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Zusammenfassung Hintergrund Die COVID-19-Pandemie („coronavirus disease 2019“) hat die Bedeutung von Real World Data (RWD) im klinischen Alltag unterstrichen und die fatalen Folgen von längst existierenden Problemen wie Lücken in der Primärdatenerfassung, Hürden bei der Auswertung von Patientendaten sowie erschwertem Patientendatenaustausch zwischen verschiedenen Einrichtungen nochmal deutlich gemacht. Darüber hinaus haben Entwicklungen weg von einem paternalistischen hin zu einem partnerschaftlichen Modell der Arzt-Patienten-Beziehung sowie die zunehmende Digitalisierung unser Verständnis von Gesundheitsversorgung geprägt, das Thema der Patientenautonomie und Selbstwirksamkeit in den Vordergrund gebracht und den Bedarf an innovativen, patientenzentrierten Lösungsansätzen verdeutlicht. Methoden Wir nutzen die „patient journey“ als theoretisches Konstrukt, entlang dessen wir die Sammlung von verschiedenen Typen von RWD, ihre Bedeutung und Umgang damit beschreiben. Schlussfolgerung Die Abbildung der „patient journey“ in Verbindung mit der Nutzung eines einheitlichen Datenstandards kann zur Erfassung von Primärdaten im Gesundheitswesen führen, die von allen medizinischen Behandlungseinrichtungen genutzt werden können. Dies wird den Austausch von Daten zwischen Einrichtungen erleichtern. Darüber hinaus könnte die fortlaufende Auswertung von patientenberichteten Ereignissen als Standard in der klinischen Routine die Patientenautonomie stärken und die Behandlung optimieren. Zusammenfassend lässt sich sagen, dass der Behandlungserfolg, das Gesamtüberleben und das Wohlbefinden der Patienten durch die Schaffung einer gemeinsamen Datensprache und eines ganzheitlichen, menschenzentrierten Ansatzes verbessert werden können.

BackgroundThe COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, researchers must agree on dataset definitions that not only cover all elements relevant to the respective medical specialty but that are also syntactically and semantically interoperable. Following such an effort, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.ObjectiveTo (i) specify a workflow for the development of interoperable dataset definitions that involves a close collaboration between medical experts and information scientists and to (ii) apply the workflow to develop dataset definitions that include data elements most relevant to COVID-19-related patient research inimmunization, pediatrics, andcardiology.MethodsWe developed a workflow to create dataset definitions that are (i) content-wise as relevant as possible to a specific field of study and (ii) universally usable across computer systems, institutions, and countries, i.e., interoperable. We then gathered medical experts from three specialties (immunization, pediatrics, andcardiology) to the select data elements most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications using HL7 FHIR. All steps were performed in close interdisciplinary collaboration between medical domain experts and medical information scientists. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process.ResultsWe created GECCO extension modules for theimmunization, pediatrics, andcardiologydomains with respect to the pandemic requests. The data elements included in each of these modules were selected according to the here developed consensus-based workflow by medical experts from the respective specialty to ensure that the contents are aligned with the respective research needs. We defined dataset specifications for a total number of 48 (immunization), 150 (pediatrics), and 52 (cardiology) data elements that complement the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.ConclusionsThese here presented GECCO extension modules, which contain data elements most relevant to COVID-19-related patient research inimmunization, pediatricsandcardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for the development of further dataset definitions. The GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.

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 December 01, 2021, 34 university and 34 non-university hospitals have enrolled 4,241 patients with local data quality reviews performed on 2,812 (66%). 47% were female, the median age was 53 (IQR: 38-63)) and 3 pediatric cases were included. 30% of patients were hospitalized, 11% admitted to an intensive care unit, and 4% of patients deceased while enrolled. 7,143 visits with biosampling in 3,595 patients were conducted by November 29, 2021. 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 registration:· https://clinicaltrials.gov/ct2/show/NCT04768998· https://clinicaltrials.gov/ct2/show/NCT04747366· https://clinicaltrials.gov/ct2/show/NCT04679584

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

BackgroundThe current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing segmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data.MethodsBased on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.ResultsA core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, anamnesis, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.ConclusionGECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

This article describes the DataBox project which offers a perspective of a new health data management solution in Germany. DataBox was initially conceptualized as a repository of individual lung cancer patient data (structured and unstructured). The patient is the owner of the data and is able to share his or her data with different stakeholders. Data is transferred, displayed, and stored online, but not archived. In the long run, the project aims at replacing the conventional method of paper- and storage-device-based handling of data for all patients in Germany, leading to better organization and availability of data which reduces duplicate diagnostic procedures, treatment errors, and enables the training as well as usage of artificial intelligence algorithms on large datasets.

