A novel simple scoring model for predicting severity of patients with SARS-CoV-2 infection.
COVID-19
SARS-CoV-2
predict
risk factor
score model
Journal
Transboundary and emerging diseases
ISSN: 1865-1682
Titre abrégé: Transbound Emerg Dis
Pays: Germany
ID NLM: 101319538
Informations de publication
Date de publication:
Nov 2020
Nov 2020
Historique:
received:
24
03
2020
revised:
06
05
2020
accepted:
11
05
2020
pubmed:
30
5
2020
medline:
17
12
2020
entrez:
30
5
2020
Statut:
ppublish
Résumé
An outbreak of pneumonia caused by a novel coronavirus (COVID-19) began in Wuhan, China in December 2019 and quickly spread throughout the country and world. An efficient and convenient method based on clinical characteristics was needed to evaluate the potential deterioration in patients. We aimed to develop a simple and practical risk scoring system to predict the severity of COVID-19 patients on admission. We retrospectively investigated the clinical information of confirmed COVID-19 patients from 10 February 2020 to 29 February 2020 in Wuhan Union Hospital. Predictors of severity were identified by univariate and multivariate logistic regression analysis. A total of 147 patients with confirmed SARS-CoV-2 infection were grouped into non-severe (94 patients) and severe (53 patients) groups. We found that an increased level of white blood cells (WBC), neutrophils, D-dimer, fibrinogen (FIB), IL-6, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), alanine aminotransferase (ALT), aspartate aminotransferase (AST), α-hydroxybutyrate dehydrogenase (HBDH), serum amyloid A (SAA) and a decreased level of lymphocytes were important risk factors associated with severity. Furthermore, three variables were used to formulate a clinical risk scoring system named COVID-19 index = 3 × D-dimer (µg/L) + 2 × lgESR (mm/hr) - 4 × lymphocyte (×10
Identifiants
pubmed: 32469137
doi: 10.1111/tbed.13651
pmc: PMC7283685
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2823-2829Subventions
Organisme : National Natural Science Foundation of China
ID : 31770983
Organisme : National Natural Science Foundation of China
ID : 81974249
Informations de copyright
© 2020 Blackwell Verlag GmbH.
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