Covid 19 İçin Buzlu Cam Opasitesi ve Konsolidasyon Belirtileri Prevalansinin Meta Analizi

Yıl 2022, Cilt: 44 Sayı: 3, 442 - 453, 14.03.2022

Deniz Tezer , Berfu Parçalı , Murat Şahin , Fezan Mutlu

https://doi.org/10.20515/otd.956897

Öz

Bu çalışmada, bilgisayarlı göğüs tomografisinde en yaygın görüntüleme bulguları olan buzlu cam opasitesi (GGO) ve konsolidasyon sonuçları incelenerek daha kesin Covid-19 tespitini sağlamak için yayınlanan çalışmalardan elde edilen sonuçlar kullanılarak meta analiz yönteminin uygulanması amaçlanmıştır. Çalışmaya gerçek zamanlı polimeraz zincir reaksiyonu (rRT-PCR) pozitif vakaların görüntü özelliklerini bildiren ve SARS-Cov-2 enfeksiyonunu doğrulayan yayınlanmış hakemli makaleler dahil edilmiştir. Bu makalelerin çalışma türü vaka serisi, geriye dönük veya ileriye dönük kohort şeklindedir. Araştırma kapsamındaki çalışmalara, Covid-19, şiddetli akut solunum yolu sendromu corona virüsü 2 (SARS-Cov-2), bilgisayarlı göğüs tomografisi, konsolidasyon ve GGO anahtar kelimelerinin radyografik araştırma veri tabanı Secure Australia (RNSA), The Science Direct ve National Library of Medicine'de araştırılması ile ulaşılmıştır. Arama terimleri sonucunda üç veri tabanından toplam 310 makale toplandı ve makaleler tarandı. Buzlu cam opasitesi ve konsolidasyon bilgilerinin olmaması nedeniyle 250 makale çıkarıldı. Geriye kalan makalelerden, çalışma türü nedeniyle 24 makale, gün kriterini sağlamaması nedeniyle 7 makale, eksik ve yanlış veriler nedeniyle 9 makale çıkarıldı. Sonuçta 20 makale meta-analiz çalışmamıza dahil edildi. Bilgisayarlı göğüs tomografisi pozitif olan bulgularda, buzlu cam opasitesinin 5 güne kadar mevcut olduğu, beşinci ve sonraki günlerde konsolidasyona dönüştüğü görülmüştür. Analiz sonuçlarına göre; Covid-19'un erken evresi için buzlu cam opasitesinin prevalansı %82 ve konsolidasyonun prevalansı %40’tır.

Anahtar Kelimeler

Meta analiz, Covid-19, rRT-PCR test, bilgisayarlı göğüs tomografisi

The Prevalence of Ground-Glass Opacity and Consolidation Symptoms of Covid-19 By Meta-Analysis

Öz

In this study, it was aimed to apply the meta-analysis method of the results obtained from the published studies to provide a more precise Covid-19 detection by examining the results of ground glass opacity (GGO) and consolidation, which are the most common imaging findings in chest computed tomography (CT). Published peer-reviewed articles reporting the image characteristics of real-time reverse transcription–polymerase chain reaction (rRT-PCR) positive cases and confirming Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) infection were included in the study. The study type of articles were case series, retrospective or prospective cohort studies. The studies under the scope of the research were reached from the National Library of Medicine, the research network for a Secure Australia (RNSA) and The Science Direct databases by searching the keywords Covid-19, SARS-Cov-2, computed chest tomography, Consolidation and GGO. As a result of the search terms, in total 310 articles were collected from three databases and articles were scanned, 250 articles were removed due to lack of GGO and Consolidation information, 24 studies were eliminated due to study type, 7 studies were unsuitable for day criteria, and 9 studies were eliminated due to missing and incorrect data. After all, 20 studies were included in our meta-analysis study. In the positive CT findings, it is known that the GGO is present for up to 5 days, the GGO turns into consolidation on the fifth and the following days, and according to the analysis result; for the early stage of Covid-19, the GGO Prevalence is 82% and Consolidation Prevalence is 40%.

