWTek), Commercial Directorate; Institute for Medical Biometry, Informatics & Epidemiology; Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany
| | - Roland Eils
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
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27
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Vogel S, Reiswich A, Ritter Z, Schmucker M, Fuchs A, Pischek-Koch K, Wache S, Esslinger K, Dietrich M, Kesztyüs T, Krefting D, Haag M, Blaschke S. Development of a Clinical Decision Support System for Smart Algorithms in Emergency Medicine. Stud Health Technol Inform 2022; 289:224-227. [PMID: 35062133 DOI: 10.3233/shti210900] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The development of clinical decision support systems (CDSS) is complex and requires user-centered planning of assistive interventions. Especially in the setting of emergency care requiring time-critical decisions and interventions, it is important to adapt a CDSS to the needs of the user in terms of acceptance, usability and utility. In the so-called ENSURE project, a user-centered approach was applied to develop the CDSS intervention. In the context of this paper, we present a path to the first mockup development for a CDSS interface by addressing Campbell's Five Rights within the CDSS workflow.
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Affiliation(s)
- Stefan Vogel
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Andreas Reiswich
- GECKO Institute, Heilbronn University of Applied Sciences, Germany
| | - Zully Ritter
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | | | - Angela Fuchs
- Emergency Department, University Medical Center Göttingen, Germany
| | | | - Stefanie Wache
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Katrin Esslinger
- Emergency Department, University Medical Center Göttingen, Germany
| | - Michael Dietrich
- German Research Center for Artificial Intelligence Berlin, Germany
| | - Tibor Kesztyüs
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Martin Haag
- GECKO Institute, Heilbronn University of Applied Sciences, Germany
| | - Sabine Blaschke
- Emergency Department, University Medical Center Göttingen, Germany
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28
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Frahm N, Fneish F, Ellenberger D, Flachenecker P, Paul F, Warnke C, Kleinschnitz C, Parciak T, Krefting D, Hellwig K, Haas J, Rommer PS, Stahmann A, Zettl UK. Therapy Switches in Fingolimod-Treated Patients with Multiple Sclerosis: Long-Term Experience from the German MS Registry. Neurol Ther 2022; 11:319-336. [PMID: 35020157 PMCID: PMC8857375 DOI: 10.1007/s40120-021-00320-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/21/2021] [Indexed: 11/26/2022] Open
Abstract
INTRODUCTIONS Therapy switches in patients with multiple sclerosis (MS) receiving treatment with fingolimod occur frequently in clinical practice but are not well represented in real-world data. The aim of this study was to identify and characterize treatment switches and reveal sociodemographic/clinical changes over time in fingolimod-treated people with MS (PwMS). METHODS Data on 2536 fingolimod-treated PwMS extracted from the German MS Registry during different time periods were analyzed (2010-2019). RESULTS Overall, 28.3% of PwMS were treatment-naïve before fingolimod initiation. Interferon beta (30.7%) was the most common pre-fingolimod treatment. Ocrelizumab (19.8%) was the most frequent subsequent treatment in the 944 patients on fingolimod who switched. Between 2010 and 2019, median disease duration at fingolimod initiation decreased from 8.5 to 7.1 years (p < 0.001), and patients taking fingolimod for ≥ 1 year after treatment initiation decreased from 89.6 to 80.5% (p < 0.001). Females (p < 0.001) and young patients (p = 0.003) showed a shorter time on fingolimod. The most frequent reason for switching was disease activity (relapse/MRI) despite treatment. The annualized relapse rate increased from 0.37 in patients on fingolimod to 0.47 after treatment cessation, decreasing to 0.19 after treatment with a subsequent disease-modifying drug (DMD) was initiated. CONCLUSION Treatment switches from fingolimod to subsequent DMDs currently occur after shorter treatment durations than 10 years ago, possibly due to the growing treatment spectrum. Planning adequate washout periods is essential and should be done on an individualized basis.
