Top

European Radiology

Published in:

01-12-2020 | Pneumonia | Chest

A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19

Authors: Le Qin, Yanzhao Yang, Qiqi Cao, Zenghui Cheng, Xiaoyang Wang, Qingfeng Sun, Fuhua Yan, Jieming Qu, Wenjie Yang

Published in: European Radiology | Issue 12/2020

Abstract

Objectives

To develop a predictive model and scoring system to enhance the diagnostic efficiency for coronavirus disease 2019 (COVID-19).

Methods

From January 19 to February 6, 2020, 88 confirmed COVID-19 patients presenting with pneumonia and 80 non-COVID-19 patients suffering from pneumonia of other origins were retrospectively enrolled. Clinical data and laboratory results were collected. CT features and scores were evaluated at the segmental level according to the lesions’ position, attenuation, and form. Scores were calculated based on the size of the pneumonia lesion, which graded at the range of 1 to 4. Air bronchogram, tree-in-bud sign, crazy-paving pattern, subpleural curvilinear line, bronchiectasis, air space, pleural effusion, and mediastinal and/or hilar lymphadenopathy were also evaluated.

Results

Multivariate logistic regression analysis showed that history of exposure (β = 3.095, odds ratio (OR) = 22.088), leukocyte count (β = − 1.495, OR = 0.224), number of segments with peripheral lesions (β = 1.604, OR = 1.604), and crazy-paving pattern (β = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). In this model, values of area under curve (AUC) in the training and testing groups were 0.910 and 0.914, respectively (p < 0.001). A predicted score for COVID-19 (PSC-19) was calculated based on the predictive model by the following formula: PSC-19 = 2 × history of exposure (0–1 point) − 1 × leukocyte count (0–2 points) + 1 × peripheral lesions (0–1 point) + 2 × crazy-paving pattern (0–1 point), with an optimal cutoff point of 1 (sensitivity, 88.5%; specificity, 91.7%).

Conclusions

Our predictive model and PSC-19 can be applied for identification of COVID-19-positive cases, assisting physicians and radiologists until receiving the results of reverse transcription–polymerase chain reaction (RT-PCR) tests.

Key Points

• Prediction of RT-PCR positivity is crucial for fast diagnosis of patients suspected of having coronavirus disease 2019 (COVID-19).

• Typical CT manifestations are advantageous for diagnosing COVID-19 and differentiation of COVID-19 from other types of pneumonia.

• A predictive model and scoring system combining both clinical and CT features were herein developed to enable high diagnostic efficiency for COVID-19.

Zhu N, Zhang D, Wang W et al (2020) A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382:727–733CrossRefPubMedPubMedCentral

Huang C, Wang Y, Li X et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395:497–506PubMedPubMedCentral

Chan JF, Yuan S, Kok KH et al (2020) A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 395:514–523CrossRefPubMedPubMedCentral

Chen N, Zhou M, Dong X et al (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395:507–513CrossRefPubMedPubMedCentral

Li Q, Guan X, Wu P et al (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med 382:1199–1207CrossRefPubMedPubMedCentral

Fang Y, Zhang H, Xie J et al (2020) Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology. https://doi.org/10.1148/radiol.2020200432

Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J (2020) Chest CT for typical 2019-nCoV pneumonia: relationship to negative RT-PCR testing. Radiology. https://doi.org/10.1148/radiol.2020200343

Huang P, Liu T, Huang L et al (2020) Use of chest CT in combination with negative RT-PCR assay for the 2019 novel coronavirus but high clinical suspicion. Radiology 295:22–23CrossRefPubMed

Committee GOoNH (2020) Office of State Administration of Traditional Chinese Medicine. Notice on the issuance of a program for the diagnosis and treatment of novel coronavirus (2019-nCoV) infected pneumonia (trial sixth edition)

10.

Mandell LA, Wunderink RG, Anzueto A et al (2007) Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis 44:S27e72CrossRef

11.

Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology 246:697–722

12.

Lim J, Jeon S, Shin HY et al (2020) Case of the index patient who caused tertiary transmission of COVID-19 infection in Korea: the application of lopinavir/ritonavir for the treatment of COVID-19 infected pneumonia monitored by quantitative RT-PCR. J Korean Med Sci 35:e79CrossRefPubMedPubMedCentral

13.

