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Identification, Monitoring, and Prediction of Disease Severity in Patients with COVID-19 Pneumonia Based on Chest Computed Tomography Scans: A Retrospective Study

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Book cover Clinical, Biological and Molecular Aspects of COVID-19

Part of the book series: Advances in Experimental Medicine and Biology ((PMISB,volume 1321))

Abstract

  • Background and Aims

Non-contrast chest computed tomography (CT) scans can accurately evaluate the type and extent of lung lesions. The aim of this study was to investigate the chest CT features associated with critical and non-critical patients with coronavirus disease 2019 (COVID-19).

  • Methods

A total of 1078 patients with COVID-19 pneumonia who underwent chest CT scans, including 169 critical cases and 909 non-critical cases, were enrolled in this retrospective study. The scans of all participants were reviewed and compared in two groups of study. In addition, the risk factors associated with disease in critical and non-critical patients were analyzed.

  • Results

Chest CT scans showed bilateral and multifocal involvement in most (86.4%) of the participants, with 97.6 and 84.3% reported in critical and non-critical patients, respectively. The incidences of pure consolidation (p = 0.019), mixed ground-glass opacities (GGOs) and consolidation (p < 0.001), pleural effusion (p < 0.001), and intralesional traction bronchiectasis (p = 0.007) were significantly higher in critical compared to non-critical patients. However, non-critical patients showed higher incidence of pure GGOs than the critical patients (p < 0.001). Finally, the total opacity scores of the critical patients were significantly higher than those of non-critical patients (13.71 ± 6.26 versus 4.86 ± 3.52, p < 0.001), with an area under the curve of 0.91 (0.88–0.94) for COVID-19 detection.

  • Conclusions

Our results revealed that the chest CT examination was an effective means of detecting pulmonary parenchymal abnormalities in the natural course of COVID-19. It can distinguish the critical patients from the non-critical patients (AUC = 0.91), which is helpful for the judgment of clinical condition and has important clinical value for the diagnosis and follow-up of COVID-19 pneumonia.

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Acknowledgments

Thanks to guidance and advice from the Clinical Research Development Unit of Baqiyatallah Hospital.

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Authors and Affiliations

  1. Department of Radiology, Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran

    Ramezan Jafari, Houshyar Maghsoudi & Fatemeh Cheraghalipoor

  2. Gastroenterology and Liver Diseases Research Center,Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Sara Ashtari & Mohamad Amin Pourhoseingholi

  3. Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran

    Nematollah Jonaidi Jafari

  4. Behavioral Sciences Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran

    Hassan Saadat

  5. Anesthesia and Critical Care Department, Hamadan University of Medical Sciences, Hamadan, Iran

    Farshid Rahimi-Bashar

  6. Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran

    Amir Vahedian-Azimi

  7. Neurogenic Inflammation Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

    Amirhossein Sahebkar

  8. Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran

    Amirhossein Sahebkar

  9. Polish Mother’s Memorial Hospital Research Institute (PMMHRI), Lodz, Poland

    Amirhossein Sahebkar

  10. Halal Research Center of IRI, FDA, Tehran, Iran

    Amirhossein Sahebkar

Authors
  1. Ramezan Jafari
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  2. Sara Ashtari
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  3. Mohamad Amin Pourhoseingholi
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  4. Houshyar Maghsoudi
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  5. Fatemeh Cheraghalipoor
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  6. Nematollah Jonaidi Jafari
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  7. Hassan Saadat
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  8. Farshid Rahimi-Bashar
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  9. Amir Vahedian-Azimi
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  10. Amirhossein Sahebkar
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Corresponding authors

Correspondence to Amir Vahedian-Azimi or Amirhossein Sahebkar .

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Editors and Affiliations

  1. University of Campinas, São Paulo, Brazil

    Paul C. Guest

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Jafari, R. et al. (2021). Identification, Monitoring, and Prediction of Disease Severity in Patients with COVID-19 Pneumonia Based on Chest Computed Tomography Scans: A Retrospective Study. In: Guest, P.C. (eds) Clinical, Biological and Molecular Aspects of COVID-19. Advances in Experimental Medicine and Biology(), vol 1321. Springer, Cham. https://doi.org/10.1007/978-3-030-59261-5_24

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