Evidence map›Paper›PMID 32801798›Full record

ArticleInfection and drug resistance2020

Difference in Biomarkers Between COVID-19 Patients and Other Pulmonary Infection Patients.

Jingyi Dai, Yingrong Du, Jianpeng Gao, Jun Zhao, Lin Wang, Ying Huang, Jiawei Xia, Yu Luo, Shenghao Li, Edward B McNeil

Abstract read
In one paragraph

Article in Infection and drug resistance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jingyi Dai *Department of Infectious Diseases, Kunming Third People's Hospital, Kunming, Yunnan, People's Republic of China.
Yingrong Du *Department of Infectious Diseases, Kunming Third People's Hospital, Kunming, Yunnan, People's Republic of China.ORCID 0000-0001-5872-865X
Jianpeng Gao *Department of Infectious Diseases, Kunming Third People's Hospital, Kunming, Yunnan, People's Republic of China.
Jun ZhaoSchool of Public Health and Management, Hubei University of Medicine, Shiyan, Hubei, People's Republic of China.ORCID 0000-0002-0045-311X
Lin WangDepartment of Clinical Laboratory, Kunming Third People's Hospital, Kunming, Yunnan, People's Republic of China.
Ying HuangDepartment of Infectious Diseases, Kunming Third People's Hospital, Kunming, Yunnan, People's Republic of China.
Jiawei XiaDepartment of Critical Care Medicine, Kunming Third People's Hospital, Kunming, Yunnan, People's Republic of China.
Yu LuoDepartment of Infectious Diseases, Kunming Third People's Hospital, Kunming, Yunnan, People's Republic of China.
Shenghao LiDepartment of Critical Care Medicine, Kunming Third People's Hospital, Kunming, Yunnan, People's Republic of China.
Edward B McNeilEpidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand.ORCID 0000-0002-9492-1040

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsThis 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.

resultsCOVID-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.

conclusionIntegration 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.

Indexed as

biomarkerCOVID-19partial least square discriminant analysispneumonia

Identifiers

PMID32801798
PMCPMC7397211

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