Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019 : A Cohort Study in Wuhan, 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: This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19).
METHODS: The training cohort included consecutive COVID-19 patients at the First People's Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020.
RESULTS: A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80-.95); threshold, -2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92-.99); threshold, -2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68-.93) and 0.88 (.75-.96) for the clinical model and laboratory model, respectively.
CONCLUSIONS: We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.
| 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), 16 vom: 19. Nov., Seite 2079-2088 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Kun [VerfasserIn] |
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| Links: |
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| Themen: |
Aspartate Aminotransferases |
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| Anmerkungen: |
Date Completed 14.12.2020 Date Revised 14.12.2020 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/cid/ciaa538 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM309453283 |
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| 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: This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19) | ||
| 520 | |a METHODS: The training cohort included consecutive COVID-19 patients at the First People's Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020 | ||
| 520 | |a RESULTS: A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80-.95); threshold, -2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92-.99); threshold, -2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68-.93) and 0.88 (.75-.96) for the clinical model and laboratory model, respectively | ||
| 520 | |a CONCLUSIONS: We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a mortality | |
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| 700 | 1 | |a Liu, Yuwei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Meng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Xiaofang |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Songpu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xinglin |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Chengyun |e verfasserin |4 aut | |
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