COVID-19 National Incident Room Surveillance Team (2020) COVID-19, Australia: epidemiology report 2: reporting week ending 19:00 AEDT 8 February 2020. Commun Dis Intell (2018) https://doi.org/10.33321/cdi.32020.33344.33314

14.

Eurosurveillance Editorial Team (2020) Latest updates on COVID-19 from the European Centre for Disease Prevention and Control. Euro Surveill. https://doi.org/10.2807/1560-7917

15.

Hoehl S, Rabenau H, Berger A et al (2020) Evidence of SARS-CoV-2 infection in returning travelers from Wuhan, China. N Engl J Med 382:1278–1280CrossRefPubMedPubMedCentral

16.

Guan W-J, Ni Z-Y, Hu Y et al (2020) Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 382:1708–1720CrossRefPubMed

17.

Rothe C, Schunk M, Sothmann P et al (2020) Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. N Engl J Med 382:970–971CrossRefPubMedPubMedCentral

18.

Huang Y, Liu A, Liang L et al (2018) Diagnostic value of blood parameters for community-acquired pneumonia. Int Immunopharmacol 64:10–15CrossRefPubMed

19.

Song F, Shi N, Shan F et al (2020) Emerging coronavirus 2019-nCoV pneumonia. Radiology 295:210–217CrossRefPubMed

20.

Pan Y, Guan H, Zhou S et al (2020) Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China. Eur Radiol. https://doi.org/10.1007/s00330-00020-06731-x

21.

Ai T, Yang Z, Hou H et al (2020) Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. https://doi.org/10.1148/radiol.2020200642

22.

Lin C, Ding Y, Xie B et al (2020) Asymptomatic novel coronavirus pneumonia patient outside Wuhan: the value of CT images in the course of the disease. Clin Imaging 63:7–9CrossRefPubMedPubMedCentral

23.

Zu ZY, Jiang MD, Xu PP et al (2020) Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology. https://doi.org/10.1148/radiol.2020200490

24.

Chung M, Bernheim A, Mei X et al (2020) CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 295:202–207CrossRefPubMed

25.

Cheng Z, Lu Y, Cao Q et al (2020) Clinical features and chest CT manifestations of coronavirus disease 2019 (COVID-19) in a single-center study in Shanghai, China. AJR Am J Roentgenol. https://doi.org/10.2214/AJR.20.22959

26.

Koo HJ, Lim S, Choe J, Choi SH, Sung H, Do KH (2018) Radiographic and CT features of viral pneumonia. Radiographics 38:719–739

27.

Bai HX, Hsieh B, Xiong Z et al (2020) Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology. https://doi.org/10.1148/radiol.2020200823

28.

Tian S, Hu W, Niu L, Liu H, Xu H, Xiao SY (2020) Pulmonary pathology of early-phase 2019 novel coronavirus (COVID-19) pneumonia in two patients with lung cancer. J Thorac Oncol 15(5):700 –704. https://doi.org/10.20944/preprints202002.0220.v1

29.

Xu Z, Shi L, Wang Y et al (2020) Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 8:420–422CrossRefPubMedPubMedCentral

Title: A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19
Authors: Le Qin
Yanzhao Yang
Qiqi Cao
Zenghui Cheng
Xiaoyang Wang
Qingfeng Sun
Fuhua Yan
Jieming Qu
Wenjie Yang
Publication date: 01-12-2020
Publisher: Springer Berlin Heidelberg
Keywords: Pneumonia
COVID-19
Published in: European Radiology / Issue 12/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07022-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Please log in to get access to this content

Other articles of this Issue 12/2020

Diffusion and perfusion MRI may predict EGFR amplification and the TERT promoter mutation status of IDH-wildtype lower-grade gliomas

Intimate partner violence crisis in the COVID-19 pandemic: how can radiologists make a difference?

Vessel-selective 4D-MR angiography using super-selective pseudo-continuous arterial spin labeling may be a useful tool for assessing brain AVM hemodynamics

Use of dental MRI for radiation-free guided dental implant planning: a prospective, in vivo study of accuracy and reliability

Characterization of parotid gland tumors: added value of permeability MR imaging to DWI and DCE-MRI

Does the change in volumetric functional MR metrics post-TACE predict histopathologic grading of hepatocellular carcinoma?