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_25
Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the .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 non-severe group ( Figure 3A ). We then computed same features from earliest CT scans after admission for each patient, and trained a risk prediction score using high-dimensional survival modeling. The model integrating CT and baseline variables significantly outperformed the univariate model and multivar.....
Document: The copyright holder for this preprint (which was not peer-reviewed) is the .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 non-severe group ( Figure 3A ). We then computed same features from earliest CT scans after admission for each patient, and trained a risk prediction score using high-dimensional survival modeling. The model integrating CT and baseline variables significantly outperformed the univariate model and multivariate model using only baseline information ( Figure 3B, 3C ). The best model achieved a mean time-dependent AUC of 0.880 (sd=0.011) and a mean prediction error of 0.079 (sd=0.024). We also developed a model integrating laboratory biomarkers tested at a time within one day of admission. This model has a mean AUC of 0.884 (sd=0.049) and a mean prediction error of 0.103 (sd=0.031) ( Figure S2 in Supplementary Appendix).
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