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Risk assessment of progression to severe conditions for patients with COVID-19 pneumonia: a single-center retrospective study

By 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

Posted 30 Mar 2020
medRxiv DOI: 10.1101/2020.03.25.20043166

Background: Management of high mortality risk due to significant progression requires prior assessment of time-to-progression. However, few related methods are available for COVID-19 pneumonia. Methods: We retrospectively enrolled 338 adult patients admitted to one hospital between Jan 11, 2020 to Feb 29, 2020. The final follow-up date was March 8, 2020. We compared characteristics between patients with severe and non-severe outcome, and used multivariate survival analyses to assess the risk of progression to severe conditions. Results: A total of 76 (31.9%) patients progressed to severe conditions and 3 (0.9%) died. The mean time from hospital admission to severity onset is 3.7 days. Age, body mass index (BMI), fever symptom on admission, co-existing hypertension or diabetes are associated with severe progression. Compared to non-severe group, the severe group already demonstrated, at an early stage, abnormalities in biomarkers indicating organ function, inflammatory responses, blood oxygen and coagulation function. The cohort is characterized with increasing cumulative incidences of severe progression up to 10 days after admission. Competing risks survival model incorporating CT imaging and baseline information showed an improved performance for predicting severity onset (mean time-dependent AUC = 0.880). Conclusions: Multiple predisposition factors can be utilized to assess the risk of progression to severe conditions at an early stage. Multivariate survival models can reasonably analyze the progression risk based on early-stage CT images that would otherwise be misjudged by artificial analysis. Acknowledgement: This work is funded by Shenzhen Science and Technology Innovation Commission (JSGG20200519160750001) and Sanming Project of Medicine in Shenzhen (SZSM201812058), China.

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