A predictive score for progression of COVID-19 in hospitalized persons : a cohort study
Accurate prediction of the risk of progression of coronavirus disease (COVID-19) is needed at the time of hospitalization. Logistic regression analyses are used to interrogate clinical and laboratory co-variates from every hospital admission from an area of 2 million people with sporadic cases. From a total of 98 subjects, 3 were severe COVID-19 on admission. From the remaining subjects, 24 developed severe/critical symptoms. The predictive model includes four co-variates: age (>60 years; odds ratio [OR] = 12 [2.3, 62]); blood oxygen saturation (<97%; OR = 10.4 [2.04, 53]); C-reactive protein (>5.75 mg/L; OR = 9.3 [1.5, 58]); and prothrombin time (>12.3 s; OR = 6.7 [1.1, 41]). Cutoff value is two factors, and the sensitivity and specificity are 96% and 78% respectively. The area under the receiver-operator characteristic curve is 0.937. This model is suitable in predicting which unselected newly hospitalized persons are at-risk to develop severe/critical COVID-19.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:31 |
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| Enthalten in: |
NPJ primary care respiratory medicine - 31(2021), 1 vom: 03. Juni, Seite 33 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xu, Jingbo [VerfasserIn] |
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| Links: |
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| Themen: |
9007-41-4 |
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| Anmerkungen: |
Date Completed 15.06.2021 Date Revised 15.06.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41533-021-00244-w |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM326318755 |
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| 520 | |a Accurate prediction of the risk of progression of coronavirus disease (COVID-19) is needed at the time of hospitalization. Logistic regression analyses are used to interrogate clinical and laboratory co-variates from every hospital admission from an area of 2 million people with sporadic cases. From a total of 98 subjects, 3 were severe COVID-19 on admission. From the remaining subjects, 24 developed severe/critical symptoms. The predictive model includes four co-variates: age (>60 years; odds ratio [OR] = 12 [2.3, 62]); blood oxygen saturation (<97%; OR = 10.4 [2.04, 53]); C-reactive protein (>5.75 mg/L; OR = 9.3 [1.5, 58]); and prothrombin time (>12.3 s; OR = 6.7 [1.1, 41]). Cutoff value is two factors, and the sensitivity and specificity are 96% and 78% respectively. The area under the receiver-operator characteristic curve is 0.937. This model is suitable in predicting which unselected newly hospitalized persons are at-risk to develop severe/critical COVID-19 | ||
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| 700 | 1 | |a Ye, Honghui |e verfasserin |4 aut | |
| 700 | 1 | |a Pang, Wenzheng |e verfasserin |4 aut | |
| 700 | 1 | |a Pang, Pengfei |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Meiwen |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhitao |e verfasserin |4 aut | |
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| 700 | 1 | |a Liang, Anqi |e verfasserin |4 aut | |
| 700 | 1 | |a Zhuang, Juan |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Chunyu |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Jiangnan |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhonghe |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Jinyu |e verfasserin |4 aut | |
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| 700 | 1 | |a Shan, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Yang |e verfasserin |4 aut | |
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