Return Hospital Admissions Among 1419 COVID‐19 Patients Discharged from Five U.S. Emergency Departments
Open accessAbstract
A lthough many emergency department (ED) patients with known or suspected COVID-19 require hospital admission, the majority are discharged home. However, some patients with COVID-19 experience delayed decompensation. Patients may develop serious illness several days after initial symptoms and require respiratory support. Additional complications, including venous thromboembolism, myocarditis, and acute kidney injury, may also require advanced therapies. It is not known how often and which patients with COVID-19 return to the hospital following initial evaluation in the ED. To date, prediction models have focused on the risk of critical illness among hospitalized patients. In this study, we describe the incidence of return hospital admission within 72 hours for patients with COVID-19 who were discharged from the ED upon initial presentation. We also evaluate patient characteristics associated with return hospital admission.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A patient at lower risk of needing invasive oxygen therapy may be placed in a less intensive care setting or discharged for self-monitoring.
“In contrast, a patient who is at a lower risk of requiring invasive oxygen therapy may be placed in a less intensive care setting such as a regular ward or even released from the ED for continued self-monitoring at home65.”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [65]
Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884 · see citing works
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148 · see citing works
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463 · see citing works
Petrilli, C. M., Jones, S. A., Yang, J., Rajagopalan, H., O’Donnell, L., Chernyak, Y., Tobin, K. A., Cerfolio, R. J., François, F., & Horwitz, L. I. (2020) · BMJ · doi:10.1136/bmj.m1966 · Cited by 3,045 · see citing works
Garg, S., Kim, L., Whitaker, M., O’Halloran, A., Cummings, C., Holstein, R., Prill, M., Chai, S. J., Kirley, P. D., Alden, N. B., et al. (2020) · MMWR. Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6915e3 · Cited by 2,335 · see citing works
Statistical Analysis of Correlated Data Using Generalized Estimating Equations: An Orientation
Hanley, J. A. (2003) · American Journal of Epidemiology · doi:10.1093/aje/kwf215 · Cited by 2,185 · see citing works
Liang, W., Liang, H., Ou, L., Chen, B., Chen, A., Li, C., Li, Y., Guan, W., Sang, L., Lu, J., et al. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2020.2033 · Cited by 1,387 · see citing works
Gandhi, R. T., Lynch, J. B., & del Rı́o, C. (2020) · New England Journal of Medicine · doi:10.1056/nejmcp2009249 · Cited by 1,312 · see citing works
Trends in Mortality for Medicare Beneficiaries Treated in the Emergency Department From 2009 to 2016
Burke, L. G., Epstein, S. K., Burke, R. C., Orav, E. J., & Jha, A. K. (2019) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2019.4866 · Cited by 382 · see citing works
Revisit Rates and Associated Costs After an Emergency Department Encounter
Duseja, R., Bardach, N. S., Lin, G. A., et al. (2015) · Annals of Internal Medicine · doi:10.7326/m14-1616 · Cited by 139 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
“In contrast, a patient who is at lower risk of requiring invasive oxygen therapy may be placed in a less intensive care setting such as a regular ward, or even released from the ED for continuing self-monitoring at home 65 .”
Background
- Authors
- Kilaru, A. S., Lee, K., Snider, C. K., Meisel, Z. F., Asch, D. A., Mitra, N., & Delgado, M. K.
- Affiliations
- Philadelphia VA Medical Center (US)
- University of Pennsylvania Health System (US)
- University of Pennsylvania (US)
- Corresponding author
- Kilaru, Austin S.
- ORCID
- Venue
- Academic Emergency Medicine
- Publisher
- Wiley
- Published
- 2020-09-27
- Volume
- 27
- Issue
- 10
- Pages
- 1039-1042
- ISSN
- 1069-6563 (ISSN-L)
- 1553-2712
- PMC
- PMC7461233
- OpenAlex
- W3080435357
- Cited by
- 34
- References
- 10
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 3 · 2022: 13 · 2021: 14 · 2020: 1
- FWCI
- 1.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Academic Emergency Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Pennsylvania
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
- Keywords
- COVID-19 pandemic, emergency department discharge, post-discharge complications, venous thromboembolism, myocarditis, acute kidney injury, respiratory support, risk factors for readmission
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Emergency Service, Hospital, Humans, Patient Discharge, Patient Readmission, Pneumonia, Viral, United States, Coronavirus Infections, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
