3044 cases reveal important prognosis signatures of COVID-19 patients

Qin, Shijie; Li, Weiwei; Shi, Xuejia; Wu, Yanjun; Wang, Canbiao; Shen, Jiawei; Pang, Rongrong; He, Bangshun; Zhao, Jun; Qiao, Qinghua; Luo, Tao; Guo, Yanju; Yang, Yang; Han, Ying; Wu, Qiuyue; Wu, Jian; Dai, Wei; Zhang, Libo; Chen, Liming; Xue, Chunyan; Jin, Ping; Gan, Zhenhua; Ma, Fei; Xia, Xinyi

Critical patients and intensive care unit (ICU) patients are the main population of COVID-19 deaths. Therefore, establishing a reliable method is necessary for COVID-19 patients to distinguish patients who may have critical symptoms from other patients. In this retrospective study, we firstly evaluated the effects of 54 laboratory indicators on critical illness and death in 3044 COVID-19 patients from the Huoshenshan hospital in Wuhan, China. Secondly, we identify the eight most important prognostic indicators (neutrophil percentage, procalcitonin, neutrophil absolute value, C-reactive protein, albumin, interleukin-6, lymphocyte absolute value and myoglobin) by using the random forest algorithm, and find that dynamic changes of the eight prognostic indicators present significantly distinct within differently clinical severities. Thirdly, our study reveals that a model containing age and these eight prognostic indicators can accurately predict which patients may develop serious illness or death. Fourthly, our results demonstrate that different genders have different critical illness rates compared with different ages, in particular the mortality is more likely to be attributed to some key genes (e.g. ACE2, TMPRSS2 and FURIN) by combining the analysis of public lung single cells and bulk transcriptome data. Taken together, we urge that the prognostic model and first-hand clinical trial data generated in this study have important clinical practical significance for predicting and exploring the disease progression of COVID-19 patients.