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Cited by 819

The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.

Predictors of COVID‐19 severity: A literature review

Open access
Gallo Marin, Benjamin, Aghagoli, Ghazal, Lavine, Katya, Yang, Lanbo, Siff, Emily J., Chiang, Silvia S., Salazar‐Mather, Thais P., Dumenco, Luba, Savaria, Michael C, Aung, Su N., Flanigan, Timothy, Michelow, Ian C.·2021·Reviews in Medical Virology

Abstract

Summary: The coronavirus disease 2019 (COVID‐19) pandemic is a rapidly evolving global emergency that continues to strain healthcare systems. Emerging research describes a plethora of patient factors—including demographic, clinical, immunologic, hematological, biochemical, and radiographic findings—that may be of utility to clinicians to predict COVID‐19 severity and mortality. We present a synthesis of the current literature pertaining to factors predictive of COVID‐19 clinical course and outcomes. Findings associated with increased disease severity and/or mortality include age > 55 years, multiple pre‐existing comorbidities, hypoxia, specific computed tomography findings indicative of extensive lung involvement, diverse laboratory test abnormalities, and biomarkers of end‐organ dysfunction. Hypothesis‐driven research is critical to identify the key evidence‐based prognostic factors that will inform the design of intervention studies to improve the outcomes of patients with COVID‐19 and to appropriately allocate scarce resources.

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Registry

Crossref · 10.1002/rmv.2146

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Metadata

Authors
Gallo Marin, B., Aghagoli, G., Lavine, K., Yang, L., Siff, E. J., Chiang, S. S., Salazar‐Mather, T. P., Dumenco, L., Savaria, M. C., Aung, S. N., Flanigan, T., & Michelow, I. C.
Venue
Reviews in Medical Virology
Publisher
Wiley
Published
2020-07-30
Volume
31
Issue
1
Pages
1-10
ISSN
  • 1052-9276 (ISSN-L)
  • 1099-1654
OpenAlex
W3046496943
Cited by
819
References
122
FWCI
19.10
Type
journal article
Language
en
Open access
green

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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