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Affiliation(s)
- Niklas Frahm
- MS Forschungs- Und Projektentwicklungs-gGmbH (MS Research and Project Development gGmbH [MSFP]), Krausenstr. 50, 30171 Hannover, Germany
- Neuroimmunological Section, Department of Neurology, University Medical Center of Rostock, Gehlsheimer Str. 20, 18147 Rostock, Germany
| | - Firas Fneish
- MS Forschungs- Und Projektentwicklungs-gGmbH (MS Research and Project Development gGmbH [MSFP]), Krausenstr. 50, 30171 Hannover, Germany
| | - David Ellenberger
- MS Forschungs- Und Projektentwicklungs-gGmbH (MS Research and Project Development gGmbH [MSFP]), Krausenstr. 50, 30171 Hannover, Germany
| | - Peter Flachenecker
- Neurological Rehabilitation Center Quellenhof, Kuranlagenallee 2, 75323 Bad Wildbad, Germany
| | - Friedemann Paul
- Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité–Universitätsmedizin Berlin, Lindenberger Weg 80, 13125 Berlin, Germany
| | - Clemens Warnke
- Department of Neurology, Medical Faculty, University Hospital of Cologne, Kerpener Str. 62, 50937 Cologne, Germany
| | - Christoph Kleinschnitz
- Department of Neurology and Center of Translational and Behavioral Neurosciences (C-TNBS), University Hospital Essen, Hufelandstr. 55, 45147 Essen, Germany
| | - Tina Parciak
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Str. 3, 37075 Göttingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Str. 3, 37075 Göttingen, Germany
| | - Kerstin Hellwig
- Department of Neurology, St. Joseph and St. Elisabeth Hospital–Ruhr University, Gudrunstr. 56, 44791 Bochum, Germany
| | - Judith Haas
- Deutsche Multiple Sklerose Gesellschaft, Bundesverband e.V. (German Multiple Sclerosis Society [DMSG], Federal Association), Krausenstr. 50, 30171 Hannover, Germany
| | - Paulus S. Rommer
- Neuroimmunological Section, Department of Neurology, University Medical Center of Rostock, Gehlsheimer Str. 20, 18147 Rostock, Germany
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18–20, 1090 Vienna, Austria
| | - Alexander Stahmann
- MS Forschungs- Und Projektentwicklungs-gGmbH (MS Research and Project Development gGmbH [MSFP]), Krausenstr. 50, 30171 Hannover, Germany
| | - Uwe K. Zettl
- Neuroimmunological Section, Department of Neurology, University Medical Center of Rostock, Gehlsheimer Str. 20, 18147 Rostock, Germany
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Muzoora MR, Schaarschmidt M, Krefting D, Oehm J, Riepenhausen S, Thun S. Towards FAIR Patient Reported Outcome: Application of the Interoperability Principle for Mobile Pandemic Apps. Stud Health Technol Inform 2021; 287:85-86. [PMID: 34795087 DOI: 10.3233/shti210820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | - Marco Schaarschmidt
- Berlin Institute of Health (BIH), Germany.,Charitè - Universitätsmedizin Berlin, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Gottingen (UMG), Germany.,Campus-Institute of Data Science (CIDAS) Gottingen, Germany
| | - Johannes Oehm
- Institute of Medical Informatics, University of Munster, Germany
| | | | - Sylvia Thun
- Charitè - Universitätsmedizin Berlin, Germany.,Hochschule Niederrhein - University of Applied Sciences, Krefeld, Germany
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30
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Krefting D, Kesztyüs T, Dathe H. Artefacts in continuous overnight blood pressure assessment based on pulse transit time. Current Directions in Biomedical Engineering 2021. [DOI: 10.1515/cdbme-2021-2215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Continuous non-invasive blood pressure measurements bear a high potential. Particular in Somnology they allow to derive comfortably the systolic and diastolic blood pressure from an electrocardiogram and a synchronous photoplethysmogram without sleep disruption. In this short article some possible problems of this method are discussed along overnight recordings with a SOMNOtouch NIBP device.
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Affiliation(s)
- Dagmar Krefting
- UMG Göttingen, Department of Medical Informatics, Göttingen, Germany, and HTW Berlin, Berlin , Germany
| | - Tibor Kesztyüs
- UMG Göttingen, Department of Medical Informatics, Göttingen , Germany
| | - Henning Dathe
- UMG Göttingen, Department of Medical Informatics, Göttingen , Germany
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31
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Thole D, Ole Diesterhöft T, Vogel S, Greve M, Bauer E, Strathmann S, Elsner C, Kolbe L, Krefting D. Relevant Aspects for Sustainable Open Source Pandemic Apps and Platform Deployment with Focus on Community Building. Stud Health Technol Inform 2021; 283:186-193. [PMID: 34545835 DOI: 10.3233/shti210559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The global COVID-19 pandemic revealed the necessity for mobile and web-based solutions for a variety of medical processes, e.g., individual risk calculation, communication of health information and contact tracing. Many such solutions are provided in form of open source software. However, there are major obstacles to the sustainable long-term continuation of such projects. As the topic of sustainability strategies is complex, a classification would be useful to help new projects to identify relevant sustainability factors. Based on a literature review a classification for long-term success of open source software was created. This paper presents a classification focusing on five unique categories: (1) structural decision, (2) revenue generation, (3) user focus, (4) openness and (5) community building. It was developed within the NUM-COMPASS project, focusing content-wise on pandemic apps and structure-wise on open-source provision. We provide some insights into the community building dimension by discussing factors that go into building sustainable communities.
