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    Registratie COVID-19 in Ysis gestart (available in Dutch only)

    Om beter inzicht te krijgen in het beloop van (verdenkingen op) COVID-19 infecties in de ouderenzorg/langdurige zorg roepen wij alle artsen en behandeldiensten op die met het EPD YSIS werken om deel te nemen aan de registratie rondom COVID-19. Door deelname aan deze registratie ontvangt u tevens feedback over de situatie in uw eigen organisatie (available in Dutch only).

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    The COVID-19 Host Genetics Initiative

    The COVID-19 host genetics initiative brings together the human genetics community to generate, share and analyze data to learn the genetic determinants of COVID-19 susceptibility, severity and outcomes. 

    This initiative includes several Duch and European biobanks.

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    ARGUS Project

    Background

    During COVID time patients with suspected pulmonary embolism (PE) and suspected COVID infection may pose challenges to clinicians. In 2017 we have published the results of the YEARS study in Lancet (van der Hulle et al Lancet 2017). In short, this study makes use of a very simple clinical decision rule in which a clinical probability dependent D-dimer test is built in. The results show that this algorithm safely excluded PE with less need for CT-PA scans because of the higher specificity of the D-dimer test compared to conventional D-dimer thresholds. We performed a similar study, called Artemis, in pregnant patients with suspected PE using the YEARS algorithm and performing compression ultrasound to confirm or refute the diagnosis of venous thromboembolism (van der Pol LM et al New Engl J Med 2019). We expect that the advantages of the YEARS algorithm (and Artemis algorithm) also count for patients with COVID and suspected PE. In this particular patient category, symptoms of the COVID infection may resemble those of PE and D-dimer levels may be increased due to inflammation and diffuse intravascular coagulation, both making the diagnostic work-up of those patients even more challenging than usual.

    Research questions

    We are interested in answering the following research questions: 

    1. What is the safety and efficacy the YEARS protocol or any other diagnostic algorithm in patients with (suspected) COVID infection and suspected PE?

    2. We want to evaluate the prevalence of incidental PE in patients with (suspected) COVID infection. 

    3. We are curious whether pregnant patients with suspected PE are being managed according to the Artemis protocol or other diagnostic algorithms.

    4. We are interested in which prophylaxis patients had prior to their suspicion of PE or DVT 

    5. We want to know what happens with patients in whom the diagnosis of PE is established, including what anticoagulant treatment they receive and how they fare over three months.

    How to proceed?

    We ask you to keep record of all COVID patients with suspected or proven PE in your hospital for post-hoc data extraction.

    If you have more time we kindly ask you to collect data in the CASTOR database, which link you can get to after you have sent us your e-mail address: https://www.castoredc.com/. We grant access to all upon confirmation that you will participate. Additional accounts for each site (e.g. for study nurses) can be added on request.

    Informed consent

    This study is non-interventional and therefore no written informed consent is needed. In our hospital, we have permission for an opt-out procedure: all COVID-19 (suspected) patients are asked for permission to use their data for scientific research. A pamphlet with written information is given to the patient and or relatives. 

    If you want to participate with this Argus project, you must obtain local approval for your hospital too, but this can also be done afterwards as soon as we start collecting the data.

    Data collection

    One of our PhD students will collect data on an e-CRF form (Castor database).

    Publications

    We intend to write papers on all data collected. If you are interested and contribute to this project by delivering, we will put you on a short list of contributing authors. Authorships on papers will be determined in a later stage, based on usual criteria.

    How to participate? 

    Please send your details to m.v.huisman@lumc.nl or f.a.klok@lumc.nl

    A minimum is your name affiliation and e-mail address.

    Questions? Remarks? They are very welcome! Please send them to us – we will try to answer as quick as possible.

    Thank you very much for participation in this project.

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    Citizen Science Resources COVID-19

    This is a selection of resources related to the current COVID19 pandemic. It contains links to citizen science and crowdsourcing projects that might be of interest to:

    • citizens wanting to help tackle the virus
    • researchers looking for support during interruptions to their fieldwork
    • parents looking for ideas to support children who are homeschooling
    • anyone looking for useful ways to fill their time while self-isolating.

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    Coronaz app: symptom registration & tracker (available in Dutch only)

    Using our renowned research platform EmmaResearch, we introduced on March 30, a COVID-19 symptoms clinical registration app, called Coronaz, to assist elderly and chronically ill patients to recognize early symptoms of Corona. People fill in a daily symptom list and are warned if they reach a yellow/orange/red zone with instructions. We supply the research data open source to interested research parties. The data will also be used by a spin-off of Radboud University AI dept. to look for causal relationships between illness and symptoms.

    This initiative is also looking for collaborations with hospitals and research institutes with the latest knowledge on COVID-19 symptoms (positive, false positive and false negative) and methodologies for designing optimal questionnaire structures.

