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

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

doi:10.1186/s12879-021-06478-w

Qiao Yang, Tang Tang + Show 34 more

Open Access

https://doi.org/10.1186/s12879-021-06478-w

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Abstract

BackgroundThe 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.MethodsA 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.ResultsOf 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.ConclusionWe 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.

Highlights

The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic
Of the 2169 COVID-19 patients confirmed by RTPCR, the median age was 61 years (IQR 50–70; range 18–100 years)
When comparing laboratory test results between the two groups, we found that the severe group had significantly higher white blood cell (WBC) count, neutrophil count, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), lactic dehydrogenase (LDH), IL-6, procalcitonin and D-dimer levels, but lower lymphocyte count, eosinophilia count, basophilia count, red blood cell (RBC) count, hemoglobin and platelet count

Summary

Introduction

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. The novel coronavirus disease 2019 (COVID-19) has become a pandemic. The most common symptoms of COVID-19 patients were fever, dry cough, fatigue, dyspnea, etc. A study [5] by the Chinese Center for Disease Control and Prevention showed that about 81% COVID-19 patients were considered as mild. The proportion was 14% and 5% respectively, for severe and critical patients, who should be hospitalized or transferred to intensive care unit (ICU) for urgent treatment. How to use effective biomarkers to identify patients who are at high risk of poor clinical outcomes have caused extensive concern

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