Difference in Biomarkers Between COVID-19 Patients and Other Pulmonary Infection Patients
Received 12 April 2020
Accepted for publication 9 July 2020
Published 28 July 2020 Volume 2020:13 Pages 2609—2615
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Professor Suresh Antony
Jingyi Dai1 ,* Yingrong Du1 ,* Jianpeng Gao1 ,* Jun Zhao,2 Lin Wang,3 Ying Huang,1 Jiawei Xia,4 Yu Luo,1 Shenghao Li,4 Edward B McNeil5
1Department of Infectious Diseases, Kunming Third People’s Hospital, Kunming, Yunnan, People’s Republic of China; 2School of Public Health and Management, Hubei University of Medicine, Shiyan, Hubei, People’s Republic of China; 3Department of Clinical Laboratory, Kunming Third People’s Hospital, Kunming, Yunnan, People’s Republic of China; 4Department of Critical Care Medicine, Kunming Third People’s Hospital, Kunming, Yunnan, People’s Republic of China; 5Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand
*These authors contributed equally to this work
Correspondence: Jun Zhao
School of Public Health and Management, Hubei University of Medicine, 30 South Renmin Road, Shiyan, Hubei 442000, People’s Republic of China
Background: The pandemic due to the novel coronavirus disease 2019 (COVID-19) has resulted in an increasing number of patients need to be tested. We aimed to determine if the use of integrated laboratory data can discriminate COVID-19 patients from other pulmonary infection patients.
Methods: This retrospective cohort study was conducted at Kunming Third People’s Hospital in China from January 20 to February 28, 2020. Medical records and laboratory data were extracted and combined for COVID-19 and other pulmonary infection patients on admission. A partial least square discriminant analysis (PLS-DA) model was constructed and calibrated to discriminate COVID-19 from other pulmonary infection patients.
Results: COVID-19 patients diagnosed and treated in Kunming were balanced in terms of sex and covered all age groups. Most of them were mild cases; only five were severe cases. The first two dimensions of the PLS-DA model could classify COVID-19 and other pulmonary infection patients with an accuracy of 96.6% (95.1% in the cross-validation model). Basophil count, the proportion of basophils, prothrombin time, prothrombin time activity, and international normalized ratio were the five most discriminant biomarkers.
Conclusion: Integration of biomarkers can discriminate COVID-19 patients from other pulmonary infections on admission to hospital and thus may be a supplement to nucleic acid tests.
Keywords: COVID-19, biomarker, pneumonia, partial least square discriminant analysis
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