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Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study

Authors: Cummings, Matthew J et al.;

Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study

Abstract

Background: Nearly 30,000 patients with coronavirus disease-2019 (COVID-19) have been hospitalized in New York City as of April 14th, 2020. Data on the epidemiology, clinical course, and outcomes of critically ill patients with COVID-19 in this setting are needed. Methods: We prospectively collected clinical, biomarker, and treatment data on critically ill adults with laboratory-confirmed-COVID-19 admitted to two hospitals in northern Manhattan between March 2nd and April 1st, 2020. The primary outcome was in-hospital mortality. Secondary outcomes included frequency and duration of invasive mechanical ventilation, frequency of vasopressor use and renal-replacement-therapy, and time to clinical deterioration following hospital admission. The relationship between clinical risk factors, biomarkers, and in-hospital mortality was modeled using Cox-proportional-hazards regression. Each patient had at least 14 days of observation. Results: Of 1,150 adults hospitalized with COVID-19 during the study period, 257 (22%) were critically ill. The median age was 62 years (interquartile range [IQR] 51-72); 170 (66%) were male. Two-hundred twelve (82%) had at least one chronic illness, the most common of which were hypertension (63%; 162/257) and diabetes mellitus (36%; 92/257). One-hundred-thirty-eight patients (54%) were obese, and 13 (5%) were healthcare workers. As of April 14th, 2020, in-hospital mortality was 33% (86/257); 47% (122/257) of patients remained hospitalized. Two-hundred-one (79%) patients received invasive mechanical ventilation (median 13 days [IQR 9-17]), and 54% (138/257) and 29% (75/257) required vasopressors and renal-replacement-therapy, respectively. The median time to clinical deterioration following hospital admission was 3 days (IQR 1-6). Older age, hypertension, chronic lung disease, and higher concentrations of interleukin-6 and d-dimer at admission were independently associated with in-hospital mortality. Conclusions: Critical illness among patients hospitalized with COVID-19 in New York City is common and associated with a high frequency of invasive mechanical ventilation, extra-pulmonary organ dysfunction, and substantial in-hospital mortality.

Keywords

Coronavirus, Cohort Studies, Environmental Biomarkers, COVID-19, Mortality, Respiration, Artificial, United States

29 references, page 1 of 3

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2 New York State Department of Health. COVID-19 tracker. https://covid19tracker.health.ny.gov/views/NYS-COVID19-Tracker/ NYSDOHCOVID-19Tracker-Map?%3Aembed=yes&%3Atoolbar=no &%3Atabs=n (accessed April 28, 2020).

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4 Guan WJ, Ni ZY, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020; 382: 1708-20.

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6 Yang X, Yu Y, Xu J, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a singlecentered, retrospective, observational study. Lancet Respir Med 2020; published online Feb 24. https://dx.doi.org/10.1016/ S2213-2600(20)30079-5.

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8 Grasselli G, Zangrillo A, Zanella A, et al. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy. JAMA 2020; published online April 6. DOI:10.1001/jama.2020.5394.

9 Arentz M, Yim E, Klaf L, et al. Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington State. JAMA 2020; published online March 19. DOI:10.1001/jama.2020.4326.

10 Bhatraju PK, Ghassemieh BJ, Nichols M, et al. COVID-19 in critically ill patients in the Seattle region-case series. N Engl J Med 2020; published online March 30. DOI:10.1056/ NEJMoa2004500. [OpenAIRE]

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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NIH| Phenotyping sepsis in Uganda using molecular pathogen diagnostics and latent class modeling
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5F32AI147528-02
  • Funding stream: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
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NIH| Clinical and Translational Science Award
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 2UL1TR001873-06
  • Funding stream: NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES
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