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Affiliation(s)
- Daniel Thole
- Department of Medical Informatics, Univ. Medical Center Göttingen, Germany
| | - Till Ole Diesterhöft
- Georg August Universität Göttingen, Germany.,Chair of Information Management, University of Göttingen, Germany
| | - Stefan Vogel
- Department of Medical Informatics, Univ. Medical Center Göttingen, Germany
| | - Maike Greve
- Georg August Universität Göttingen, Germany.,Chair of Information Management, University of Göttingen, Germany
| | - Elena Bauer
- Georg August Universität Göttingen, Germany.,Chair of Information Management, University of Göttingen, Germany
| | - Stefan Strathmann
- Department of Medical Informatics, Univ. Medical Center Göttingen, Germany.,Göttingen State and University Library, Germany
| | | | - Lutz Kolbe
- Georg August Universität Göttingen, Germany.,Chair of Information Management, University of Göttingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, Univ. Medical Center Göttingen, Germany
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32
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Beierle F, Schobel J, Vogel C, Allgaier J, Mulansky L, Haug F, Haug J, Schlee W, Holfelder M, Stach M, Schickler M, Baumeister H, Cohrdes C, Deckert J, Deserno L, Edler JS, Eichner FA, Greger H, Hein G, Heuschmann P, John D, Kestler HA, Krefting D, Langguth B, Meybohm P, Probst T, Reichert M, Romanos M, Störk S, Terhorst Y, Weiß M, Pryss R. Corona Health-A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic. Int J Environ Res Public Health 2021; 18:ijerph18147395. [PMID: 34299846 PMCID: PMC8303497 DOI: 10.3390/ijerph18147395] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 01/09/2023]
Abstract
Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
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Affiliation(s)
- Felix Beierle
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
- Correspondence:
| | - Johannes Schobel
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, 89231 Neu-Ulm, Germany;
| | - Carsten Vogel
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Johannes Allgaier
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Lena Mulansky
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Fabian Haug
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany; (F.H.); (M.S.); (M.S.); (M.R.)
| | - Julian Haug
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Winfried Schlee
- Department of Psychiatry and Psychotherapy, University Regensburg, 93053 Regensburg, Germany; (W.S.); (B.L.)
| | | | - Michael Stach
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany; (F.H.); (M.S.); (M.S.); (M.R.)
| | - Marc Schickler
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany; (F.H.); (M.S.); (M.S.); (M.R.)
| | - Harald Baumeister
- Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany; (H.B.); (Y.T.)
| | - Caroline Cohrdes
- Mental Health Research Unit, Department of Epidemiology and Health Monitoring, Robert Koch Institute, 12101 Berlin, Germany; (C.C.); (J.-S.E.)
| | - Jürgen Deckert
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, 97080 Würzburg, Germany; (J.D.); (G.H.); (M.W.)
| | - Lorenz Deserno
- Department of Child and Adolescent Psychiatry, University Hospital Würzburg, 97080 Würzburg, Germany; (L.D.); (M.R.)
| | - Johanna-Sophie Edler
- Mental Health Research Unit, Department of Epidemiology and Health Monitoring, Robert Koch Institute, 12101 Berlin, Germany; (C.C.); (J.-S.E.)
| | - Felizitas A. Eichner
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Helmut Greger
- Service Center Medical Informatics, University Hospital Würzburg, 97080 Würzburg, Germany;
| | - Grit Hein
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, 97080 Würzburg, Germany; (J.D.); (G.H.); (M.W.)
| | - Peter Heuschmann
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Dennis John
- Lutheran University of Applied Sciences Nürnberg, 90429 Nürnberg, Germany;
| | - Hans A. Kestler
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany;
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, 37075 Göttingen, Germany;
| | - Berthold Langguth
- Department of Psychiatry and Psychotherapy, University Regensburg, 93053 Regensburg, Germany; (W.S.); (B.L.)
| | - Patrick Meybohm
- Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Würzburg, 97080 Würzburg, Germany;
| | - Thomas Probst
- Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, 3500 Krems, Austria;
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany; (F.H.); (M.S.); (M.S.); (M.R.)
| | - Marcel Romanos
- Department of Child and Adolescent Psychiatry, University Hospital Würzburg, 97080 Würzburg, Germany; (L.D.); (M.R.)
| | - Stefan Störk
- Comprehensive Heart Failure Center, University and University Hospital Würzburg, 97080 Würzburg, Germany;
- Department of Internal Medicine I, University Hospital Würzburg, 97080 Würzburg, Germany
| | - Yannik Terhorst
- Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany; (H.B.); (Y.T.)
| | - Martin Weiß
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, 97080 Würzburg, Germany; (J.D.); (G.H.); (M.W.)
