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      Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study

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          To describe outcomes of people admitted to hospital with coronavirus disease 2019 (covid-19) in the United States, and the clinical and laboratory characteristics associated with severity of illness.


          Prospective cohort study.


          Single academic medical center in New York City and Long Island.


          5279 patients with laboratory confirmed severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection between 1 March 2020 and 8 April 2020. The final date of follow up was 5 May 2020.

          Main outcome measures

          Outcomes were admission to hospital, critical illness (intensive care, mechanical ventilation, discharge to hospice care, or death), and discharge to hospice care or death. Predictors included patient characteristics, medical history, vital signs, and laboratory results. Multivariable logistic regression was conducted to identify risk factors for adverse outcomes, and competing risk survival analysis for mortality.


          Of 11 544 people tested for SARS-Cov-2, 5566 (48.2%) were positive. After exclusions, 5279 were included. 2741 of these 5279 (51.9%) were admitted to hospital, of whom 1904 (69.5%) were discharged alive without hospice care and 665 (24.3%) were discharged to hospice care or died. Of 647 (23.6%) patients requiring mechanical ventilation, 391 (60.4%) died and 170 (26.2%) were extubated or discharged. The strongest risk for hospital admission was associated with age, with an odds ratio of >2 for all age groups older than 44 years and 37.9 (95% confidence interval 26.1 to 56.0) for ages 75 years and older. Other risks were heart failure (4.4, 2.6 to 8.0), male sex (2.8, 2.4 to 3.2), chronic kidney disease (2.6, 1.9 to 3.6), and any increase in body mass index (BMI) (eg, for BMI >40: 2.5, 1.8 to 3.4). The strongest risks for critical illness besides age were associated with heart failure (1.9, 1.4 to 2.5), BMI >40 (1.5, 1.0 to 2.2), and male sex (1.5, 1.3 to 1.8). Admission oxygen saturation of <88% (3.7, 2.8 to 4.8), troponin level >1 (4.8, 2.1 to 10.9), C reactive protein level >200 (5.1, 2.8 to 9.2), and D-dimer level >2500 (3.9, 2.6 to 6.0) were, however, more strongly associated with critical illness than age or comorbidities. Risk of critical illness decreased significantly over the study period. Similar associations were found for mortality alone.


          Age and comorbidities were found to be strong predictors of hospital admission and to a lesser extent of critical illness and mortality in people with covid-19; however, impairment of oxygen on admission and markers of inflammation were most strongly associated with critical illness and mortality. Outcomes seem to be improving over time, potentially suggesting improvements in care.

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          Most cited references 8

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          Hospitalizations associated with influenza and respiratory syncytial virus in the United States, 1993-2008.

          Age-specific comparisons of influenza and respiratory syncytial virus (RSV) hospitalization rates can inform prevention efforts, including vaccine development plans. Previous US studies have not estimated jointly the burden of these viruses using similar data sources and over many seasons. We estimated influenza and RSV hospitalizations in 5 age categories (<1, 1-4, 5-49, 50-64, and ≥65 years) with data for 13 states from 1993-1994 through 2007-2008. For each state and age group, we estimated the contribution of influenza and RSV to hospitalizations for respiratory and circulatory disease by using negative binomial regression models that incorporated weekly influenza and RSV surveillance data as covariates. Mean rates of influenza and RSV hospitalizations were 63.5 (95% confidence interval [CI], 37.5-237) and 55.3 (95% CI, 44.4-107) per 100000 person-years, respectively. The highest hospitalization rates for influenza were among persons aged ≥65 years (309/100000; 95% CI, 186-1100) and those aged <1 year (151/100000; 95% CI, 151-660). For RSV, children aged <1 year had the highest hospitalization rate (2350/100000; 95% CI, 2220-2520) followed by those aged 1-4 years (178/100000; 95% CI, 155-230). Age-standardized annual rates per 100000 person-years varied substantially for influenza (33-100) but less for RSV (42-77). Overall US hospitalization rates for influenza and RSV are similar; however, their age-specific burdens differ dramatically. Our estimates are consistent with those from previous studies focusing either on influenza or RSV. Our approach provides robust national comparisons of hospitalizations associated with these 2 viral respiratory pathogens by age group and over time.
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            Generalized Collinearity Diagnostics

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              Do chronic respiratory diseases or their treatment affect the risk of SARS-CoV-2 infection?

              Coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an acute respiratory disease that can lead to respiratory failure and death. 1 Previous epidemics of novel coronavirus diseases, such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), were associated with similar clinical features and outcomes. 2 One might anticipate that patients with chronic respiratory diseases, particularly chronic obstructive pulmonary disease (COPD) and asthma, would be at increased risk of SARS-CoV-2 infection and more severe presentations of COVID-19. However, it is striking that both diseases appear to be under-represented in the comorbidities reported for patients with COVID-19, compared with the global burden of disease estimates of the prevalence of these conditions in the general population (table ); a similar pattern was seen with SARS. By contrast, the prevalence of diabetes in patients with COVID-19 or SARS is as high as or higher than the estimated national prevalence, as might be expected. Table Prevalence of chronic respiratory diseases and diabetes in patients with COVID-19 and SARS Number of patients Health-care workers (%) Mean or median age (years) Prevalence (%) Chronic respiratory disease COPD Asthma Diabetes Patients with COVID-19 China12 44 672 3·8% ∼51 2·4% .. .. 5·3% Wuhan, China13 140 .. 57* .. 1·4% .. 12·1% Patients with SARS Toronto, Canada14 147 51% 45* .. 1·0% .. 11·0% Taipei, Taiwan15 67 37% 51·0 6·0% .. .. 23·9% Kaohsiung, Taiwan16 52 31% 48·1 .. 10·0% .. .. Hong Kong17 88 19% 42·1 .. 0 1·0% 10·0% Hong Kong18 112 61% 39·3 .. 2·6% .. 4·5% General population † China19 .. .. .. 6·9% 4·9% 2·3% 6·6% Canada19 .. .. .. 10·4% 5·4% 5·4% 8·2% Taiwan19 .. .. .. 13·1% 10·4% 3·9% 10·6% Hong Kong20 .. .. .. .. 1·4% 1·9% 3·8% Table references are listed in the appendix. COPD=chronic obstructive pulmonary disease. COVID-19=coronavirus disease 2019. SARS=severe acute respiratory syndrome. * Median age. † Estimates for China, Canada, and Taiwan from the Global Burden of Disease Study; Hong Kong estimates from the Department of Health, Hong Kong Special Administrative Region Government. The lower reported prevalence of asthma and COPD in patients diagnosed with COVID-19 might be due to one or a number of factors. First, it is possible that, in contrast to the diagnosis of diabetes, there was substantial underdiagnosis or poor recognition of chronic respiratory disease in patients with COVID-19, particularly in China. However, this seems unlikely, as in very recent data (March 23, 2020) from Italy, among 355 patients dying with COVID-19 (mean age 79·5 years), diabetes was reported in 20·3% of patients but COPD was not listed as a comorbidity for any patient. 3 Similarly, provisional data from the USA (March 31, 2020) show that chronic respiratory diseases and diabetes were comorbidities in 8·5% and 10·2% of patients with COVID-19, respectively, compared with Global Burden of Disease figures for the population as a whole of 11·3% for chronic respiratory diseases and 10·2% for diabetes; however these data are based on only 7162 of the 74 439 patients reported. 4 A second possibility is that having a chronic respiratory disease protects against COVID-19, perhaps through a different immune response elicited by the chronic disease itself. However, this theory is not supported by the finding that among those with COVID-19 who have COPD as a comorbidity, mortality is increased, as would otherwise be expected. 5 A third possibility is that therapies used by patients with chronic respiratory diseases can reduce the risk of infection or of developing symptoms leading to diagnosis. It is important to note that, at most, only around half of patients with COPD in China take treatments that are standard in Europe and North America, 6 but up to 75% of people in China with asthma use inhaled corticosteroids. 7 Furthermore, in in-vitro models, inhaled corticosteroids alone or in combination with bronchodilators have been shown to suppress coronavirus replication and cytokine production.8, 9 Low-quality evidence also exists from a case series in Japan, in which improvement was seen in three patients with COVID-19 requiring oxygen, but not ventilatory support, after being given inhaled ciclesonide; 10 however, no control group was used and it is not known whether these patients would have improved spontaneously. Yet, the possibility that inhaled corticosteroids might prevent (at least partly) the development of symptomatic infection or severe presentations of COVID-19 cannot be ignored. By contrast, a systematic review on the use of systemic corticosteroids to treat SARS, once established, showed no benefit but possible harm. 11 The potential benefits or harms of inhaled corticosteroids and other treatments for people at risk of SARS-CoV-2 infection or patients with COVID-19 are unclear at present, and no changes to the treatment or management of chronic respiratory conditions, including COPD and asthma, should be considered at this stage. However, collecting accurate data for the comorbidities and previous therapy of patients with COVID-19 will be essential to understanding risk factors for becoming infected, developing symptoms, and being diagnosed, as well as enabling answers to questions about possible benefits or harms of therapy for asthma and COPD during the COVID-19 pandemic. This could be achieved using a standard dataset as advocated by WHO, including information about the presence and severity of comorbidities and all medication that was being taken at the time of infection.

                Author and article information

                Role: assistant professor of medicine
                Role: professor of population health and statistician
                Role: data scientist
                Role: assistant manager
                Role: hospitalist physician
                Role: medical center information technology lead developer
                Role: assistant director
                Role: professor of cardiothoracic surgery and chief of hospital operations
                Role: professor of medicine and chief medical officer
                Role: associate professor of population health and medicine
                The BMJ
                BMJ Publishing Group Ltd.
                22 May 2020
                : 369
                [1 ]Division of General Internal Medicine and Clinical Innovation, Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA
                [2 ]NYU Langone Health, New York, NY, USA
                [3 ]Division of Healthcare Delivery Science, Department of Population Health, NYU Grossman School of Medicine, 227 East 30th Street #633, New York, NY 10016, USA
                [4 ]Center for Healthcare Innovation and Delivery Science, NYU Langone Health, New York, NY, USA
                [5 ]Department of Cardiothoracic Surgery, NYU Grossman School of Medicine, New York, NY, USA
                [6 ]Division of Gastroenterology, Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA
                Author notes
                Correspondence to: L Horwitz leora.horwitz@ 123456nyulangone.org (or @leorahorwitzmd on Twitter)
                © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.




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