Published June 21, 2021 | Version v1
Journal article Open

Catching the First Wave in the Pandemic: A Retrospective Evaluation of Chest CT Images for COVID-19

Creators

  • 1. Department of Radiology, Mehmet Akif Inan Education and Research Hospital, University of Health Sciences, Şanlıurfa, Turkey.
  • 2. Department of General Surgery, Ankara City Hospital, Ankara, Turkey.
  • 3. Department of Radiology, 19 Mayıs Hospital, Ankara, Turkey.
  • 4. Department of Medical Microbiology, Gulhane Training and Research Hospital, University of Health Sciences, Ankara, Turkey.

Description

Abstract

Pandemics generally begin in small areas and subsequently spread widely. In order to detect an outbreak in its early stage, observation of the data for small regions is important. The aim of this study to investigate the presence of COVID-19-like pneumonia findings in computed tomography (CT) taken before the COVID-19 pandemic in Turkey. The chest CTs performed in our hospital between December 1, 2019 and March 10, 2020 (study group) and those performed between December 1, 2018 and March 10, 2019 (control group) were retrospectively analyzed. A total of 1,432 chest CT images in the study group and 1,443 chest CT images in the control group were evaluated for COVID-19. The lesion characteristics on CT, length of hospital stay (LOS), and mortality rate were investigated. Typical lung involvement defined for COVID-19 was present in 1.39% (n=20/1,432) of CTs in the study group and 0.49% (n=7/1,443) in the control group (p=0.011). Seventy-five percent (n=15/20) of the study group were male, and the mean age of the patients was 51.8 (±17.1) years. All the patients in the study group had at least one of the symptoms of COVID-19, such as fever, cough, and respiratory distress. Ninety percent (n=18/20) of the patients in the study group had ground-glass opacities that showed a predominantly peripheral distribution. Five of these had accompanying consolidation and one had a reverse halo sign. According to clinical records, in-hospital mortality developed in seven of 20 patients (35%), the LOS was 5.5±6.2 days, and the median time from the symptom onset to admission was 4 (range: 1-12) days. Our study reveals that the onset of COVID-19 or a similar disease is more likely to occur earlier than first reported in the country.

Özet

Pandemiler genellikle küçük alanlarda başlar ve daha sonra geniş bir alana yayılırlar. Bir salgını erken aşamada tespit etmek için küçük bölgelerdeki verilerin gözlemlenmesi önemlidir. Bu çalışmanın amacı Türkiye'de COVID-19 pandemisinden önce çekilen bilgisayarlı tomografi (BT) görüntülerinde COVID-19 benzeri pnömoni bulgularının varlığını araştırmaktır. Hastanemizde 1 Aralık 2019-10 Mart 2020 (çalışma grubu) ve 1 Aralık 2018-10 Mart 2019 (kontrol grubu) tarihleri arasında yapılan göğüs BT'leri geriye dönük olarak incelendi. Çalışma grubundaki toplam 1.432 göğüs BT görüntüsü ve kontrol grubundaki 1.443 göğüs BT görüntüsü COVID-19 için değerlendirildi. BT'de lezyon özellikleri, hastanede yatış süresi (HYS) ve mortalite oranı araştırıldı. COVID-19 için tanımlanan tipik akciğer tutulumu, çalışma grubundaki BT'lerin %1.39'unda (n=20/1,432) ve kontrol grubunun %0.49'unda (n=7/1,443) mevcuttu (p=0.011). Çalışma grubunun yüzde yetmiş beşi (n=15/20) erkek olup, hastaların yaş ortalaması 51.8’dir (±17,1). Çalışma grubundaki tüm hastalarda ateş, öksürük ve solunum sıkıntısı gibi COVID-19 semptomlarından en az biri vardı. Çalışma grubundaki hastaların yüzde doksanı (n=18/20) ağırlıklı olarak periferik dağılım gösteren buzlu cam opasitelerine sahipti. Bunlardan beşinde bu bulgulara konsolidasyon eşlik ederken ve birinde ters hale işareti vardı. Klinik kayıtlara göre 20 hastanın yedisinde (%35) hastane içi mortalite gelişti, HYS 5.5±6.2 gün ve semptom başlangıcından başvuruya kadar geçen medyan süre 4 (aralık: 1-12) gündü. Çalışmamız, COVID-19 veya benzeri bir hastalığın başlangıcının ülkemizde ilk bildirilenden daha erken ortaya çıkma olasılığının yüksek olduğunu ortaya koymaktadır.