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
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33
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Muzoora MR, El-Badawi N, Elsner C, Essenwanger A, Gocke P, Krefting D, Poyraz RA, Pryss R, Sax U, Thun S. Motivating Developers to Use Interoperable Standards for Data in Pandemic Health Apps. Stud Health Technol Inform 2021; 281:1027-1028. [PMID: 34042834 DOI: 10.3233/shti210339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The COVID-19 pandemic has brought along a massive increase in app development. However, most of these apps are not using interoperable data. The COMPASS project of the German COVID-19 Research Network of University Medicine ("Netzwerk Universitätsmedizin (NUM)") tackles this issue, by offering open-source technology, best practice catalogues, and suggestions for designing interoperable pandemic health applications (https://www.netzwerk-universitaetsmedizin.de/projekte/compass). Therefore, COMPASS conceived a framework that includes automated conformity checks as well as reference implementations for more efficient and pandemic-tailored app developments. It further aims to motivate and support developers to use interoperable standards.
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Affiliation(s)
- Michael Rusongoza Muzoora
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany.,Charité - Universitätsmedizin Berlin, Germany
| | - Nabil El-Badawi
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany.,Charité - Universitätsmedizin Berlin, Germany
| | | | - Andrea Essenwanger
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany
| | - Peter Gocke
- Charité - Universitätsmedizin Berlin, Germany
| | - Dagmar Krefting
- Hochschule Niederrhein - University of Applied Sciences, Krefeld, Germany
| | - Rasim Atakan Poyraz
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany.,Charité - Universitätsmedizin Berlin, Germany
| | | | | | - Sylvia Thun
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany.,Charité - Universitätsmedizin Berlin, Germany.,Hochschule Niederrhein - University of Applied Sciences, Krefeld, Germany
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34
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Peeters LM, Parciak T, Kalra D, Moreau Y, Kasilingam E, van Galen P, Thalheim C, Uitdehaag B, Vermersch P, Hellings N, Stinissen P, Van Wijmeersch B, Ardeshirdavani A, Pirmani A, De Brouwer E, Bauer CR, Krefting D, Ribbe S, Middleton R, Stahmann A, Comi G. Multiple Sclerosis Data Alliance - A global multi-stakeholder collaboration to scale-up real world data research. Mult Scler Relat Disord 2020; 47:102634. [PMID: 33278741 DOI: 10.1016/j.msard.2020.102634] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/09/2020] [Accepted: 11/16/2020] [Indexed: 11/26/2022]
Abstract
The Multiple Sclerosis Data Alliance (MSDA), a global multi-stakeholder collaboration, is working to accelerate research insights for innovative care and treatment for people with multiple sclerosis (MS) through better use of real-world data (RWD). Despite the increasing reliance on RWD, challenges and limitations complicate the generation, collection, and use of these data. MSDA aims to tackle sociological and technical challenges arising with scaling up RWD, specifically focused on MS data. MSDA envisions a patient-centred data ecosystem in which all stakeholders contribute and use big data to co-create the innovations needed to advance timely treatment and care of people with MS.
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Affiliation(s)
- Liesbet M Peeters
- University MS Center, Biomedical Research Institute (BIOMED), Hasselt University, Agoralaan Building C, 3590 Diepenbeek, Belgium; Data Science Institute (DSI), Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium.
