Selected article for: "curve analysis and ROC curve analysis"
Author: Cong-Ying Song; Jia Xu; Jian-Qin He; Yuan-Qiang Lu
Title: COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patients
  • Document date: 2020_3_8
    • ID: fcvstps9_35
    Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.05.20031906 doi: medRxiv preprint 37.0-59.0], P < 0.001), indicating that older patients were more likely to develop severe illness. The proportion of male patients in the severe group was great larger than that of female (71.4% vs 28.6%). Percentage of patients with hypertension was higher in severe group (22 [52.4%] vs 4 [12.9%], P < 0......
    Document: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.05.20031906 doi: medRxiv preprint 37.0-59.0], P < 0.001), indicating that older patients were more likely to develop severe illness. The proportion of male patients in the severe group was great larger than that of female (71.4% vs 28.6%). Percentage of patients with hypertension was higher in severe group (22 [52.4%] vs 4 [12.9%], P < 0.001), while other coexisting disorders, including diabetes, cardiovascular disease and chronic obstructive pulmonary disease showed no significant differences. In addition, many laboratory items and CT score presenting significant differences too. In order to better distinguish severe and non-severe patients, we defined the new threshold value of the selected parameters (P < 0.05) by calculating the cut-off value using ROC curve analysis. Table 3 showed the parameters with AUROC > 0.60.

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