UNSTRUCTURED This article describes the DataBox project which offers a perspective of a new health data management solution in Germany. DataBox was initially conceptualized as a repository of individual lung cancer patient data (structured and unstructured). The patient is the owner of the data and is able to share his or her data with different stakeholders. Data is transferred, displayed, and stored online, but not archived. In the long run, the project aims at replacing the conventional method of paper- and storage-device-based handling of data for all patients in Germany, leading to better organization and availability of data which reduces duplicate diagnostic procedures, treatment errors, and enables the training as well as usage of artificial intelligence algorithms on large datasets.

Three indolyl- and pyrrolylsilylamines 1–3 were prepared from lithiated heterocyclics and fluorosilylamines. Further reaction with di- tert -butyldifluorosilane led to the fluorosilylpyrrolylsilylamines 4 and 5 . The fluorosilylpyrrolylsilylamine 6 was prepared by reaction of bis(fluorodiisopropylsilyl)amine and lithium pyrrolide. Lithiiation with n-butyllithium of 4–6 solved in THF gave the iminosilane-LiF adducts 7–9 ; in contrast, lithiation of 5 in n-hexane led to the lithium fluorosilylamid 10 . Heating of the iminosilane-LiF adduct 7 gave the bicyclic system 11 , an intramolecularly stabilized iminosilane. Addition of chlorotrimethylsilane to 9 led to fluorine-chlorine exchange and elimination of lithium chloride. The product is the intermolecular stabilization of the intermediate iminosilane as a four-membered ring 12 . The X-ray structures of 1, 11 and 12 have been determined.

New ways to synthesize 1,2- and 1,3-bis(silyl)indoles are shown in this paper and new members of these classes of substances are isolated and characterized The crystal structures of the lithium salt of 2-(tert-butyldimethylsilyl) indole (1) (space group P21/c,a = 1018.0(3), b = 1651.4(3), c = 1648.9(5) pm, β = 94.54(3)°, V = 2.763(1) nm3, ρcalc = 1.090 Mg/m3, Z = 4, 4831 reflections, R1 (I > 2σ(I) = 0.052, wR2 (all data) = 0.152) and 1-(di-tert-butylfluorosilyl)-2-(trimethylsilyl)indole(4) (space group P21/c,a = 1015.5(1), b = 1733.5(3), c = 1261.3(2)pm, β = 105.61(2)°, V = 2.1385(5) nm3, ρcalc = 1.086 Mg/m3, Z = 4, 2779 reflections, R1 (I > 2σ(I) = 0.055, wR2 (all data) = 0.163) are reported. Furthermore the first examples of bis(indol-3-yl)silanes (9, 10) are isolated. Specific reactions for the desilylation of 1, 3-bis(silyl)indoles are discussed.

Di-tert-butylmethylsilylamide reacts with n-BuLi to give the tetrameric lithium di-tert-butylmethylsilylamide 1. Reactions of lithium (di-tert-butylmethylsilyl)amide 1 with SiF4 give the mono- (2), bis- (3), tris- (4), and tetrakis- (5) -[(di-tert-butylmethylsilyl)amino]silanes. Lithiated 3 reacts with Me3SiCl to form (di-tert-butylmethylsilyl-trimethylsilyl)amino-(di-tert-butylmethylsilyl)amino-difluorosilane (6); the 1,3-diaza-2,4-disilacyclobutane 7 is obtained by LiF elimination. The lithium derivative of 6 (compound 8) crystallizes with formation of a (SiFNLi) four-membered ring system. The 19F-NMR spectrum proves that the Li–F bond fluctuates in solution. Reactions of 1 with (Me3Si)2C(SiF3)2 afford different products (9, 10) by controlling the reaction sequence. An excess of 1 reacts to give a four-membered CSi2N ring system, the 1-(di-tert-butylmethylsilyl)-2,4-bis-[(di-tert-butylmethylsilyl)-amino]-2,4-difluoro-3-bis(trimethylsilyl)-1-aza-2,4-disila-cyclobutane (9), disubstitution with formation of bis[(di-tert-butylmethylsilyl)aminodifluorosilyl]bis(trimethylsilyl)methane (10) occurs in a molar ratio of 2:1. The cyclodisilazane 11 is obtained from dilithiated 10 in an intramolecular interconversion reaction. The crystal structures of 1, 8, 9, and 11 are reported.

