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      Clinical Characteristics and Outcomes for 7,995 Patients with SARS-CoV-2 Infection

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objective:

          Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2.

          Design:

          This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository.

          Setting:

          Yale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas.

          Populations:

          The study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020.

          Main outcome and performance measures:

          Primary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support.

          Results:

          Of the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 1633 (75.8%) had a discharge disposition at the end of the study period. Of these, 192 (11.8%) required invasive mechanical ventilation and 227 (13.5%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 81.9 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14–1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50–2.18) were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups.

          Conclusions:

          This observational study identified, among people testing positive for SARS-CoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines.

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          Most cited references 29

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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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            Comorbidity Measures for Use with Administrative Data

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              Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area

              There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19).
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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                21 July 2020
                Affiliations
                [1 ]Department of Pediatrics, Yale School of Medicine, New Haven, CT;
                [2 ]Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT;
                [3 ]Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT;
                [4 ]Department of Internal Medicine, Yale University School of Medicine, New Haven, CT;
                [5 ]Department of Computer Science and Engineering, Texas A&M University, College Station, TX;
                [6 ]Corporate Pharmacy Services, Yale New Haven Health, New Haven, CT;
                [7 ]Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT;
                [8 ]Interdepartmental Program in Computational Biology and Bioinformatics, Yale University School of Medicine, New Haven, CT;
                [9 ]Yale School of Medicine, New Haven, CT;
                [10 ]Department of Emergency Medicine, Yale School of Medicine, New Haven, CT;
                [11 ]Department of Genetics, Yale University School of Medicine, New Haven, CT;
                [12 ]Medical Scientist Training Program, Yale University School of Medicine, New Haven, CT;
                [13 ]Yale Center for Genome Analysis, Yale University School of Medicine, New Haven, CT;
                [14 ]Department of Neurosurgery, Yale University School of Medicine, New Haven, CT;
                [15 ]Department of Internal Medicine, Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT;
                [16 ]Department of Internal Medicine, Section of Infectious Diseases, Yale School of Medicine, New Haven, CT;
                [17 ]Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT;
                [18 ]Yale New Haven Hospital, New Haven, CT;
                [19 ]Information Technology Services, Yale New Haven Health, New Haven, CT;
                [20 ]Yale Comprehensive Cancer Center, Yale School of Medicine, New Haven, CT
                [21 ]Department of Immunobiology, Yale University School of Medicine, New Haven, CT;
                [22 ]Howard Hughes Medical Institute, Chevy Chase, MD;
                [23 ]Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT;
                [24 ]Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX;
                [25 ]Department of Health Policy and Management, Yale School of Public Health, New Haven, CT
                Author notes
                [$]

                These authors contributed equally to the work.

                [* ]Corresponding Author: 55 Church St, Suite 804, New Haven, CT 06510. wade.schulz@ 123456yale.edu .
                Article
                10.1101/2020.07.19.20157305
                7386526

                It is made available under a CC-BY-NC-ND 4.0 International license.

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