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Published on: December 19, 2020
Difference in Biomarkers Between COVID-19 Patients and Other Pulmonary Infection Patients
Jingyi Dai1, Yingrong Du1, Jianpeng Gao1
1Department of Infectious Diseases, Kunming Third People's Hospital, Kunming, Yunnan, People's Republic of China.
Insights
Integrated laboratory data, including basophil counts and coagulation parameters, can accurately distinguish COVID-19 patients from other pulmonary infections. This approach may supplement nucleic acid testing for early diagnosis.
Area of Science:
- Medical diagnostics
- Infectious disease research
- Biomarker discovery
Background:
- The COVID-19 pandemic necessitates rapid and accurate patient testing.
- Distinguishing COVID-19 from other pulmonary infections is crucial for effective management.
- Integrated laboratory data offers a potential diagnostic avenue.
Purpose of the Study:
- To evaluate the efficacy of integrated laboratory data in differentiating COVID-19 patients from those with other pulmonary infections.
- To identify key laboratory biomarkers for distinguishing between these conditions.
Main Methods:
- Retrospective cohort study at Kunming Third People's Hospital (Jan 20-Feb 28, 2020).
- Combined medical records and laboratory data for COVID-19 and other pulmonary infection patients.
- Utilized partial least square discriminant analysis (PLS-DA) for classification.
Main Results:
- The PLS-DA model achieved 96.6% accuracy in classifying COVID-19 patients.
- Cross-validation confirmed model reliability with 95.1% accuracy.
- Key discriminant biomarkers included basophil count, prothrombin time, and international normalized ratio.
Conclusions:
- Integrated laboratory biomarkers can effectively discriminate COVID-19 from other pulmonary infections upon hospital admission.
- This method shows promise as a supplementary diagnostic tool alongside nucleic acid tests.
- Biomarker integration aids in early identification and management of COVID-19.
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.
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