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=8

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=<p><bold>Objective:</bold> To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators.</p><p><bold>Materials and methods:</bold> In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared.</p><p><bold>Results:</bold> 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 &gt;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 in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation <italic>p</italic> &lt; 0.05, including eosinophil percentage (<italic>p</italic> = 0.007), white blood cell count (<italic>p</italic> = 0.045), albumin (<italic>p</italic> = 0.041), aspartate transaminase (<italic>p</italic> = 0.043), lactate dehydrogenase (<italic>p</italic> = 0.002), and hemoglobin (<italic>p</italic> = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators.</p><p><bold>Conclusions:</bold> Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.</p>