Top

Published in:

01-12-2021 | Artificial Intelligence | Research article

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

Authors: Xingrui Wang, Qinglin Che, Xiaoxiao Ji, Xinyi Meng, Lang Zhang, Rongrong Jia, Hairong Lyu, Weixian Bai, Lingjie Tan, Yanjun Gao

Published in: BMC Infectious Diseases | Issue 1/2021

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.

Available only for authorised users

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.

Chen J. Pathogenicity and transmissibility of 2019-nCoV-A quick overview and comparison with other emerging viruses. Microbes Infect. 2020;22(2):69–71.CrossRefPubMedPubMedCentral

World Health Organization. Coronavirus disease (COVID-19) pandemic.2020. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed 15 Mar 2020.

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–E7.CrossRefPubMed

Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500–10.CrossRefPubMedPubMedCentral

Epidemiology Working Group for NCIP Epidemic Response. Chinese Center for Disease Control and Prevention. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua liu xing bing xue za zhi. 2020;41(2):145–51.

Onder G, Rezza G, Case-Fatality Rate BS. Characteristics of patients dying in relation to COVID-19 in Italy. JAMA. 2020;323(18):1775–6.PubMed

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497–506.CrossRefPubMedPubMedCentral

Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020;323(11):1061–1069.

10.

National Health Commission of the People's Republic of China. Notice on the novel coronavirus infection diagnosis and treatment plan (trial version 7) (in Chinese). In: Medical Letter Issued by General Office of National Health Commission (2020) No.184. Beijing, 2020. Available from: http://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a7dfe4cef80dc7f5912eb1989.shtml. Accessed 15 Mar 2020.

11.

Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, Xue Z, Shen D, Shi Y. Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction. Med Phys. 2020; https://doi.org/10.1002/mp.14609.

12.

Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, Liu S, Zhao P, Liu H, Zhu L, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med. 2020;8(4):420–2.CrossRefPubMedPubMedCentral

13.

Liu Q, Wang RS, Qu GQ, Wang YY, Liu P, Zhu YZ, Fei G, Ren L, Zhou YW, Liu L. Gross examination report of a COVID-19 death autopsy. Fa yi xue za zhi. 2020;36(1):21–3.PubMed

14.

Yao XH, Li TY, He ZC, Ping YF, Liu HW, Yu SC, Mou HM, Wang LH, Zhang HR, Fu WJ, et al. A pathological report of three COVID-19 cases by minimal invasive autopsies. Zhonghua bing li xue za zhi. 2020;49(5):411–7.PubMed

15.

Liu J, Zheng X, Tong Q, Li W, Wang B, Sutter K, Trilling M, Lu M, Dittmer U, Yang D. Overlapping and discrete aspects of the pathology and pathogenesis of the emerging human pathogenic coronaviruses SARS-CoV, MERS-CoV, and 2019-nCoV. J Med Virol. 2020;92(5):491–4.CrossRefPubMedPubMedCentral

16.

Wang HJ, Du SH, Yue X, Review CCX. Prospect of pathological features of Corona virus disease. Fa yi xue za zhi. 2020;36(1):16–20.PubMed

17.

Chan PK, Chen GG. Mechanisms of lymphocyte loss in SARS coronavirus infection. Hong Kong Med J. 2008;14(Suppl 4):21–6.PubMed

18.

Letko M, Marzi A, Munster V. Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses. Nat Microbiol. 2020;5(4):562–9.CrossRefPubMed

19.

Hamming I, Timens W, Bulthuis ML, Lely AT, Navis G, van Goor H. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J Pathol. 2004;203(2):631–7.CrossRefPubMedPubMedCentral

20.

Sobek V, Balkow S, Körner H, Simon MM. Antigen-induced cell death of T effector cells in vitro proceeds via the Fas pathway, requires endogenous interferon-γ and is independent of perforin and granzymes. Eur J Immunol. 2002;32(9):2490–9.CrossRefPubMed

21.

Bahl K, Kim SK, Calcagno C, Ghersi D, Puzone R, Celada F, Selin LK, Welsh RM. IFN-induced attrition of CD8 T cells in the presence or absence of cognate antigen during the early stages of viral infections. J Immunol. 2006;176(7):4284–95.CrossRefPubMed

22.

Chen RF, Chang JC, Yeh WT, Lee CH, Liu JW, Eng HL, Yang KD. Role of vascular cell adhesion molecules and leukocyte apoptosis in the lymphopenia and thrombocytopenia of patients with severe acute respiratory syndrome (SARS). Microbes Infect. 2006;8(1):122–7.CrossRefPubMed

23.

