A novel severity score to predict inpatient mortality in COVID-19 patients.
Sci Rep
; 10(1): 16726, 2020 10 07.
Article
in English
| MEDLINE | ID: covidwho-841180
ABSTRACT
COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality. 4711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n = 2355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2356 patients. Mortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814-0.851) and an AUC of 0.798 (95% CI 0.789-0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0-3), moderate (4-6) and high (7-10) COVID-19 severity score. This developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Research Design
/
Severity of Illness Index
/
Hospital Mortality
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Coronavirus Infections
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Betacoronavirus
Type of study:
Cohort study
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Experimental Studies
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Observational study
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Prognostic study
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Randomized controlled trials
Limits:
Adult
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Aged
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Female
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Humans
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Male
/
Middle aged
Language:
English
Journal:
Sci Rep
Year:
2020
Document Type:
Article
Affiliation country:
S41598-020-73962-9
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