| | - Tina Parciak
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Straße 3, 37075, Göttingen, Germany
| | - Dipak Kalra
- Unit of Medical Informatics and Statistics, University of Gent, Gent, 9000, Belgium
| | - Yves Moreau
- Department of Electrical Engineering (ESAT-STADIUS), KULeuven, Kasteelpark Arenberg, 10 3001 Leuven, Belgium
| | | | - Pieter van Galen
- European Multiple Sclerosis Platform, Rue A. Lambiotte, 1030, Brussels, Belgium
| | - Christoph Thalheim
- European Multiple Sclerosis Platform, Rue A. Lambiotte, 1030, Brussels, Belgium
| | - Bernard Uitdehaag
- Amsterdam UMC, Vrije Universiteit Amsterdam, Dept. of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, po Box 7057, 1007, MB, Amsterdam, The Netherlands
| | | | - Niels Hellings
- University MS Center, Biomedical Research Institute (BIOMED), Hasselt University, Agoralaan Building C, 3590 Diepenbeek, Belgium
| | - Piet Stinissen
- University MS Center, Biomedical Research Institute (BIOMED), Hasselt University, Agoralaan Building C, 3590 Diepenbeek, Belgium
| | - Bart Van Wijmeersch
- University MS Center, Biomedical Research Institute (BIOMED), Hasselt University, Agoralaan Building C, 3590 Diepenbeek, Belgium
| | - Amin Ardeshirdavani
- Department of Electrical Engineering (ESAT-STADIUS), KULeuven, Kasteelpark Arenberg, 10 3001 Leuven, Belgium
| | - Ashkan Pirmani
- University MS Center, Biomedical Research Institute (BIOMED), Hasselt University, Agoralaan Building C, 3590 Diepenbeek, Belgium; Data Science Institute (DSI), Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium; Department of Electrical Engineering (ESAT-STADIUS), KULeuven, Kasteelpark Arenberg, 10 3001 Leuven, Belgium
| | - Edward De Brouwer
- Department of Electrical Engineering (ESAT-STADIUS), KULeuven, Kasteelpark Arenberg, 10 3001 Leuven, Belgium
| | - Christian Robert Bauer
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Straße 3, 37075, Göttingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Straße 3, 37075, Göttingen, Germany; University of Applied Sciences Berlin, Germany
| | - Stephanie Ribbe
- Novartis International AG. Forum 1. Novartis Campus CH-4056, Basel. Switzerland
| | - Rod Middleton
- UK MS Register, Swansea University, DSB, Swansea University, Singleton Park, Swansea, United Kingdom
| | - Alexander Stahmann
- German MS Register, MS Forschungs- und Projektentwicklungs - gGmbH, Krausenstraße 50, 30171, Hannover, Germany
| | - Giancarlo Comi
- Institute of Experimental Neurology, Ospedale San Raffaele, Via Olgettina, 48, 20132, Milan, Italy
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Benning NH, Haag M, Knaup P, Krefting D, Rienhoff O, Suhr M, Hege I, Tolks D. Digital teaching as an instrument for cross-location teaching networks in medical informatics: opportunities and challenges. GMS J Med Educ 2020; 37:Doc56. [PMID: 33225048 PMCID: PMC7672385 DOI: 10.3205/zma001349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 07/26/2020] [Accepted: 10/01/2020] [Indexed: 05/07/2023]
Abstract
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.
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Affiliation(s)
- Nils-Hendrik Benning
- Heidelberg University Hospital, Institute of Medical Biometry and Informatics, Heidelberg, Germany
- HiGHmed Consortium, Working Group Teaching, Heidelberg, Germany
- *To whom correspondence should be addressed: Nils-Hendrik Benning, Heidelberg University Hospital, Institute of Medical Biometry and Informatics, Im Neuenheimer Feld 130.3, D-69120 Heidelberg, Germany, E-mail:
| | - Martin Haag
- Heilbronn University of applied sciences, GECKO Institute for Medicine, Informatics and Economics, Heilbronn, Germany
- German Society for Medical Informatics, Biometry and Epidemiology, WG Technology-based Teaching and Learning in Medicine, Germany
- Gesellschaft für Medizinische Ausbildung (Society for Medical Education), Committee on Digitization - Technology-Assisted Learning and Teaching, Erlangen, Germany
| | - Petra Knaup
- Heidelberg University Hospital, Institute of Medical Biometry and Informatics, Heidelberg, Germany
- HiGHmed Consortium, Working Group Teaching, Heidelberg, Germany
| | - Dagmar Krefting
- HiGHmed Consortium, Working Group Teaching, Heidelberg, Germany
- University Medical Center Göttingen, Department of Medical Informatics, Göttingen, Germany
- University of Applied Sciences Berlin, Berlin, Germany
| | - Otto Rienhoff
- HiGHmed Consortium, Working Group Teaching, Heidelberg, Germany
- University Medical Center Göttingen, Department of Medical Informatics, Göttingen, Germany
| | - Markus Suhr
- HiGHmed Consortium, Working Group Teaching, Heidelberg, Germany
- University Medical Center Göttingen, Department of Medical Informatics, Göttingen, Germany
| | - Inga Hege
