BROWSE

Related Researcher

Author's Photo

Bhak, Jong
KOrean GenomIcs Center(KOGIC)
Research Interests
  • Geromics, genomics, bioinformatics, protein Engineering, OMICS

ITEM VIEW & DOWNLOAD

A Clinical Risk Score to Predict In-hospital Mortality from COVID-19 in South Korea

Cited 0 times inthomson ciCited 0 times inthomson ci
Title
A Clinical Risk Score to Predict In-hospital Mortality from COVID-19 in South Korea
Author
Her, Ae-YoungBhak, YoungjuneJun, Eun JungYuan, Song LinGarg, ScotLee, SeminBhak, JongShin, Eun-Seok
Issue Date
2021-04
Publisher
KOREAN ACAD MEDICAL SCIENCES
Citation
JOURNAL OF KOREAN MEDICAL SCIENCE, v.36, no.15, pp.e108
Abstract
Background: Early identification of patients with coronavirus disease 2019 (COVID-19) who are at high risk of mortality is of vital importance for appropriate clinical decision making and delivering optimal treatment. We aimed to develop and validate a clinical risk score for predicting mortality at the time of admission of patients hospitalized with COVID-19. Methods: Collaborating with the Korea Centers for Disease Control and Prevention (KCDC), we established a prospective consecutive cohort of 5,628 patients with confirmed COVID-19 infection who were admitted to 120 hospitals in Korea between January 20, 2020, and April 30, 2020. The cohort was randomly divided using a 7:3 ratio into a development (n = 3,940) and validation (n = 1,688) set. Clinical information and complete blood count (CBC) detected at admission were investigated using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-Mortality Score). The discriminative power of the risk model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic curves. Results: The incidence of mortality was 4.3% in both the development and validation set. A COVID-Mortality Score consisting of age, sex, body mass index, combined comorbidity, clinical symptoms, and CBC was developed. AUCs of the scoring system were 0.96 (95% confidence interval [CI], 0.85-0.91) and 0.97 (95% CI, 0.84-0.93) in the development and validation set, respectively. If the model was optimized for > 90% sensitivity, accuracies were 81.0% and 80.2% with sensitivities of 91.7% and 86.1% in the development and validation set, respectively. The optimized scoring system has been applied to the public online risk calculator (https://www.diseaseriskscore.com). Conclusion: This clinically developed and validated COVID-Mortality Score, using clinical data available at the time of admission, will aid clinicians in predicting in-hospital mortality.
URI
https://scholarworks.unist.ac.kr/handle/201301/53062
URL
https://jkms.org/DOIx.php?id=10.3346/jkms.2021.36.e108
DOI
10.3346/jkms.2021.36.e108
ISSN
1011-8934
Appears in Collections:
BME_Journal Papers
Files in This Item:
000646716500005.pdf Download

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU