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Tytuł pozycji:

Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system.

Tytuł:
Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system.
Autorzy:
Rechtman E; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1057, New York, NY, 10029, USA.
Curtin P; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1057, New York, NY, 10029, USA.
Navarro E; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1057, New York, NY, 10029, USA.
Nirenberg S; Scientific Computing, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Horton MK; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1057, New York, NY, 10029, USA. .
Źródło:
Scientific reports [Sci Rep] 2020 Dec 09; Vol. 10 (1), pp. 21545. Date of Electronic Publication: 2020 Dec 09.
Typ publikacji:
Journal Article; Observational Study; Research Support, N.I.H., Extramural
Język:
English
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:
COVID-19*/mortality
COVID-19*/physiopathology
COVID-19*/therapy
Hospitalization*
Machine Learning*
Models, Biological*
Pandemics*
SARS-CoV-2*
Vital Signs*
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; New York City ; Predictive Value of Tests ; Retrospective Studies ; Risk Factors
References:
JAMA. 2020 Mar 17;323(11):1061-1069. (PMID: 32031570)
N Engl J Med. 2020 Apr 30;382(18):1708-1720. (PMID: 32109013)
JAMA. 2020 Apr 28;323(16):1612-1614. (PMID: 32191259)
BMJ. 2020 Feb 19;368:m606. (PMID: 32075786)
BMJ. 2020 Apr 7;369:m1328. (PMID: 32265220)
Ann Transl Med. 2018 Feb;6(3):42. (PMID: 29610734)
J Infect. 2020 Jun;80(6):639-645. (PMID: 32240670)
BMJ. 2020 Sep 9;370:m3339. (PMID: 32907855)
JAMA. 2020 Apr 28;323(16):1574-1581. (PMID: 32250385)
Nat Rev Endocrinol. 2020 Jul;16(7):341-342. (PMID: 32327737)
JAMA. 2020 May 26;323(20):2052-2059. (PMID: 32320003)
Lancet. 2020 Mar 28;395(10229):1054-1062. (PMID: 32171076)
Allergy. 2020 Jul;75(7):1730-1741. (PMID: 32077115)
Lancet Respir Med. 2020 Apr;8(4):e21. (PMID: 32171062)
Grant Information:
P30 ES023515 United States ES NIEHS NIH HHS; UL1TR001433 United States TR NCATS NIH HHS
Entry Date(s):
Date Created: 20201210 Date Completed: 20201221 Latest Revision: 20210127
Update Code:
20240104
PubMed Central ID:
PMC7726000
DOI:
10.1038/s41598-020-78392-1
PMID:
33298991
Czasopismo naukowe
Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66-1.92]), male sex (OR, 1.57 [95% CI 1.30-1.90]), higher BMI (OR, 1.03 [95% CI 1.102-1.05]), higher heart rate (OR, 1.01 [95% CI 1.00-1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03-1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93-0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20-1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.

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