- Gesellschaft für Medizinische Ausbildung (Society for Medical Education), Committee on Digitization - Technology-Assisted Learning and Teaching, Erlangen, Germany
- University of Augsburg, Medical School, Medical Education Sciences, Augsburg, Germany
| | - Daniel Tolks
- Gesellschaft für Medizinische Ausbildung (Society for Medical Education), Committee on Digitization - Technology-Assisted Learning and Teaching, Erlangen, Germany
- LMU Munich, University Hospital, Institute for Medical Education, Munich, Germany
- Leuphana University Lüneburg, Center for Applied Health Sciences, Lüneburg, Germany
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36
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Jansen C, Penzel T, Hodel S, Breuer S, Spott M, Krefting D. Network physiology in insomnia patients: Assessment of relevant changes in network topology with interpretable machine learning models. Chaos 2019; 29:123129. [PMID: 31893662 DOI: 10.1063/1.5128003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/03/2019] [Indexed: 06/10/2023]
Abstract
Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topological changes in different sleep stages. However, the number of network links can quickly grow above the number of parameters that are typically analyzed with standard statistical methods. Artificial Neural Networks (ANNs) are a promising approach as they are successful in large parameter spaces, such as in digital imaging. On the other hand, ANN models do not provide an intrinsic approach to interpret their predictions, and they typically require large training data sets. Both aspects are critical in biomedical research. Medical decisions need to be explainable, and large data sets of quality assured patient and control data are rare. In this paper, different models for the classification of insomnia-a common sleep disorder-have been trained with 59 patients and age and gender matched controls, based on their physiological networks. Feature relevance evaluation is employed for all methods. For ANNs, the extrinsic interpretation method DeepLift is applied. The results are not identical across methods, but certain network links have been rated as relevant by all or most of the models. While ANNs show less classification accuracy (0.89) than advanced tree-based models (0.92 and 0.93), DeepLift provides an in-depth ANN interpretation with feature relevance scores for individual data samples. The analysis revealed modifications in the pulmonar, ocular, and cerebral subnetworks that have not been described before but are consistent with known findings on the physiological impact of insomnia.
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Affiliation(s)
- Christoph Jansen
- Center for Biomedical Image and Information Processing, HTW Berlin-University of Applied Sciences, Berlin 12459, Germany
| | - Thomas Penzel
- Interdisciplinary Sleep Medicine Center, Charité-Universitäsmedizin Berlin, Berlin 11017, Germany
| | - Stephan Hodel
- Center for Biomedical Image and Information Processing, HTW Berlin-University of Applied Sciences, Berlin 12459, Germany
| | - Stefanie Breuer
- Center for Biomedical Image and Information Processing, HTW Berlin-University of Applied Sciences, Berlin 12459, Germany
| | - Martin Spott
- School of Computing, Communication and Business, HTW Berlin-University of Applied Sciences, Berlin 12459, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen 37075, Germany
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37
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Beier M, Penzel T, Krefting D. A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals. Front Neuroinform 2019; 13:65. [PMID: 31607882 PMCID: PMC6769110 DOI: 10.3389/fninf.2019.00065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 09/09/2019] [Indexed: 11/13/2022] Open
Abstract
Biosignal-based research is often multidisciplinary and benefits greatly from multi-site collaboration. This requires appropriate tooling that supports collaboration, is easy to use, and is accessible. However, current software tools do not provide the necessary functionality, usability, and ubiquitous availability. The latter is particularly crucial in environments, such as hospitals, which often restrict users' permissions to install software. This paper introduces a new web-based application for interactive biosignal visualization and assessment. A focus has been placed on performance to allow for handling files of any size. The proposed solution can load local and remote files. It parses data locally on the client, and harmonizes channel labels. The data can then be scored, annotated, pseudonymized and uploaded to a clinical data management system for further analysis. The data and all actions can be interactively shared with a second party. This lowers the barrier to quickly visually examine data, collaborate and make informed decisions.