Notes

Pandemide İlk Dalgayı Yakalamak: COVID-19 için Göğüs BT Görüntülerinin Retrospektif Değerlendirilmesi

Files

jmvi.2021.33.pdf

Files (558.4 kB)

Name Size Download all
md5:0e9ccca7ff67e73b03a90e0b98a7e8cd
558.4 kB Preview Download

Additional details

References

  • 1. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395(10223): 497-506.
  • 2. Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet 2020; 395(10223): 470-3.
  • 3. Coronaviridae Study Group of the International Committee on Taxonomy of Viruses. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020; 5(4): 536-44.
  • 4. Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. AJR Am J Roentgenol 2020; 215(1): 87-93.
  • 5. Şahin M. Impact of weather on COVID-19 pandemic in Turkey. Sci Total Environ 2020; 728: 138810.
  • 6. Oztoprak F, Javed A. Case Fatality Rate estimation of COVID-19 for European Countries: Turkey's Current Scenario Amidst a Global Pandemic; Comparison of Outbreaks with European Countries. EJMO 2020; 4(2): 149-59.
  • 7. Reuters, London, United Kingdom. Turkey's coronavirus cases overtake Iran, highest in Middle East. Available at: https://www.reuters.com/article/us-health-coronavirus-turkey-idUSKBN2200T4 [Accessed April 18, 2020].
  • 8. Foreign Policy, Washington, USA. Turkey's Coronavirus Crisis Grows as Infections Exceed China's. Available at: https://foreignpolicy.com/2020/04/20/turkeys-coronavirus-crisis-grows-as-infections-exceed-chinas/ [Accessed April 20, 2020].
  • 9. Alpdagtas S, Ilhan E, Uysal E, Sengor M, Ustundag CB, Gunduz O. Evaluation of current diagnostic methods for COVID-19. APL Bioeng 2020; 4(4): 041506.
  • 10. Long QX, Tang XJ, Shi QL, Li Q, Deng HJ, Yuan J, et al. Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections. Nat Med 2020; 26(8): 1200-4.
  • 11. Gietema HA, Zelis N, Nobel JM, Lambriks LJG, van Alphen LB, Oude Lashof AML, et al. CT in relation to RT-PCR in diagnosing COVID-19 in The Netherlands: A prospective study. PLoS One 2020; 15(7): e0235844.
  • 12. Zuo H. Contribution of CT Features in the Diagnosis of COVID-19. Can Respir J 2020; 2020: 1237418.
  • 13. Tahtabaşı M. COVID-19 Pneumonia: Experiences Regarding the Use of Computed Tomography in Diagnosis and Follow-up. J Mol Virol Immunol 2020; 1(2): 51-3.
  • 14. Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, et al. Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT. Radiology 2020; 296(2): E46-E54.
  • 15. Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M, et al. Radiological Society of North America Expert Consensus Document on Reporting Chest CT Findings Related to COVID-19: Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA. Radiol Cardiothorac Imaging 2020; 2(2): e200152.
  • 16. Bulut C, Kato Y. Epidemiology of COVID-19. Turk J Med Sci 2020; 50(SI-1): 563-70.
  • 17. Ferraris A, Calandri M, Fonio P. Retrospective evaluation of CT findings to shape epidemiological models of COVID-19 outbreak. Eur Radiol 2020.
  • 18. Sümbül HE, Şahiner F. Rapid Spreading of SARS-CoV-2 Infection and Risk Factors: Epidemiological, Immunological and Virological Aspects. J Mol Virol Immunol 2020; 1(2): 36-50.
  • 19. Christaki E. New technologies in predicting, preventing and controlling emerging infectious diseases. Virulence 2015; 6(6): 558-65.
  • 20. Syeda HB, Syed M, Sexton KW, Syed S, Begum S, Syed F, et al. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Med Inform 2021; 9(1): e23811.
  • 21. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology 2020; 296(2): E32-E40.
  • 22. Amorim VB, Rodrigues RS, Barreto MM, Zanetti G, Hochhegger B, Marchiori E. Influenza A (H1N1) pneumonia: HRCT findings. J Bras Pneumol 2013; 39(3): 323-9.
  • 23. Altmayer S, Zanon M, Pacini GS, Watte G, Barros MC, Mohammed TL, et al. Comparison of the computed tomography findings in COVID-19 and other viral pneumonia in immunocompetent adults: a systematic review and meta-analysis. Eur Radiol 2020; 30(12): 6485-96.
  • 24. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA 2020; 323(13): 1239-42.
  • 25. Kooraki S, Hosseiny M, Myers L, Gholamrezanezhad A. Coronavirus (COVID-19) Outbreak: What the Department of Radiology Should Know. J Am Coll Radiol 2020; 17(4): 447-51.
  • 26. American College of Radiology, Virginia, USA. ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection. Available at: https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection [Accessed March 11, 2020].
  • 27. Khalili M, Karamouzian M, Nasiri N, Javadi S, Mirzazadeh A, Sharifi H. Epidemiological characteristics of COVID-19: a systematic review and meta-analysis. Epidemiol Infect 2020; 148: e130.
  • 28. Lessler J, Reich NG, Brookmeyer R, Perl TM, Nelson KE, Cummings DA. Incubation periods of acute respiratory viral infections: a systematic review. Lancet Infect Dis 2009; 9(5): 291-300.