A predictive score for progression of COVID-19 in hospitalized persons: a cohort study
Abstract 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 ca...
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Nature Portfolio
2021
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oai:doaj.org-article:43320e58c92a49ecb89ec27d60afd8152021-12-02T17:51:28ZA predictive score for progression of COVID-19 in hospitalized persons: a cohort study10.1038/s41533-021-00244-w2055-1010https://doaj.org/article/43320e58c92a49ecb89ec27d60afd8152021-06-01T00:00:00Zhttps://doi.org/10.1038/s41533-021-00244-whttps://doaj.org/toc/2055-1010Abstract 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.Jingbo XuWeida WangHonghui YeWenzheng PangPengfei PangMeiwen TangFeng XieZhitao LiBixiang LiAnqi LiangJuan ZhuangJing YangChunyu ZhangJiangnan RenLin TianZhonghe LiJinyu XiaRobert P. GaleHong ShanYang LiangNature PortfolioarticleDiseases of the respiratory systemRC705-779ENnpj Primary Care Respiratory Medicine, Vol 31, Iss 1, Pp 1-6 (2021) |
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DOAJ |
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DOAJ |
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Diseases of the respiratory system RC705-779 |
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Diseases of the respiratory system RC705-779 Jingbo Xu Weida Wang Honghui Ye Wenzheng Pang Pengfei Pang Meiwen Tang Feng Xie Zhitao Li Bixiang Li Anqi Liang Juan Zhuang Jing Yang Chunyu Zhang Jiangnan Ren Lin Tian Zhonghe Li Jinyu Xia Robert P. Gale Hong Shan Yang Liang A predictive score for progression of COVID-19 in hospitalized persons: a cohort study |
| description |
Abstract 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. |
| format |
article |
| author |
Jingbo Xu Weida Wang Honghui Ye Wenzheng Pang Pengfei Pang Meiwen Tang Feng Xie Zhitao Li Bixiang Li Anqi Liang Juan Zhuang Jing Yang Chunyu Zhang Jiangnan Ren Lin Tian Zhonghe Li Jinyu Xia Robert P. Gale Hong Shan Yang Liang |
| author_facet |
Jingbo Xu Weida Wang Honghui Ye Wenzheng Pang Pengfei Pang Meiwen Tang Feng Xie Zhitao Li Bixiang Li Anqi Liang Juan Zhuang Jing Yang Chunyu Zhang Jiangnan Ren Lin Tian Zhonghe Li Jinyu Xia Robert P. Gale Hong Shan Yang Liang |
| author_sort |
Jingbo Xu |
| title |
A predictive score for progression of COVID-19 in hospitalized persons: a cohort study |
| title_short |
A predictive score for progression of COVID-19 in hospitalized persons: a cohort study |
| title_full |
A predictive score for progression of COVID-19 in hospitalized persons: a cohort study |
| title_fullStr |
A predictive score for progression of COVID-19 in hospitalized persons: a cohort study |
| title_full_unstemmed |
A predictive score for progression of COVID-19 in hospitalized persons: a cohort study |
| title_sort |
predictive score for progression of covid-19 in hospitalized persons: a cohort study |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/43320e58c92a49ecb89ec27d60afd815 |
| work_keys_str_mv |
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