Patient characteristics and admitting vital signs associated with coronavirus disease 2019 (COVID-19)–related mortality among patients admitted with noncritical illness
Open accessAbstract
Objective: To determine risk factors for mortality among COVID-19 patients admitted to a system of community hospitals in the United States. Design: Retrospective analysis of patient data collected from the routine care of COVID-19 patients. Setting: System of >180 acute-care facilities in the United States. Participants: All admitted patients with positive identification of COVID-19 and a documented discharge as of May 12, 2020. Methods: Determination of demographic characteristics, vital signs at admission, patient comorbidities and recorded discharge disposition in this population to construct a logistic regression estimating the odds of mortality, particular for those patients characterized as not being critically ill at admission. Results: In total, 6,180 COVID-19+ patients were identified as of May 12, 2020. Most COVID-19+ patients (4,808, 77.8%) were admitted directly to a medical-surgical unit with no documented critical care or mechanical ventilation within 8 hours of admission. After adjusting for demographic characteristics, comorbidities, and vital signs at admission in this subgroup, the largest driver of the odds of mortality was patient age (OR, 1.07; 95% CI, 1.06–1.08; P < .001). Decreased oxygen saturation at admission was associated with increased odds of mortality (OR, 1.09; 95% CI, 1.06–1.12; P < .001) as was diabetes (OR, 1.57; 95% CI, 1.21–2.03; P < .001). Conclusions: The identification of factors observable at admission that are associated with mortality in COVID-19 patients who are initially admitted to non-critical care units may help care providers, hospital epidemiologists, and hospital safety experts better plan for the care of these patients.
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The CDS model used chest X-ray images, vital signs, demographic data, and laboratory values that prior publications showed to be predictive of COVID-19 outcomes.
“The inputs to the CDS model were Chest X-Ray (CXR) images, vital signs, demographic data, and lab values that were shown in previous publications to be predictive of COVID-19 patient outcomes18–21.”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [21]
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Federated learning for predicting clinical outcomes in patients with COVID-19
Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842 · see citing works
- Authors
- Sands, K. E., Wenzel, R. P., McLean, L. E., Korwek, K. M., Roach, J. D., Miller, K. M., Poland, R. E., Burgess, L. H., Jackson, E. S., & Perlin, J. B.
- Affiliations
- HCA Healthcare (US)
- Virginia Commonwealth University Medical Center (US)
- Corresponding author
- Sands, Kenneth E.
- Venue
- Infection Control & Hospital Epidemiology
- Publisher
- Cambridge University Press (CUP)
- Published
- 2020-09-15
- Volume
- 42
- Issue
- 4
- Pages
- 399-405
- ISSN
- 0899-823X (ISSN-L)
- 1559-6834
- PMC
- PMC7520636
- OpenAlex
- W3088181458
- Cited by
- 29
- References
- 22
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 3 · 2022: 10 · 2021: 13
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Infection Control and Hospital Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- HCA Healthcare
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Keywords
- COVID-19 pandemic, mortality risk factors, community hospitals, oxygen saturation, diabetes mellitus, patient age, comorbidities, discharge disposition
- MeSH terms
- COVID-19, Age Factors, Aged, Aged, 80 and over, Female, Humans, Male, Middle Aged, Oxygen, Patient Admission, Retrospective Studies, Risk Factors, United States, Logistic Models, Vital Signs
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