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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.

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.

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.