Full metadata record
DC Field | Value | Language |
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dc.citation.number | 15 | - |
dc.citation.startPage | e108 | - |
dc.citation.title | JOURNAL OF KOREAN MEDICAL SCIENCE | - |
dc.citation.volume | 36 | - |
dc.contributor.author | Her, Ae-Young | - |
dc.contributor.author | Bhak, Youngjune | - |
dc.contributor.author | Jun, Eun Jung | - |
dc.contributor.author | Yuan, Song Lin | - |
dc.contributor.author | Garg, Scot | - |
dc.contributor.author | Lee, Semin | - |
dc.contributor.author | Bhak, Jong | - |
dc.contributor.author | Shin, Eun-Seok | - |
dc.date.accessioned | 2023-12-21T16:06:58Z | - |
dc.date.available | 2023-12-21T16:06:58Z | - |
dc.date.created | 2021-06-07 | - |
dc.date.issued | 2021-04 | - |
dc.description.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. | - |
dc.identifier.bibliographicCitation | JOURNAL OF KOREAN MEDICAL SCIENCE, v.36, no.15, pp.e108 | - |
dc.identifier.doi | 10.3346/jkms.2021.36.e108 | - |
dc.identifier.issn | 1011-8934 | - |
dc.identifier.scopusid | 2-s2.0-85105040426 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/53062 | - |
dc.identifier.url | https://jkms.org/DOIx.php?id=10.3346/jkms.2021.36.e108 | - |
dc.identifier.wosid | 000646716500005 | - |
dc.language | 영어 | - |
dc.publisher | KOREAN ACAD MEDICAL SCIENCES | - |
dc.title | A Clinical Risk Score to Predict In-hospital Mortality from COVID-19 in South Korea | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Medicine, General & Internal | - |
dc.identifier.kciid | ART002706740 | - |
dc.relation.journalResearchArea | General & Internal Medicine | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | In-hospital Mortality | - |
dc.subject.keywordAuthor | Death | - |
dc.subject.keywordAuthor | Prediction | - |
dc.subject.keywordAuthor | Risk Score | - |
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