Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study.
Adult
Age Factors
Aged
Aged, 80 and over
COVID-19
Cohort Studies
Comorbidity
Coronavirus Infections
/ drug therapy
Female
Hospitalization
Humans
Influenza, Human
/ drug therapy
Male
Middle Aged
Pandemics
Pneumonia, Viral
/ drug therapy
Prevalence
Republic of Korea
/ epidemiology
Sex Factors
Spain
/ epidemiology
United States
/ epidemiology
Young Adult
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
06 10 2020
06 10 2020
Historique:
received:
01
07
2020
accepted:
10
09
2020
entrez:
7
10
2020
pubmed:
8
10
2020
medline:
21
10
2020
Statut:
epublish
Résumé
Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.
Identifiants
pubmed: 33024121
doi: 10.1038/s41467-020-18849-z
pii: 10.1038/s41467-020-18849-z
pmc: PMC7538555
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
5009Subventions
Organisme : NIA NIH HHS
ID : P30 AG059307
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM006910
Pays : United States
Organisme : Medical Research Council
ID : MR/K501256/1
Pays : United Kingdom
Organisme : Department of Health
ID : SRF-2018-11-ST2-004
Pays : United Kingdom
Organisme : Versus Arthritis
ID : 21605
Pays : United Kingdom
Organisme : NCATS NIH HHS
ID : UL1 TR002544
Pays : United States
Commentaires et corrections
Type : UpdateOf
Références
Chen, N. et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395, 507–513 (2020).
doi: 10.1016/S0140-6736(20)30211-7
Wang, D. et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA 323, 1061–1069 (2020).
doi: 10.1001/jama.2020.1585
Bhatraju, P. K. et al. Covid-19 in critically ill patients in the seattle region—case series. N. Engl. J. Med. https://doi.org/10.1056/NEJMoa2004500 (2020).
Verity, R. et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet 3099, 1–9 (2020).
Mertz, D. et al. Populations at risk for severe or complicated influenza illness: systematic review and meta-analysis. BMJ Br. Med. J. 347, f5061 (2013).
doi: 10.1136/bmj.f5061
Reed, C. et al. Complications among adults hospitalized with influenza: a comparison of seasonal influenza and the 2009 H1N1 pandemic. Clin. Infect. Dis. 59, 166–174 (2014).
doi: 10.1093/cid/ciu285
Guan, W. et al. Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. https://doi.org/10.1056/NEJMoa2002032 (2020).
Zhou, F. et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395, 1054–1062 (2020).
doi: 10.1016/S0140-6736(20)30566-3
Petrilli, C. M. et al. Factors associated with hospitalization and critical illness among 4103 patients with COVID-19 disease in New York City. medRxiv https://doi.org/10.1101/2020.04.08.20057794 (2020).
Garg, S., Kim, L., Whitaker, M., O’Halloran, A. & Cummings, C. Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019—COVID-NET, 14 States, March 1–30, 2020. MMWR Morb. Mortal Wkly Rep. https://doi.org/10.15585/mmwr.mm6915e3 (2020).
Whiting, P. et al. What are the risks and benefits of temporarily discontinuing medications to prevent acute kidney injury? A systematic review and meta-analysis. BMJ Open 7, e012674 (2017).
doi: 10.1136/bmjopen-2016-012674
Horby, P. et al. Effect of dexamethasone in hospitalized patients with COVID-19: preliminary report. medRxiv https://doi.org/10.1101/2020.06.22.20137273 (2020).
Vogelstein, J. T. et al. Alpha-1 adrenergic receptor antagonists for preventing acute respiratory distress syndrome and death from cytokine storm syndrome. Preprint at https://arxiv.org/abs/2004.10117 (2020).
CDC COVID-19 Response Team. Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019—United States, February 12–March 28, 2020. MMWR Morb. Mortal Wkly Rep. 69, 382 (2020).
Grasselli, G. et al. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy. JAMA https://doi.org/10.1001/jama.2020.5394 (2020).
Voss, E. A. et al. Feasibility and utility of applications of the common data model to multiple, disparate observational health databases. J. Am. Med. Inf. Assoc. 22, 553–564 (2015).
doi: 10.1093/jamia/ocu023
Hripcsak, G. et al. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud. Health Technol. Inform. 216, 574–578 (2015).
pubmed: 26262116
pmcid: 4815923
García-Gil, M. D. M. et al. Construction and validation of a scoring system for the selection of high-quality data in a Spanish population primary care database (SIDIAP). Inform. Prim. Care 19, 135–145 (2011).
Kim, J.-A., Yoon, S., Kim, L.-Y. & Kim, D.-S. Towards actualizing the value potential of Korea Health Insurance Review and Assessment (HIRA) data as a resource for health research: strengths, limitations, applications, and strategies for optimal use of HIRA data. J. Korean Med. Sci. 32, 718–728 (2017).
doi: 10.3346/jkms.2017.32.5.718
Datta, S. et al. A new paradigm for accelerating clinical data science at Stanford Medicine. Preprint at https://arxiv.org/abs/2003.10534 (2020).
Overhage, J. M., Ryan, P. B., Reich, C. G., Hartzema, A. G. & Stang, P. E. Validation of a common data model for active safety surveillance research. J. Am. Med. Inform. Assoc. 19, 54–60 (2011).
doi: 10.1136/amiajnl-2011-000376
Observational Health Data Sciences and Informatics. The Book of OHDSI (Independently published, 2019).
Brat, G. A. et al. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. npj Digit. Med. 3, 109 (2020).
doi: 10.1038/s41746-020-00308-0
Melissa, H., Christopher, C. & Kenneth, G. The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment. J. Am. Med. Informatics Assoc. https://doi.org/10.1093/jamia/ocaa196 (2020).
Austin, P. C. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar. Behav. Res. 46, 399–424 (2011).
doi: 10.1080/00273171.2011.568786
Burn, E. et al. Deep phenotyping of 34,128 patients hospitalised with COVID-19 and a comparison with 81,596 influenza patients in America, Europe and Asia: an international network study. medRxiv https://doi.org/10.1101/2020.04.22.20074336 (2020).
OHDSI. CohortDiagnostics. https://github.com/OHDSI/CohortDiagnostics .
OHDSI. FeatureExtraction. https://github.com/OHDSI/FeatureExtraction .