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. 2021 Feb 18;21(1):192.
doi: 10.1186/s12879-021-05839-9.

Correlation between lung infection severity and clinical laboratory indicators in patients with COVID-19: a cross-sectional study based on machine learning

Xingrui Wang #  1   2 , Qinglin Che #  3 , Xiaoxiao Ji  1 , Xinyi Meng  1 , Lang Zhang  1 , Rongrong Jia  1 , Hairong Lyu  1 , Weixian Bai  1 , Lingjie Tan  1 , Yanjun Gao  4   5
Affiliations

Correlation between lung infection severity and clinical laboratory indicators in patients with COVID-19: a cross-sectional study based on machine learning

Xingrui Wang et al. BMC Infect Dis. .

Abstract

Background: Coronavirus disease 2019 (COVID-19) has caused a global pandemic that has raised worldwide concern. This study aims to investigate the correlation between the extent of lung infection and relevant clinical laboratory testing indicators in COVID-19 and to analyse its underlying mechanism.

Methods: Chest high-resolution computer tomography (CT) images and laboratory examination data of 31 patients with COVID-19 were extracted, and the lesion areas in CT images were quantitatively segmented and calculated using a deep learning (DL) system. A cross-sectional study method was carried out to explore the differences among the proportions of lung lobe infection and to correlate the percentage of infection (POI) of the whole lung in all patients with clinical laboratory examination values.

Results: No significant difference in the proportion of infection was noted among various lung lobes (P > 0.05). The POI of total lung was negatively correlated with the peripheral blood lymphocyte percentage (L%) (r = - 0.633, P < 0.001) and lymphocyte (LY) count (r = - 0.555, P = 0.001) but positively correlated with the neutrophil percentage (N%) (r = 0.565, P = 0.001). Otherwise, the POI was not significantly correlated with the peripheral blood white blood cell (WBC) count, monocyte percentage (M%) or haemoglobin (HGB) content. In some patients, as the infection progressed, the L% and LY count decreased progressively accompanied by a continuous increase in the N%.

Conclusions: Lung lesions in COVID-19 patients are significantly correlated with the peripheral blood lymphocyte and neutrophil levels, both of which could serve as prognostic indicators that provide warning implications, and contribute to clinical interventions in patients.

Keywords: Artificial intelligence; COVID-19; Deep learning; Infection severity; Lymphocyte; Neutrophil; SARS-CoV-2.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Software interface obtained by inputting original HRCT images of one patient into the DL system. The VOIs and POIs in the lung lobes and bronchopulmonary segments are presented; the HU histogram within the infection regions can be visualized
Fig. 2
Fig. 2
Lesion segmentation results of three COVID-19 cases displayed using the software post-processing platform. Rows 1–3: early, progressive, and severe stages. Columns 1–3: CT image, CT images overlaid with segmentation, and 3D surface rendering of segmented infections
Fig. 3
Fig. 3
Correlation between total pulmonary POI and L%, LY count, or N%. POI of the total lung was negatively correlated with the L% [r = −0.633, P < 0.001, (a)] and LY count [r = − 0.555, P = 0.001, (b)] but positively correlated with the N% [r = 0.565, P = 0.001, (c)]
Fig. 4
Fig. 4
Dynamic trend of L%, LY count, and N% in several patients. As the pulmonary infection volume increased, L% and LY count exhibit progressively decreased accompanied by continuously increased N%
See this image and copyright information in PMC

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References

    1. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, et al. A novel coronavirus from patients with pneumonia in China, 2019 N Engl J Med 2020;382(8):727–733. - PMC - PubMed
    1. Chen J. Pathogenicity and transmissibility of 2019-nCoV-A quick overview and comparison with other emerging viruses. Microbes Infect. 2020;22(2):69–71. doi: 10.1016/j.micinf.2020.01.004. - DOI - PMC - PubMed
    1. World Health Organization. Coronavirus disease (COVID-19) pandemic.2020. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed 15 Mar 2020.
    1. Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, Ji W. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology. 2020;296(2):E115–E1E7. doi: 10.1148/radiol.2020200432. - DOI - PMC - PubMed
    1. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500–510. doi: 10.1038/s41568-018-0016-5. - DOI - PMC - PubMed