AUTHOR=Liu Caidong , Wang Ziyu , Wu Wei , Xiang Changgang , Wu Lingxiang , Li Jie , Hou Weiye , Sun Huiling , Wang Youli , Nie Zhenling , Gao Yingdong , Zhang Ruisheng , Tang Haixia , Wang Qianghu , Li Kening , Xia Xinyi , Li Pengping , Wang Shukui 

TITLE=Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients

JOURNAL=Frontiers in Medicine

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.699706

DOI=10.3389/fmed.2021.699706

ISSN=2296-858X

ABSTRACT=Background: 
To classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators.
Material and methods: 
This is a case series of patients from a China healthcare system in Wuhan. In this retrospective cohort, 3563 patients confirmed COVID-19 pneumonia, including 548 patients in the training dataset, and 3015 patients in the testing dataset.
Main Results:
We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC greater than 0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission. Results showed that this model could stratify the patients in the testing dataset effectively (AUC=0.89). Moreover, laboratory indicators detected in the first week after admission were able to estimate the probability of death (AUC=0.95). Besides, we could diagnose COVID-19 and differentiated it from other kinds of viral pneumonia based on laboratory indicators (accuracy=0.97).
Conclusions:
Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19.