A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19) : A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China
© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissionsoup.com..
BACKGROUND: Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19.
METHODS: In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance.
RESULTS: The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846-.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790-.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit.
CONCLUSIONS: Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:71 |
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| Enthalten in: |
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America - 71(2020), 15 vom: 28. Juli, Seite 833-840 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gong, Jiao [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 10.08.2020 Date Revised 18.12.2020 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/cid/ciaa443 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM308817281 |
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| 100 | 1 | |a Gong, Jiao |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19) |b A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China |
| 264 | 1 | |c 2020 | |
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| 500 | |a Date Revised 18.12.2020 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissionsoup.com. | ||
| 520 | |a BACKGROUND: Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19 | ||
| 520 | |a METHODS: In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance | ||
| 520 | |a RESULTS: The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846-.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790-.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit | ||
| 520 | |a CONCLUSIONS: Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a nomogram | |
| 650 | 4 | |a risk stratification | |
| 650 | 4 | |a severe COVID-19 prediction | |
| 700 | 1 | |a Ou, Jingyi |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Xueping |e verfasserin |4 aut | |
| 700 | 1 | |a Jie, Yusheng |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yaqiong |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Lianxiong |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Mingkai |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Wenxiong |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Fang |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Yaling |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Bo |e verfasserin |4 aut | |
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