    Contact details: 

    Medicine Men BV
    Ir. Oscar van Dijk
    oscar@medicinemen.nl
    +31 85 1307020

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    Systems Biology Amsterdam (SysBA) COVID-19

    1. Model of the COVID-19 epidemics showing that most governments' policies are too-late-too-little, i.e. require change:

    https://medrxiv.org/cgi/content/short/2020.03.29.20045039v1

    2. Model of the cytokine storm elicited by COVID-19 in patients which could be used as predictor of ICU necessity and therapies

    Request for collaboration: Looking for critical forums on COVID-19 epidemic.

    Contact: Hans V. Westerhoff

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    Call for papers: AI driven informatics, sensing, imaging and big data analysis for fighting the COVID-19 pandemic

    The Journal of Biomedical and Health Informatics encourages researchers, who are using informatics to address COVID – 19 issues are to submit high quality data and unpublished work. The submitted manuscripts will be processed through a fast track procedure, and the time from submission to first decision will be limited to 15 days.

    Topics of interest include, but are not limited to, the following:

    • Collection, harmonization, sharing, and visualization of COVID – 19 related data
    • AI-driven exploration of susceptibility and infection in humans
    • Modeling of virus propagation, recurrence and virulence from epidemiological observations
    • AI-driven medical imaging (including chest X-ray and CT) analysis for COVID-19 detection
    • AI-driven histopathology analysis for COVID-19 diagnosis
    • Bioinformatics for COVID-19 subtype rational drug design
    • ML-based treatment evaluation and outcome prediction
    • AI-based care pathways planning for comorbid patients
    • Deep Learning for COVID-19 treatment, and prognosis
    • Sensor informatics for monitoring COVID-19 infected patients at home or in ICU
    • Informatics-driven rapid testing of the virus in humans
    • In silico modeling of clinical trials in COVID-19 drug and vaccine development
    • Big Data-enabled Citizen-Mediated Public Health Policy making

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    COMET: COvid MEdicaTion study; a retrospective cohort study


    Due to the rapid development from a local epidemic in Wuhan China to the current pandemic, clinical care has clearly received highest priority with limited possibilities to further study pathophysiology. However, this may be key to develop strategies for treatment and prevention of unfavourable clinical course.

    Important aspects such as the role of angiotensin converting enzyme (ACE)2 in facilitating SARS-CoV-2 cell invasion via binding of a viral spike protein to ACE2 have already been established during an earlier SARS-CoV outbreak with a variant showing large similarity with the current virus, therefore now referred to as SARS-CoV-2. 1-3

    Certain drugs have been suggested to influence ACE2 expression, such as ACE inhibitors (inhibiting ACE1), angiotensin II receptor blockers and non-steroid anti-inflammatory drugs. However, this was mainly used on animal studies with conflicting results. Clinical data so far did not show a clear correlation but analyses at a large scale are lacking. The fact that patients with previous cardiovascular disease (CVD, odds ratio (OR) ~25), hypertension and diabetes (both OR ~3) have an increased risk of dying of infection with SARS-CoV-2 (Zhou Lancet 2020) could be explained by the common factor of use of inhibitors of the renin-angiotensin-aldosterone system (RAAS) or more specific of angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs).

    Current study aims to assess the risk of mediations, suchs as ACE inhibitors and ARBs on the severity of COVID-19 disease and mortality due to COVID-19 in a retrospective database.
    Patients will be included from hospitals in Europe. The optimal selection to prevent bias is selection of all patients presenting at the emergency department who turned out to be COVID positive at a certain day or several days if possible. The current aim of the study is to report on medication use and COVID-19 severity within a month.
     
    Any center that is willing to participate is more than welcome!

    Contact: 

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    Got COVID-19 patient data? Want to collaborate? IMI’s EHDEN project can help you standardise it

    EHDEN is offering to harmonise organisations’ clinical data to a standard model, while preserving patients’ privacy. This will make it easier to aggregate and jointly analyse data from different sources, something that is essential if we are to stop the outbreak and save lives.

    The project is offering its expertise to organisations that have amassed data from COVID-19 patients and want help converting it to a standardised data format so that it can be used as part of wider studies on the disease. They have launched a ‘Call for data partners’ through which organisations with clinical data on COVID-19 can apply to benefit from this offer.

    Organisations have until 14 May to apply; a panel of bioinformatics experts will review the applications as they are received.

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    COVID-19 HPC Consortium

    The COVID-19 HPC Consortium encompasses computing capabilities from some of the most powerful and advanced computers in the world. We hope to empower researchers around the world to accelerate understanding of the COVID-19 virus and the development of treatments and vaccines to help address infections. Consortium members manage a range of computing capabilities that span from small clusters to some of the very largest supercomputers in the world.

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