Robertson AM, Bird CC, Waddell AW, Currie AR. Morphological aspects of glucocorticoid-induced cell death in human lymphoblastoid cells. J Pathol. 1978;126(3):181–7.CrossRefPubMed

24.

Fauci AS, Dale DC, Balow JE. Glucocorticosteroid therapy: mechanisms of action and clinical considerations. Ann Intern Med. 1976;84(3):304–15.CrossRefPubMed

25.

Norris DA, Fine R, Weston WL, Spector S. Monocyte cellular function in asthmatic patients on alternate-day steroid therapy. J Allergy Clin Immunol. 1978;61(4):255–60.CrossRefPubMed

26.

Russell CD, Millar JE, Baillie JK. Clinical evidence does not support corticosteroid treatment for 2019-nCoV lung injury. Lancet. 2020;395(10223):473–5.CrossRefPubMedPubMedCentral

27.

Chen RC, Tang XP, Tan SY, Liang BL, Wan ZY, Fang JQ, Zhong N. Treatment of severe acute respiratory syndrome with glucosteroids: the Guangzhou experience. Chest. 2006;129(6):1441–52.CrossRefPubMed

28.

Villar J, Ferrando C, Martínez D, Ambrós A, Muñoz T, Soler JA, Aguilar G, Alba F, González-Higueras E, Conesa LA, et al. Dexamethasone treatment for the acute respiratory distress syndrome: a multicentre, randomised controlled trial. Lancet Respir Med. 2020;8(3):267–76.CrossRefPubMed

29.

Belz GT, Smith CM, Kleinert L, Reading P, Brooks A, Shortman K, Carbone FR, heath WR. Distinct migrating and nonmigrating dendritic cell populations are involved in MHC class I-restricted antigen presentation after lung infection with virus. Proc Natl Acad Sci U S A 2004;101(23):8670–8675.

30.

Norbury CC, Malide D, Gibbs JS, Bennink JR, Yewdell JW. Visualizing priming of virus-specific CD8+ T cells by infected dendritic cells in vivo. Nat Immunol. 2002;3(3):265–71.CrossRefPubMed

31.

Wong CK, Lam CW, Wu AK, Ip WK, Lee NL, Chan IH, Lit LC, Hui DS, Chan MH, Chung SS, et al. Plasma inflammatory cytokines and chemokines in severe acute respiratory syndrome. Clin Exp Immunol. 2004;136(1):95–103.CrossRefPubMedPubMedCentral

32.

Gralinski LE, Sheahan TP, Morrison TE, Menachery VD, Jensen K, Leist SR, Whitmore A, Heise MT, Baric RS. Complement activation contributes to severe acute respiratory syndrome coronavirus pathogenesis. mBio. 2018;9(5).

33.

Chan JC, Tsui EL, Wong VC. Prognostication in severe acute respiratory syndrome: a retrospective time-course analysis of 1312 laboratory-confirmed patients in Hong Kong. Respirology. 2007;12(4):531–42.CrossRefPubMedPubMedCentral

34.

Ichikawa A, Kuba K, Morita M, Chida S, Tezuka H, Hara H, Sasaki T, Ohteki T, Ranieri VM. dos Santos CC, et al. CXCL10-CXCR3 enhances the development of neutrophil-mediated fulminant lung injury of viral and nonviral origin. Am J Respir Crit Care Med. 2013;187(1):65–77.CrossRefPubMedPubMedCentral

Title: Correlation between lung infection severity and clinical laboratory indicators in patients with COVID-19: a cross-sectional study based on machine learning
Authors: Xingrui Wang
Qinglin Che
Xiaoxiao Ji
Xinyi Meng
Lang Zhang
Rongrong Jia
Hairong Lyu
Weixian Bai
Lingjie Tan
Yanjun Gao
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Artificial Intelligence
SARS-CoV-2
COVID-19
Published in: BMC Infectious Diseases / Issue 1/2021
Electronic ISSN: 1471-2334
DOI: https://doi.org/10.1186/s12879-021-05839-9

ACC 2024 Congress Coverage

Heart Failure Video

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusions

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2021

Human papillomavirus-associated anal squamous intraepithelial lesions in men who have sex with men and transgender women living with and without HIV in Karachi Pakistan: implications for screening and prevention

Effectiveness of mobile text reminder in improving adherence to medication, physical exercise, and quality of life in patients living with HIV: a systematic review

The association between ambient air pollution and scarlet fever in Qingdao, China, 2014–2018: a quantitative analysis

Cutaneous Leishmaniasis in Pakistan: a neglected disease needing one health strategy

Monitoring sick leave data for early detection of influenza outbreaks

Association between glucocorticoids treatment and viral clearance delay in patients with COVID-19: a systematic review and meta-analysis

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page