Author: Lijiao Zeng; Jialu Li; Mingfeng Liao; Rui Hua; Pilai Huang; Mingxia Zhang; Youlong Zhang; Qinlang Shi; Zhaohua Xia; Xinzhong Ning; Dandan Liu; Jiu Mo; Ziyuan Zhou; Zigang Li; Yu Fu; Yuhui Liao; Jing Yuan; Lifei Wang; Qing He; Lei Liu; Kun Qiao
Title: Risk assessment of progression to severe conditions for patients with COVID-19 pneumonia: a single-center retrospective study Document date: 2020_3_30
ID: 8n3q30hy_39
Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.25.20043166 doi: medRxiv preprint from this study will be helpful to medical practitioners as they consider how to better manage the care of COVID-19 pneumonia patients upon admission. Figure 1 : Analysis of representative features significantly associated with the severe group. A, the age distribution of the study cohort, which is overla.....
Document: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.25.20043166 doi: medRxiv preprint from this study will be helpful to medical practitioners as they consider how to better manage the care of COVID-19 pneumonia patients upon admission. Figure 1 : Analysis of representative features significantly associated with the severe group. A, the age distribution of the study cohort, which is overlaid with kernel density estimates (solid curve); B, boxplot summary of the age and BMI as stratified by the severe and non-severe group; C, levels of individual laboratory biomarker plotted along with the time after symptom onset. Data at the same day from multiple patients were collapsed together and only the median value (solid dots) was shown.
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