3044 Cases reveal important prognosis signatures of COVID-19 patients

Computational and Structural Biotechnology Journal

卷:19

Wei Dai¹  Rongrong Pang¹  Tao Luo²  Yang Yang³  Jiawei Shen⁴  Jian Wu⁴  Chunyan Xue⁵  Weiwei Li⁵  Zhenhua Gan⁶  Xuejia Shi⁶  Jun Zhao⁶  Liming Chen⁶  Libo Zhang⁶  Ying Han⁶  Fei Ma⁶  Ping Jin⁶  Xinyi Xia⁶  Yanju Guo⁶  Qinghua Qiao⁶  Shijie Qin⁶  Canbiao Wang⁷  Bangshun He⁸  Yanjun Wu⁸  Qiuyue Wu⁹ 

[1] Blood Transfusion, Wuhan Huoshenshan Hospital, Wuhan, Hubei 430100, China;

[2] Department of Laboratory Medicine, Nanjing Red Cross Blood Center, Nanjing 210003, Jiangsu, China;

[3] General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, Nanjing, China;

[4] Joint Expert Group for COVID-19, Department of Laboratory Medicine &

[5] Laboratory for Comparative Genomics and Bioinformatics, College of Life Science, Nanjing Normal University, Nanjing 210046, China;

[6] COVID-19 Research Center, Institute of Laboratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing Clinical College of Southern Medical University, Nanjing, Jiangsu 210002, China;

[7] Department of Information, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China;

[8] Medical and Technical Support Department, Pingdingshan Medical District, the 989th Hospital, Pingdingshan, Henan 467000, China;

关键词: COVID-19;    SARS-CoV-2;    Clinical characteristics;    Prognostic factors;    China;   

DOI  :  

来源: DOAJ

【 摘 要 】

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

【 授权许可】

Unknown