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WeightNameValue
1000 Titel
  • Development and validation a nomogram for predicting the risk of severe COVID-19: A multi-center study in Sichuan, China
1000 Autor/in
  1. Zhou, Yiwu |
  2. He, Yanqi |
  3. Yang, Huan |
  4. Yu, He |
  5. Wang, Ting |
  6. Chen, Zhu |
  7. Yao, Rong |
  8. Liang, Zongan |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-05-18
1000 Erschienen in
1000 Quellenangabe
  • 15(5):e0233328
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pone.0233328 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • BACKGROUND: Since December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan and spread across the globe. The objective of this study is to build and validate a practical nomogram for estimating the risk of severe COVID-19. METHODS: A cohort of 366 patients with laboratory-confirmed COVID-19 was used to develop a prediction model using data collected from 47 locations in Sichuan province from January 2020 to February 2020. The primary outcome was the development of severe COVID-19 during hospitalization. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data size and select relevant features. Multivariable logistic regression analysis was applied to build a prediction model incorporating the selected features. The performance of the nomogram regarding the C-index, calibration, discrimination, and clinical usefulness was assessed. Internal validation was assessed by bootstrapping. RESULTS: The median age of the cohort was 43 years. Severe patients were older than mild patients by a median of 6 years. Fever, cough, and dyspnea were more common in severe patients. The individualized prediction nomogram included seven predictors: body temperature at admission, cough, dyspnea, hypertension, cardiovascular disease, chronic liver disease, and chronic kidney disease. The model had good discrimination with an area under the curve of 0.862, C-index of 0.863 (95% confidence interval, 0.801–0.925), and good calibration. A high C-index value of 0.839 was reached in the interval validation. Decision curve analysis showed that the prediction nomogram was clinically useful. CONCLUSION: We established an early warning model incorporating clinical characteristics that could be quickly obtained on admission. This model can be used to help predict severe COVID-19 and identify patients at risk of developing severe disease.
1000 Sacherschließung
lokal Dyspnea
gnd 1206347392 COVID-19
lokal Fevers
lokal Chronic liver disease
lokal Respiratory infections
lokal Cardiovascular diseases
lokal Chronic kidney disease
lokal Coughing
lokal Hypertension
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/WmhvdSwgWWl3dQ==|https://frl.publisso.de/adhoc/uri/SGUsIFlhbnFp|https://frl.publisso.de/adhoc/uri/WWFuZywgSHVhbg==|https://frl.publisso.de/adhoc/uri/WXUsIEhl|https://frl.publisso.de/adhoc/uri/V2FuZywgVGluZw==|https://frl.publisso.de/adhoc/uri/Q2hlbiwgWmh1|https://orcid.org/0000-0001-9086-255X|https://frl.publisso.de/adhoc/uri/TGlhbmcsIFpvbmdhbg==
1000 Label
1000 Förderer
  1. Department of Science and Technology of Sichuan Province |
  2. Chengdu Science and Technology Bureau |
1000 Fördernummer
  1. 2020YFS0009; 2020YFS0005
  2. 2016-HM02-00099-SF
1000 Förderprogramm
  1. Emergency Response Project for New Coronavirus
  2. Science and Technology Benefit People Project
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Department of Science and Technology of Sichuan Province |
    1000 Förderprogramm Emergency Response Project for New Coronavirus
    1000 Fördernummer 2020YFS0009; 2020YFS0005
  2. 1000 joinedFunding-child
    1000 Förderer Chengdu Science and Technology Bureau |
    1000 Förderprogramm Science and Technology Benefit People Project
    1000 Fördernummer 2016-HM02-00099-SF
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6421030.rdf
1000 Erstellt am 2020-05-19T15:39:36.040+0200
1000 Erstellt von 122
1000 beschreibt frl:6421030
1000 Bearbeitet von 122
1000 Zuletzt bearbeitet 2020-05-19T15:40:52.687+0200
1000 Objekt bearb. Tue May 19 15:40:34 CEST 2020
1000 Vgl. frl:6421030
1000 Oai Id
  1. oai:frl.publisso.de:frl:6421030 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
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