Encephalopathy at admission predicts adverse outcomes in patients with SARS-CoV-2 infection.


Journal

CNS neuroscience & therapeutics
ISSN: 1755-5949
Titre abrégé: CNS Neurosci Ther
Pays: England
ID NLM: 101473265

Informations de publication

Date de publication:
10 2021
Historique:
revised: 12 05 2021
received: 30 11 2020
accepted: 14 05 2021
pubmed: 17 6 2021
medline: 29 9 2021
entrez: 16 6 2021
Statut: ppublish

Résumé

To determine if neurologic symptoms at admission can predict adverse outcomes in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Electronic medical records of 1053 consecutively hospitalized patients with laboratory-confirmed infection of SARS-CoV-2 from one large medical center in the USA were retrospectively analyzed. Univariable and multivariable Cox regression analyses were performed with the calculation of areas under the curve (AUC) and concordance index (C-index). Patients were stratified into subgroups based on the presence of encephalopathy and its severity using survival statistics. In sensitivity analyses, patients with mild/moderate and severe encephalopathy (defined as coma) were separately considered. Of 1053 patients (mean age 52.4 years, 48.0% men [n = 505]), 35.1% (n = 370) had neurologic manifestations at admission, including 10.3% (n = 108) with encephalopathy. Encephalopathy was an independent predictor for death (hazard ratio [HR] 2.617, 95% confidence interval [CI] 1.481-4.625) in multivariable Cox regression. The addition of encephalopathy to multivariable models comprising other predictors for adverse outcomes increased AUCs (mortality: 0.84-0.86, ventilation/ intensive care unit [ICU]: 0.76-0.78) and C-index (mortality: 0.78 to 0.81, ventilation/ICU: 0.85-0.86). In sensitivity analyses, risk stratification survival curves for mortality and ventilation/ICU based on severe encephalopathy (n = 15) versus mild/moderate encephalopathy (n = 93) versus no encephalopathy (n = 945) at admission were discriminative (p < 0.001). Encephalopathy at admission predicts later progression to death in SARS-CoV-2 infection, which may have important implications for risk stratification in clinical practice.

Identifiants

pubmed: 34132473
doi: 10.1111/cns.13687
pmc: PMC8444722
doi:

Types de publication

Journal Article Multicenter Study Observational Study Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1127-1135

Subventions

Organisme : National Cancer Institute (NCI) of the National Institutes of Health under Award
ID : R03CA249554
Organisme : Hunan Natural Science Foundation under Award
ID : 2018JJ3709
Organisme : Brown University COVID-19 seed grant
ID : GR399196
Organisme : Research Scholar Grant by RSNA Research &amp; Education Foundation
Organisme : National Natural Science Foundation of China grant under Award
ID : 8181101287
Organisme : National Natural Science Foundation of China grant under Award
ID : 81971696
Organisme : Amazon Web Service for the COVID-19 Diagnostic Development Initiative
Organisme : Hunan Natural Science Foundation under Award
ID : GR399196

Informations de copyright

© 2021 The Authors. CNS Neuroscience & Therapeutics Published by John Wiley & Sons Ltd.

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Auteurs

Lei Tang (L)

Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Xiangya School of Medicine, Central South University, Changsha, China.

Shixin Liu (S)

Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Xiangya School of Medicine, Central South University, Changsha, China.

Yanhe Xiao (Y)

Xiangya School of Medicine, Central South University, Changsha, China.

Thi My Linh Tran (TML)

Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA.

Ji Whae Choi (JW)

Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA.

Jing Wu (J)

Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China.

Kasey Halsey (K)

Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA.

Raymond Y Huang (RY)

Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.

Jerrold Boxerman (J)

Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA.

Sohil H Patel (SH)

Department of Radiology, University of Virginia, Charlottesville, VA, USA.

David Kung (D)

Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

Renyu Liu (R)

Department of Anaesthesiology and critical care medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

Michael D Feldman (MD)

Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

Daniel D Danoski (DD)

Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

Wei-Hua Liao (WH)

Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.

Scott E Kasner (SE)

Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

Tao Liu (T)

Department of Biostatistics and Public Health, Brown University, Providence, RI, USA.

Bo Xiao (B)

Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.

Paul J Zhang (PJ)

Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

Michael Reznik (M)

Department of Neurology, Warren Alpert Medical School of Brown University, Providence, RI, USA.

Harrison X Bai (HX)

Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA.

Li Yang (L)

Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China.

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