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  1. Article ; Online: Tumor biomarkers predict clinical outcome of COVID-19 patients.

    He, Bangshun / Zhong, Aifang / Wu, Qiuyue / Liu, Xiong / Lin, Jie / Chen, Chao / He, Yiming / Guo, Yanju / Zhang, Man / Zhu, Peiran / Wu, Jian / Wang, Changjun / Wang, Shukui / Xia, Xinyi

    The Journal of infection

    2020  Volume 81, Issue 3, Page(s) 452–482

    MeSH term(s) Betacoronavirus ; Biomarkers, Tumor ; COVID-19 ; China ; Coronavirus ; Coronavirus Infections/epidemiology ; Humans ; Pandemics ; Pneumonia, Viral/epidemiology ; SARS-CoV-2
    Chemical Substances Biomarkers, Tumor
    Keywords covid19
    Language English
    Publishing date 2020-06-11
    Publishing country England
    Document type Letter ; Comment
    ZDB-ID 424417-5
    ISSN 1532-2742 ; 0163-4453
    ISSN (online) 1532-2742
    ISSN 0163-4453
    DOI 10.1016/j.jinf.2020.05.069
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
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  2. Article: Tumor biomarkers predict clinical outcome of COVID-19 patients

    He, Bangshun / Zhong, Aifang / Wu, Qiuyue / Liu, Xiong / Lin, Jie / Chen, Chao / He, Yiming / Guo, Yanju / Zhang, Man / Zhu, Peiran / Wu, Jian / Wang, Changjun / Wang, Shukui / Xia, Xinyi

    J Infect

    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #537529
    Database COVID19

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  3. Article ; Online: Tumor biomarkers predict clinical outcome of COVID-19 patients

    He, Bangshun / Zhong, Aifang / Wu, Qiuyue / Liu, Xiong / Lin, Jie / Chen, Chao / He, Yiming / Guo, Yanju / Zhang, Man / Zhu, Peiran / Wu, Jian / Wang, Changjun / Wang, Shukui / Xia, Xinyi

    Journal of Infection

    2020  Volume 81, Issue 3, Page(s) 452–482

    Keywords Microbiology (medical) ; Infectious Diseases ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 424417-5
    ISSN 1532-2742 ; 0163-4453
    ISSN (online) 1532-2742
    ISSN 0163-4453
    DOI 10.1016/j.jinf.2020.05.069
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1501: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 48: Show issues

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  4. Article ; Online: Preinfection laboratory parameters may predict COVID-19 severity in tumor patients.

    Kiani, Alexander / Roesch, Romina / Wendtner, Clemens M / Kullmann, Frank / Kubin, Thomas / Südhoff, Thomas / Augustin, Marinela / Schaich, Markus / Müller-Naendrup, Clemens / Illerhaus, Gerald / Hartmann, Frank / Hebart, Holger / Seggewiss-Bernhardt, Ruth / Bentz, Martin / Späth-Schwalbe, Ernst / Reimer, Peter / Kaiser, Ulrich / Kapp, Markus / Graeven, Ullrich /
    Chemnitz, Jens-Marcus / Baesecke, Jörg / Lambertz, Helmut / Naumann, Ralph

    Cancer medicine

    2021  Volume 10, Issue 13, Page(s) 4424–4436

    Abstract: ... of individuals with variable risk. Identifying specific risk factors for a severe course of COVID-19 in patients ... the strongest association with COVID-19-related death.: Conclusion: The course of COVID-19 in patients ... at risk for severe COVID-19 at an early stage prior to infection with the virus. German Clinical ...

    Abstract Background: Infection with SARS-CoV-2 leads to COVID-19, the course of which is highly variable and depends on numerous patient-specific risk factors. Patients with tumor diseases are considered to be more susceptible to severe COVID-19; however, they also represent a heterogeneous group of individuals with variable risk. Identifying specific risk factors for a severe course of COVID-19 in patients with cancer is of great importance.
    Methods: Patients diagnosed with solid tumors or hematological malignancies and PCR-confirmed SARS-CoV-2 infection were included into the multicentric ADHOK (Arbeitsgemeinschaft der Hämatologen und Onkologen im Krankenhaus e.V.) coronavirus tumor registry. Detailed information about the patients' cancer disease, treatment, and laboratory parameters prior to infection, was collected retrospectively. The outcome of the SARS-CoV-2 infection was graded according to the WHO.
    Results: A total of 195 patients (68% with solid neoplasms and 32% with hematological malignancies) were included in the registry. Overall, the course of the SARS-CoV-2 infection varied greatly, as 69% of all patients were either asymptomatic or encountered a mild to moderate course, while 23% of the cohort died from COVID-19. In multivariable analysis, preinfection laboratory parameters (determined at least 10 days and a median of 21 days before the first documentation of SARS-CoV-2 infection) significantly correlated with severe course of the disease. Out of these, the absolute neutrophil count prior to infection showed the strongest association with COVID-19-related death.
    Conclusion: The course of COVID-19 in patients with tumor diseases is highly variable. Preinfection laboratory parameters may aid to identify patients at risk for severe COVID-19 at an early stage prior to infection with the virus. German Clinical Trials Register identification: DRKS00023012.
    MeSH term(s) Adolescent ; Adult ; Aged ; Aged, 80 and over ; Biomarkers/blood ; COVID-19/immunology ; COVID-19/mortality ; Child ; Child, Preschool ; Female ; Humans ; Male ; Middle Aged ; Neoplasms/immunology ; Neoplasms/mortality ; Neoplasms/virology ; Neutrophils/metabolism ; Retrospective Studies ; SARS-CoV-2 ; Severity of Illness Index ; Young Adult
    Chemical Substances Biomarkers
    Language English
    Publishing date 2021-06-13
    Publishing country United States
    Document type Journal Article ; Multicenter Study
    ZDB-ID 2659751-2
    ISSN 2045-7634 ; 2045-7634
    ISSN (online) 2045-7634
    ISSN 2045-7634
    DOI 10.1002/cam4.4023
    Database MEDical Literature Analysis and Retrieval System OnLINE

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