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      Associations between blood type and COVID-19 infection, intubation, and death

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          Abstract

          The rapid global spread of the novel coronavirus SARS-CoV-2 has strained healthcare and testing resources, making the identification and prioritization of individuals most at-risk a critical challenge. Recent evidence suggests blood type may affect risk of severe COVID-19. Here, we use observational healthcare data on 14,112 individuals tested for SARS-CoV-2 with known blood type in the New York Presbyterian (NYP) hospital system to assess the association between ABO and Rh blood types and infection, intubation, and death. We find slightly increased infection prevalence among non-O types. Risk of intubation was decreased among A and increased among AB and B types, compared with type O, while risk of death was increased for type AB and decreased for types A and B. We estimate Rh-negative blood type to have a protective effect for all three outcomes. Our results add to the growing body of evidence suggesting blood type may play a role in COVID-19.

          Abstract

          Recent evidence has suggested that blood type may be associated with severe COVID-19. Here, the authors use data from ~14,000 individuals tested for SARS-CoV-2 at a New York City hospital, and find that certain ABO and Rh blood types are associated with infection, intubation, and death.

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          Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

          Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
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            Temporal dynamics in viral shedding and transmissibility of COVID-19

            We report temporal patterns of viral shedding in 94 patients with laboratory-confirmed COVID-19 and modeled COVID-19 infectiousness profiles from a separate sample of 77 infector-infectee transmission pairs. We observed the highest viral load in throat swabs at the time of symptom onset, and inferred that infectiousness peaked on or before symptom onset. We estimated that 44% (95% confidence interval, 25-69%) of secondary cases were infected during the index cases' presymptomatic stage, in settings with substantial household clustering, active case finding and quarantine outside the home. Disease control measures should be adjusted to account for probable substantial presymptomatic transmission.
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              A Proportional Hazards Model for the Subdistribution of a Competing Risk

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                Author and article information

                Contributors
                nick.tatonetti@columbia.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 November 2020
                13 November 2020
                2020
                : 11
                : 5761
                Affiliations
                [1 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Biomedical Informatics, , Columbia University Irving Medical Center, ; New York, NY USA
                [2 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Medicine, , Columbia University Irving Medical Center, ; New York, NY USA
                [3 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Systems Biology, , Columbia University Irving Medical Center, ; New York, NY USA
                [4 ]GRID grid.21729.3f, ISNI 0000000419368729, Institute for Genomic Medicine, , Columbia University Irving Medical Center, ; New York, NY USA
                Author information
                http://orcid.org/0000-0003-0539-630X
                http://orcid.org/0000-0002-2700-2597
                Article
                19623
                10.1038/s41467-020-19623-x
                7666188
                33188185
                12a0db92-070a-4c1d-bacd-0c02fa0a43d1
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 April 2020
                : 16 October 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000092, U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM);
                Award ID: 5 T15 LM007079-27
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: 1R35GM131905-01
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2020

                Uncategorized
                sars-cov-2,predictive markers,epidemiology,risk factors
                Uncategorized
                sars-cov-2, predictive markers, epidemiology, risk factors

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