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Affiliation(s)
- Maximilian Beier
- Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Center for Biomedical Image and Information Processing, University of Applied Sciences, Berlin, Germany
| | - Thomas Penzel
- Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Dagmar Krefting
- Center for Biomedical Image and Information Processing, University of Applied Sciences, Berlin, Germany.,Department of Medical Informatics, University Medical Center Goettingen, Göttingen, Germany
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Khvastova M, Witt M, Krefting D. Towards Interoperability in Clinical Research: Enabling FHIR on the Open Source Research Platform XNAT. Stud Health Technol Inform 2019; 258:3-5. [PMID: 30942702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
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Jansen C, Hodel S, Penzel T, Spott M, Krefting D. Feature relevance in physiological networks for classification of obstructive sleep apnea. Physiol Meas 2018; 39:124003. [PMID: 30524083 DOI: 10.1088/1361-6579/aaf0c9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Physiological networks (PN) model couplings between organs in a high-dimensional parameter space. Machine learning methods, in particular artifical neural networks (ANNs), are powerful on high-dimensional classification tasks. However, lack of interpretability of the resulting models has been a drawback in research. We assess relevant PN topology changes in obstructive sleep apnea (OSA) by novel ANN interpretation techniques. APPROACH ANNs are trained to classify OSA based on the PNs of 48 patients and 48 age and gender matched healthy controls. The PNs consisting of 2812 links are derived from overnight biosignal recordings. The interpretation technique DeepLift is applied to the resulting ANN models, enabling the determination of the relevant features for classification decisions on individual subjects. The mean relevance scores of the features are compared to other machine learning methods (decision tree and random forests) and statistical tests on group differences. MAIN RESULTS The ANN interpretation results show good agreement with the compared methods and 87% of the samples could be correctly classified. OSA patients show a significantly higher coupling (p [Formula: see text] 0.001) in light sleep (N2) between breathing rate and EEG [Formula: see text] power in all electrode locations and to chin and leg muscular tone. In deep sleep (N3), OSA leads to significantly lower coupling (p [Formula: see text] 0.01) in lateral connections of EEG [Formula: see text] and [Formula: see text] power in central and frontal positions. Misclassified OSA patients had all mild/moderate AHIs and did not show PN topology changes. Both nights of these patients have been consistently misclassified as healthy. This may indicate, that the impact of respiratory events differs in subjects, thus forming different phenotypes. SIGNIFICANCE The proposed PN analysis provides a powerful and robust method to quantify a broad range of physiological interactions. Interpretability of the ANN make them a promising tool to identify new diagnostic markers in data-driven approaches.
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Affiliation(s)
- Christoph Jansen
- Center of Biomedical Image and Information Processing, HTW Berlin-University of Applied Sciences Berlin, Berlin, Germany. Sleep Medicine Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
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Zhang X, Kantelhardt JW, Dong XS, Krefting D, Li J, Yan H, Pillmann F, Fietze I, Penzel T, Zhao L, Han F. Nocturnal Dynamics of Sleep–Wake Transitions in Patients With Narcolepsy. Sleep 2016; 40:2740618. [DOI: 10.1093/sleep/zsw050] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2016] [Indexed: 11/13/2022] Open
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Witt M, Krefting D. Multi-Level Data-Security and Data-Protection in a Distributed Search Infrastructure for Digital Medical Samples. Stud Health Technol Inform 2016; 228:807-809. [PMID: 27577500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Human sample data is stored in biobanks with software managing digital derived sample data. When these stand-alone components are connected and a search infrastructure is employed users become able to collect required research data from different data sources. Data protection, patient rights, data heterogeneity and access control are major challenges for such an infrastructure. This dissertation will investigate concepts for a multi-level security architecture to comply with these requirements.
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Blau A, Rodenbeck A, Schmidt M, Drepper J, Wu J, Glos M, Canisius S, Siewert R, Penzel T, Oswald D, Krefting D. Somnonetz - Eine digitale Lösung der schlafmedizinischen Qualitätssicherung? Pneumologie 2013. [DOI: 10.1055/s-0033-1334790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Elgeti T, Tzschätzsch H, Hirsch S, Krefting D, Klatt D, Niendorf T, Braun J, Sack I. Vibration-synchronized magnetic resonance imaging for the detection of myocardial elasticity changes. Magn Reson Med 2012; 67:919-24. [DOI: 10.1002/mrm.24185] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2011] [Revised: 11/18/2011] [Accepted: 01/05/2012] [Indexed: 12/27/2022]
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Streitberger KJ, Sack I, Krefting D, Pfüller C, Braun J, Paul F, Wuerfel J. Brain viscoelasticity alteration in chronic-progressive multiple sclerosis. PLoS One 2012; 7:e29888. [PMID: 22276134 PMCID: PMC3262797 DOI: 10.1371/journal.pone.0029888] [Citation(s) in RCA: 165] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2011] [Accepted: 12/08/2011] [Indexed: 01/11/2023] Open
Abstract
Introduction Viscoelastic properties indicate structural alterations in biological tissues at multiple scales with high sensitivity. Magnetic Resonance Elastography (MRE) is a novel technique that directly visualizes and quantitatively measures biomechanical tissue properties in vivo. MRE recently revealed that early relapsing-remitting multiple sclerosis (MS) is associated with a global decrease of the cerebral mechanical integrity. This study addresses MRE and MR volumetry in chronic-progressive disease courses of MS. Methods We determined viscoelastic parameters of the brain parenchyma in 23 MS patients with primary or secondary chronic progressive disease course in comparison to 38 age- and gender-matched healthy individuals by multifrequency MRE, and correlated the results with clinical data, T2 lesion load and brain volume. Two viscoelastic parameters, the shear elasticity μ and the powerlaw exponent α, were deduced according to the springpot model and compared to literature values of relapsing-remitting MS. Results In chronic-progressive MS patients, μ and α were reduced by 20.5% and 6.1%, respectively, compared to healthy controls. MR volumetry yielded a weaker correlation: Total brain volume loss in MS patients was in the range of 7.5% and 1.7% considering the brain parenchymal fraction. All findings were significant (P<0.001). Conclusions Chronic-progressive MS disease courses show a pronounced reduction of the cerebral shear elasticity compared to early relapsing-remitting disease. The powerlaw exponent α decreased only in the chronic-progressive stage of MS, suggesting an alteration in the geometry of the cerebral mechanical network due to chronic neuroinflammation.
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Affiliation(s)
| | - Ingolf Sack
- Department of Radiology, Charité – University Medicine Berlin, Berlin, Germany
- * E-mail:
| | - Dagmar Krefting
- Department of Radiology, Charité – University Medicine Berlin, Berlin, Germany
| | - Caspar Pfüller
- NeuroCure Clinical Research Center and Experimental and Clinical Research Center, Charité – University Medicine Berlin and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Jürgen Braun
- Institute of Medical Informatics, Charité – University Medicine Berlin, Berlin, Germany
| | - Friedemann Paul
- NeuroCure Clinical Research Center and Experimental and Clinical Research Center, Charité – University Medicine Berlin and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
- Clinical and Experimental Multiple Sclerosis Research Center, Charité - University Medicine Berlin, Berlin, Germany
| | - Jens Wuerfel
- NeuroCure Clinical Research Center and Experimental and Clinical Research Center, Charité – University Medicine Berlin and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
- Institute of Neuroradiology, University Luebeck, Luebeck, Germany
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Korkhov V, Krefting D, Montagnat J, Truong Huu T, Kukla T, Terstyanszky G, Manset D, Caan M, Olabarriaga S. SHIWA workflow interoperability solutions for neuroimaging data analysis. Stud Health Technol Inform 2012; 175:109-110. [PMID: 22941999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Affiliation(s)
- Vladimir Korkhov
- Academic Medical Center, University of Amsterdam, the Netherlands.
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Wu J, Siewert R, Krefting D. Hands-on tutorial on Grid portal development focused on biomedical image and signal processing. Stud Health Technol Inform 2012; 175:104. [PMID: 22941995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Affiliation(s)
- Jie Wu
- Charité-Universitätsmedizin Berlin, Germany.
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Siewert R, Specovius S, Wu J, Krefting D. Web-based interactive visualization in a Grid-enabled neuroimaging application using HTML5. Stud Health Technol Inform 2012; 175:173-181. [PMID: 22942008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Interactive visualization and correction of intermediate results are required in many medical image analysis pipelines. To allow certain interaction in the remote execution of compute- and data-intensive applications, new features of HTML5 are used. They allow for transparent integration of user interaction into Grid- or Cloud-enabled scientific workflows. Both 2D and 3D visualization and data manipulation can be performed through a scientific gateway without the need to install specific software or web browser plugins. The possibilities of web-based visualization are presented along the FreeSurfer-pipeline, a popular compute- and data-intensive software tool for quantitative neuroimaging.
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Affiliation(s)
- René Siewert
- Charité-Universitätsmedizin Berlin, Institute for Medical Informatics, Germany.
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Wu J, Siewert R, Specovius S, Löhnhardt B, Grütz R, Dickmann F, Brandt A, Scheel M, Oswald D, Krefting D. Web-based interactive visualization of Grid-enabled neuroimaging applications. Stud Health Technol Inform 2012; 175:107. [PMID: 22941997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Affiliation(s)
- Jie Wu
- Charité-Universitätsmedizin Berlin, Germany.
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