Patient Trajectories Among Persons Hospitalized for COVID-19 : A Cohort Study.


Journal

Annals of internal medicine
ISSN: 1539-3704
Titre abrégé: Ann Intern Med
Pays: United States
ID NLM: 0372351

Informations de publication

Date de publication:
01 2021
Historique:
pubmed: 23 9 2020
medline: 29 1 2021
entrez: 22 9 2020
Statut: ppublish

Résumé

Risk factors for progression of coronavirus disease 2019 (COVID-19) to severe disease or death are underexplored in U.S. cohorts. To determine the factors on hospital admission that are predictive of severe disease or death from COVID-19. Retrospective cohort analysis. Five hospitals in the Maryland and Washington, DC, area. 832 consecutive COVID-19 admissions from 4 March to 24 April 2020, with follow-up through 27 June 2020. Patient trajectories and outcomes, categorized by using the World Health Organization COVID-19 disease severity scale. Primary outcomes were death and a composite of severe disease or death. Median patient age was 64 years (range, 1 to 108 years); 47% were women, 40% were Black, 16% were Latinx, and 21% were nursing home residents. Among all patients, 131 (16%) died and 694 (83%) were discharged (523 [63%] had mild to moderate disease and 171 [20%] had severe disease). Of deaths, 66 (50%) were nursing home residents. Of 787 patients admitted with mild to moderate disease, 302 (38%) progressed to severe disease or death: 181 (60%) by day 2 and 238 (79%) by day 4. Patients had markedly different probabilities of disease progression on the basis of age, nursing home residence, comorbid conditions, obesity, respiratory symptoms, respiratory rate, fever, absolute lymphocyte count, hypoalbuminemia, troponin level, and C-reactive protein level and the interactions among these factors. Using only factors present on admission, a model to predict in-hospital disease progression had an area under the curve of 0.85, 0.79, and 0.79 at days 2, 4, and 7, respectively. The study was done in a single health care system. A combination of demographic and clinical variables is strongly associated with severe COVID-19 disease or death and their early onset. The COVID-19 Inpatient Risk Calculator (CIRC), using factors present on admission, can inform clinical and resource allocation decisions. Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Sections du résumé

BACKGROUND
Risk factors for progression of coronavirus disease 2019 (COVID-19) to severe disease or death are underexplored in U.S. cohorts.
OBJECTIVE
To determine the factors on hospital admission that are predictive of severe disease or death from COVID-19.
DESIGN
Retrospective cohort analysis.
SETTING
Five hospitals in the Maryland and Washington, DC, area.
PATIENTS
832 consecutive COVID-19 admissions from 4 March to 24 April 2020, with follow-up through 27 June 2020.
MEASUREMENTS
Patient trajectories and outcomes, categorized by using the World Health Organization COVID-19 disease severity scale. Primary outcomes were death and a composite of severe disease or death.
RESULTS
Median patient age was 64 years (range, 1 to 108 years); 47% were women, 40% were Black, 16% were Latinx, and 21% were nursing home residents. Among all patients, 131 (16%) died and 694 (83%) were discharged (523 [63%] had mild to moderate disease and 171 [20%] had severe disease). Of deaths, 66 (50%) were nursing home residents. Of 787 patients admitted with mild to moderate disease, 302 (38%) progressed to severe disease or death: 181 (60%) by day 2 and 238 (79%) by day 4. Patients had markedly different probabilities of disease progression on the basis of age, nursing home residence, comorbid conditions, obesity, respiratory symptoms, respiratory rate, fever, absolute lymphocyte count, hypoalbuminemia, troponin level, and C-reactive protein level and the interactions among these factors. Using only factors present on admission, a model to predict in-hospital disease progression had an area under the curve of 0.85, 0.79, and 0.79 at days 2, 4, and 7, respectively.
LIMITATION
The study was done in a single health care system.
CONCLUSION
A combination of demographic and clinical variables is strongly associated with severe COVID-19 disease or death and their early onset. The COVID-19 Inpatient Risk Calculator (CIRC), using factors present on admission, can inform clinical and resource allocation decisions.
PRIMARY FUNDING SOURCE
Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Identifiants

pubmed: 32960645
doi: 10.7326/M20-3905
pmc: PMC7530643
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

33-41

Subventions

Organisme : NIA NIH HHS
ID : T32 AG000247
Pays : United States

Commentaires et corrections

Type : ErratumIn

Auteurs

Brian T Garibaldi (BT)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Jacob Fiksel (J)

Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.F., J.M., J.P., G.S., M.W., K.B., S.L.Z.).

John Muschelli (J)

Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.F., J.M., J.P., G.S., M.W., K.B., S.L.Z.).

Matthew L Robinson (ML)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Masoud Rouhizadeh (M)

Institute for Clinical and Translational Research, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.R., B.W.).

Jamie Perin (J)

Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.F., J.M., J.P., G.S., M.W., K.B., S.L.Z.).

Grant Schumock (G)

Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.F., J.M., J.P., G.S., M.W., K.B., S.L.Z.).

Paul Nagy (P)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Josh H Gray (JH)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Harsha Malapati (H)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Mariam Ghobadi-Krueger (M)

Information Technology, Johns Hopkins Medicine, Baltimore, Maryland (M.G., K.H.).

Timothy M Niessen (TM)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Bo Soo Kim (BS)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Peter M Hill (PM)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

M Shafeeq Ahmed (MS)

Howard Country General Hospital, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.S.A.).

Eric D Dobkin (ED)

Suburban Hospital, Johns Hopkins Medicine, Bethesda, Maryland (E.D.D.).

Renee Blanding (R)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Jennifer Abele (J)

Sibley Memorial Hospital, Johns Hopkins Medicine, Washington, DC (J.A.).

Bonnie Woods (B)

Institute for Clinical and Translational Research, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.R., B.W.).

Kenneth Harkness (K)

Information Technology, Johns Hopkins Medicine, Baltimore, Maryland (M.G., K.H.).

David R Thiemann (DR)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Mary G Bowring (MG)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Aalok B Shah (AB)

Technology Innovation Center, Johns Hopkins University School of Medicine, Baltimore, Maryland (A.B.S.).

Mei-Cheng Wang (MC)

Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.F., J.M., J.P., G.S., M.W., K.B., S.L.Z.).

Karen Bandeen-Roche (K)

Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.F., J.M., J.P., G.S., M.W., K.B., S.L.Z.).

Antony Rosen (A)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

Scott L Zeger (SL)

Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.F., J.M., J.P., G.S., M.W., K.B., S.L.Z.).

Amita Gupta (A)

Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G., M.L.R., P.N., J.H.G., H.M., T.M.N., B.S.K., P.M.H., R.B., D.R.T., M.G.B., A.R., A.G.).

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