Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram
DC Field | Value | Language |
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dc.contributor.author | Moon, Hui Jeong | - |
dc.contributor.author | Kim, Kyunghoon | - |
dc.contributor.author | Kang, Eun Kyeong | - |
dc.contributor.author | Yang, Hyeon-Jong | - |
dc.contributor.author | Lee, Eun | - |
dc.date.accessioned | 2022-06-03T01:40:06Z | - |
dc.date.available | 2022-06-03T01:40:06Z | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 1011-8934 | - |
dc.identifier.issn | 1598-6357 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/20911 | - |
dc.description.abstract | Background: Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. Methods: This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. Results: Age >= 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/. Conclusion: The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes. | - |
dc.format.extent | 15 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 대한의학회 | - |
dc.title | Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.doi | 10.3346/jkms.2021.36.e248 | - |
dc.identifier.scopusid | 2-s2.0-85114863431 | - |
dc.identifier.wosid | 000694750800002 | - |
dc.identifier.bibliographicCitation | Journal of Korean Medical Science, v.36, no.35, pp 1 - 15 | - |
dc.citation.title | Journal of Korean Medical Science | - |
dc.citation.volume | 36 | - |
dc.citation.number | 35 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 15 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | General & Internal Medicine | - |
dc.relation.journalWebOfScienceCategory | Medicine, General & Internal | - |
dc.subject.keywordPlus | UNDERLYING CONDITIONS | - |
dc.subject.keywordPlus | RESPIRATORY SYNDROME | - |
dc.subject.keywordPlus | CORONAVIRUS | - |
dc.subject.keywordPlus | OUTCOMES | - |
dc.subject.keywordPlus | DISEASE | - |
dc.subject.keywordPlus | KOREA | - |
dc.subject.keywordPlus | RATES | - |
dc.subject.keywordPlus | RISK | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | Mortality | - |
dc.subject.keywordAuthor | Nomogram | - |
dc.subject.keywordAuthor | Underlying Diseases | - |
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