Di-tert-butyldichlorosilane yields with methylamine (CMe3)2Si(NHMe)2, 1. The dilithium-derivative of 1 (2) reacts with PF3 to give the cyclodiphosph(III)azane, [(CMe3)2SiFNMe[sbnd]P[sbnd]NMe]2, 3. The dithiocyclodiphosph(V)azane; [(CMe3)2SiFNMe[sbnd]SP[sbnd]NMe], 4 is formed by treatment of 3 with sulfur. Reaction mechanisms and crystal structures are discussed. Di-tert-butyldichlorsilan bildet mit Methylamin (CMe3)2Si(NHMe)2 1. Das Dilithiumderivat von 1 (2) reagiert mit PF3 unter Bildung des Cyclodiphosph(III)azans, [(CMe3)2SiFNMe[sbnd]P[sbnd]NMe]2, 3. Das Dithiocyclodiphosph(V)azan, [(CMe3)2SiFNMe[sbnd]SP[sbnd]NMe]2 4 entsteht in der Reaktion von 3 mit Schwefel. Die Reaktionsmechanismen und Kristallstrukturen werden diskutiert.

Four-membered heterocycles (2, 3, 4, 5, 7) are formed in [2 + 2]-cycloadditions of the SiN double bond of di-tert-butyl(di-tertbutylphenylsilyl)iminosilane (1) with compounds including PN (2), CC (3), C0 (4, 5), and CN (7) double bonds. For example: di-tert-butyl(di-tert-butylphenylsilyl)iminosilane (1) reacts with bis(trimethylsilyl)amino-(trimethylsilyl)iminophosphane under formation of the 1,3-diaza-2-phospha-4-silacyclobutane (2). The 1-aza-2-silacyclobutane (3) is obtained in the reaction of the iminosilane (1) with ethylvinylether. Treatment of 1 with 2-methylpropenal leads to the formation of the 1-aza-3-oxa-4-silacyclobutane (4). The 1-aza-3-silacyclobutane (5) is formed in the reaction of the iminosilane 1 and tert-butylisocyanate. A 1,3-silylgroup migration from the nitrogen to the oxygen occurs in the reaction of 1 with trimethylsilylisocyanate. The aminonitril (6) is obtained. The 1,3-diaza-4silacyclobutanone (7) is the product of the reaction of 1 and phenylisocyanate. The crystal structures of 2, 3 and 7 are reported.

Three indolyl- and pyrrolylsilylamines 1–3 were prepared from lithiated heterocyclics and fluorosilylamines. Further reaction with di- tert -butyldifluorosilane led to the fluorosilylpyrrolylsilylamines 4 and 5 . The fluorosilylpyrrolylsilylamine 6 was prepared by reaction of bis(fluorodiisopropylsilyl)amine and lithium pyrrolide. Lithiiation with n-butyllithium of 4–6 solved in THF gave the iminosilane-LiF adducts 7–9 ; in contrast, lithiation of 5 in n-hexane led to the lithium fluorosilylamid 10 . Heating of the iminosilane-LiF adduct 7 gave the bicyclic system 11 , an intramolecularly stabilized iminosilane. Addition of chlorotrimethylsilane to 9 led to fluorine-chlorine exchange and elimination of lithium chloride. The product is the intermolecular stabilization of the intermediate iminosilane as a four-membered ring 12 . The X-ray structures of 1, 11 and 12 have been determined.