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      Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

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          Abstract

          The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

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          Most cited references52

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          Genomewide Association Study of Severe Covid-19 with Respiratory Failure

          Abstract Background There is considerable variation in disease behavior among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19). Genomewide association analysis may allow for the identification of potential genetic factors involved in the development of Covid-19. Methods We conducted a genomewide association study involving 1980 patients with Covid-19 and severe disease (defined as respiratory failure) at seven hospitals in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe. After quality control and the exclusion of population outliers, 835 patients and 1255 control participants from Italy and 775 patients and 950 control participants from Spain were included in the final analysis. In total, we analyzed 8,582,968 single-nucleotide polymorphisms and conducted a meta-analysis of the two case–control panels. Results We detected cross-replicating associations with rs11385942 at locus 3p21.31 and with rs657152 at locus 9q34.2, which were significant at the genomewide level (P<5×10−8) in the meta-analysis of the two case–control panels (odds ratio, 1.77; 95% confidence interval [CI], 1.48 to 2.11; P=1.15×10−10; and odds ratio, 1.32; 95% CI, 1.20 to 1.47; P=4.95×10−8, respectively). At locus 3p21.31, the association signal spanned the genes SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6 and XCR1. The association signal at locus 9q34.2 coincided with the ABO blood group locus; in this cohort, a blood-group–specific analysis showed a higher risk in blood group A than in other blood groups (odds ratio, 1.45; 95% CI, 1.20 to 1.75; P=1.48×10−4) and a protective effect in blood group O as compared with other blood groups (odds ratio, 0.65; 95% CI, 0.53 to 0.79; P=1.06×10−5). Conclusions We identified a 3p21.31 gene cluster as a genetic susceptibility locus in patients with Covid-19 with respiratory failure and confirmed a potential involvement of the ABO blood-group system. (Funded by Stein Erik Hagen and others.)
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            Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

            Summary Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using ten independent patients, seven of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 new COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.
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              Digital technology and COVID-19

              The past decade has allowed the development of a multitude of digital tools. Now they can be used to remediate the COVID-19 outbreak.
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                Author and article information

                Contributors
                Journal
                Brief Bioinform
                Brief Bioinform
                bib
                Briefings in Bioinformatics
                Oxford University Press
                1467-5463
                1477-4054
                18 January 2021
                : bbaa418
                Affiliations
                Department of Electrical, Computer and Biomedical Engineering, University of Pavia , Italy
                IRCCS Istituti Clinici Scientifici Maugeri , Pavia, Italy
                IRCCS Istituti Clinici Scientifici Maugeri , Pavia, Italy
                Department of Electrical, Computer and Biomedical Engineering, University of Pavia , Italy
                IRCCS Istituti Clinici Scientifici Maugeri , Pavia, Italy
                Author notes
                Corresponding author: Riccardo Bellazzi, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, Pavia 27100, Italy. Tel.: +39 0382 985720; Fax: +39 0382 985373; E-mail: riccardo.bellazzi@ 123456unipv.it
                Author information
                http://orcid.org/0000-0002-5041-0409
                http://orcid.org/0000-0002-6974-9808
                Article
                bbaa418
                10.1093/bib/bbaa418
                7929411
                33454728
                ac2597d2-15ff-4e8b-87ef-c055bb877fe1
                © The Author(s) 2021. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 11 August 2020
                : 29 October 2020
                : 19 December 2020
                Page count
                Pages: 11
                Categories
                AcademicSubjects/SCI01060
                Method Review
                Custom metadata
                PAP

                Bioinformatics & Computational biology
                covid-19 pandemic,electronic health record,clinical research,international initiatives,data sharing

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