Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients

Yang, Qiao; Li, Jixi; Zhang, Zhijia; Wu, Xiaocheng; Liao, Tongquan; Yu, Shiyong; You, Zaichun; Hou, Xianhua; Ye, Jun; Liu, Gang; Ma, Siyuan; Xie, Ganfeng; Zhou, Yi; Li, Mengxia; Wu, Meihui; Feng, Yimei; Wang, Weili; Li, Lufeng; Xie, Dongjing; Hu, Yunhui; Liu, Xi; Wang, Bin; Zhao, Songtao; Li, Li; Luo, Chunmei; Tang, Tang; Wu, Hongmei; Hu, Tianyu; Yang, Guangrong; Luo, Bangyu; Li, Lingchen; Yang, Xiu; Li, Qi; Xu, Zhi; Wu, Hao; Sun, Jianguo

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. METHODS: A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. RESULTS: Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. CONCLUSION: We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06478-w.