Depression and fatigue six months post-COVID-19 disease are associated with overlapping symptom constellations: A prospective, multi-center, population-based cohort study.

BACKGROUND: Depression and fatigue are commonly observed sequelae following viral diseases such as COVID-19. Identifying symptom constellations that differentially classify post-COVID depression and fatigue may be helpful to individualize treatment strategies. Here, we investigated whether self-reported post-COVID depression and post-COVID fatigue are associated with the same or different symptom constellations. METHODS: To address this question, we used data from COVIDOM, a population-based cohort study conducted as part of the NAPKON-POP platform. Data were collected in three different German regions (Kiel, Berlin, Würzburg). We analyzed data from >2000 individuals at least six months past a PCR-confirmed COVID-19 disease, using elastic net regression and cluster analysis. The regression model was developed in the Kiel data set, and externally validated using data sets from Berlin and Würzburg. RESULTS: Our results revealed that post-COVID depression and fatigue are associated with overlapping symptom constellations consisting of difficulties with daily activities, perceived health-related quality of life, chronic exhaustion, unrestful sleep, and impaired concentration. Confirming the overlap in symptom constellations, a follow-up cluster analysis could categorize individuals as scoring high or low on depression and fatigue but could not differentiate between both dimensions. LIMITATIONS: The data presented are cross-sectional, consisting primarily of self-reported questionnaire or medical records rather than biometric data. CONCLUSIONS: In summary, our results suggest a strong link between post-COVID depression and fatigue, highlighting the need for integrative treatment approaches.

Analysis of acute COVID-19 including chronic morbidity: protocol for the deep phenotyping National Pandemic Cohort Network in Germany (NAPKON-HAP).

BACKGROUND: The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) pandemic causes a high burden of acute and long-term morbidity and mortality worldwide despite global efforts in containment, prophylaxis, and therapy. With unprecedented speed, the global scientific community has generated pivotal insights into the pathogen and the host response evoked by the infection. However, deeper characterization of the pathophysiology and pathology remains a high priority to reduce morbidity and mortality of coronavirus disease 2019 (COVID-19). METHODS: NAPKON-HAP is a multi-centered prospective observational study with a long-term follow-up phase of up to 36 months post-SARS-CoV-2 infection. It constitutes a central platform for harmonized data and biospecimen for interdisciplinary characterization of acute SARS-CoV-2 infection and long-term outcomes of diverging disease severities of hospitalized patients. RESULTS: Primary outcome measures include clinical scores and quality of life assessment captured during hospitalization and at outpatient follow-up visits to assess acute and chronic morbidity. Secondary measures include results of biomolecular and immunological investigations and assessment of organ-specific involvement during and post-COVID-19 infection. NAPKON-HAP constitutes a national platform to provide accessibility and usability of the comprehensive data and biospecimen collection to global research. CONCLUSION: NAPKON-HAP establishes a platform with standardized high-resolution data and biospecimen collection of hospitalized COVID-19 patients of different disease severities in Germany. With this study, we will add significant scientific insights and provide high-quality data to aid researchers to investigate COVID-19 pathophysiology, pathology, and chronic morbidity.

Reusing Biomedical Data as Agreed - Towards Structured Metadata for Data Use Agreements.

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.

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

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

Impact of Clinical Study Implementation on Data Quality Assessments - Using Contradictions within Interdependent Health Data Items as a Pilot Indicator.

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.

The Importance of Being FAIR and FAST - The Clinical Epidemiology and Study Platform of the German Network University Medicine (NUKLEUS).

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.

Towards a Consistent Representation of Contradictions Within Health Data for Efficient Implementation of Data Quality Assessments.

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, ß, θ): the number of interdependent items as α, the number of contradictory dependencies defined by domain experts as ß, 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.

The DZHK research platform: maximisation of scientific value by enabling access to health data and biological samples collected in cardiovascular clinical studies.

The German Centre for Cardiovascular Research (DZHK) is one of the German Centres for Health Research and aims to conduct early and guideline-relevant studies to develop new therapies and diagnostics that impact the lives of people with cardiovascular disease. Therefore, DZHK members designed a collaboratively organised and integrated research platform connecting all sites and partners. The overarching objectives of the research platform are the standardisation of prospective data and biological sample collections among all studies and the development of a sustainable centrally standardised storage in compliance with general legal regulations and the FAIR principles. The main elements of the DZHK infrastructure are web-based and central units for data management, LIMS, IDMS, and transfer office, embedded in a framework consisting of the DZHK Use and Access Policy, and the Ethics and Data Protection Concept. This framework is characterised by a modular design allowing a high standardisation across all studies. For studies that require even tighter criteria additional quality levels are defined. In addition, the Public Open Data strategy is an important focus of DZHK. The DZHK operates as one legal entity holding all rights of data and biological sample usage, according to the DZHK Use and Access Policy. All DZHK studies collect a basic set of data and biosamples, accompanied by specific clinical and imaging data and biobanking. The DZHK infrastructure was constructed by scientists with the focus on the needs of scientists conducting clinical studies. Through this, the DZHK enables the interdisciplinary and multiple use of data and biological samples by scientists inside and outside the DZHK. So far, 27 DZHK studies recruited well over 11,200 participants suffering from major cardiovascular disorders such as myocardial infarction or heart failure. Currently, data and samples of five DZHK studies of the DZHK Heart Bank can be applied for.

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections.

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.

The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics.

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 registration https://clinicaltrials.gov/ct2/show/NCT04768998 . https://clinicaltrials.gov/ct2/show/NCT04747366 . https://clinicaltrials.gov/ct2/show/NCT04679584.

Integration of decentralized clinical data in a data warehouse: a service-oriented design and realization.

OBJECTIVES: In this paper we present a general concept and describe the difficulties for the integration of data from various clinical partners in one data warehouse using the Open European Nephrology Science Center (OpEN.SC) as an example. This includes a requirements analysis of the data integration process and also the design according to these requirements. METHODS: This conceptual approach based on the Rational Unified Process (RUP) and paradigm of Service-Oriented Architecture (SOA). RESULTS: Because we have to enhance the confidence of our partners in the OpEN.SC system and with this the willingness of them to participate, important requirements are controllability, transparency and security for all partners. Reusable and fine-grained components were found to be necessary when working with diverse data sources. With SOA the requested reusability is implemented easily. CONCLUSIONS: A key step in the development of a data integration process within such a health information system like OpEN.SC is to analyze the requirements. And to show that this is not only a theoretical work, we present a design - developed with RUP and SOA - which fulfills these requirements.