Anahtar Kelimeler

Meta-analysis, Covid-19, rRT-PCR test, chest computed tomography

Kaynakça

• 1. Borenstein, M., et al., Introduction to meta-analysis. 2011: John Wiley & Sons
• 2. Webster's Dictionary. meta-analysis. 2020, April 15; Available from: https://www.merriamwebster.com/dictionary/meta-analysis
• 3. Viechtbauer, W., Conducting meta-analyses in R with the metafor package. Journal of statistical software, 2010. 36(3): p. 1-48.
• 4. Barendregt, J.J., et al., Meta-analysis of prevalence. J Epidemiol Community Health, 2013. 67(11): p. 974-978.
• 5. Schwarzer, G., et al., Seriously misleading results using inverse of Freeman‐Tukey double arcsine transformation in meta‐analysis of single proportions. Research synthesis methods, 2019. 10(3): p. 476-483.
• 6. Wang, K.-S. and X. Liu, Statistical methods in the meta-analysis of prevalence of human diseases. Journal of Biostatistics and Epidemiology, 2016. 2(1): p. 20-24.
• 7. He, Y., Translation: Diagnosis and treatment protocol for novel coronavirus pneumonia (trial version 7): National Health Commission, National Administration of Traditional Chinese Medicine. Infectious Microbes & Diseases, 2020.
• 8. Koo, H.J., et al., Radiographic and CT features of viral pneumonia. Radiographics, 2018. 38(3): p. 719-739
• 9. Ai, T., 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): p. E32-E40.
• 10. Kim, H., H. Hong, and S.H. Yoon, Diagnostic performance of CT and reverse transcriptase polymerase chain reaction for coronavirus disease 2019: a meta-analysis. Radiology, 2020. 296(3): p. E145-E155.
• 11. Adair II, L.B. and E.J. Ledermann, Chest CT findings of early and progressive phase COVID-19 infection from a US patient. Radiology case reports, 2020. 15(7): p. 819-824.
• 12. Lomoro, P., et al., COVID-19 pneumonia manifestations at the admission on chest ultrasound, radiographs, and CT: single-center study and comprehensive radiologic literature review. European journal of radiology open, 2020. 7: p. 100231.
• 13. Moher, D., et al., Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine, 2009. 6(7): p. e1000097.
• 14. Deeks, J.J., et al., Analysing data and undertaking meta‐analyses. Cochrane handbook for systematic reviews of interventions, 2019: p. 241-284.
• 15. Caruso, D., et al., Chest CT features of COVID-19 in Rome, Italy. Radiology, 2020. 296(2): p. E79-E85.
• 16. Himoto, Y., et al., Diagnostic performance of chest CT to differentiate COVID-19 pneumonia in non-high-epidemic area in Japan. Japanese journal of radiology, 2020. 38(5): p. 400-406.
• 17. Dashraath, P., et al., Coronavirus disease 2019 (COVID-19) pandemic and pregnancy. American journal of obstetrics and gynecology, 2020. 222(6): p. 521-531.
• 18. Bai, H.X., et al., Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT. Radiology, 2020. 296(2): p. E46-E54.
• 19. Chung, M., et al., CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology, 2020. 295(1): p. 202-207.
• 20. Wang, Y., et al., Temporal changes of CT findings in 90 patients with COVID-19 pneumonia: a longitudinal study. Radiology, 2020. 296(2): p. E55-E64.
• 21. Bernheim, A., et al., Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology, 2020: p. 200463.
• 22. Wang, K., et al., Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area. Clin Radiol, 2020. 75(5): p. 341-347.
• 23. Huang, L., et al., Rapid asymptomatic transmission of COVID-19 during the incubation period demonstrating strong infectivity in a cluster of youngsters aged 16-23 years outside Wuhan and characteristics of young patients with COVID-19: A prospective contact-tracing study. Journal of Infection, 2020. 80(6): p. e1-e13.
• 24. Luo, Z., et al., Association between chest CT features and clinical course of Coronavirus Disease 2019. Respiratory medicine, 2020. 168: p. 105989.
• 25. Fang, X., et al., Low-dose corticosteroid therapy does not delay viral clearance in patients with COVID-19. Journal of Infection, 2020. 81(1): p. 147-178.
• 26. Peng, S., et al., Clinical course of coronavirus disease 2019 in 11 patients after thoracic surgery and challenges in diagnosis. The Journal of thoracic and cardiovascular surgery, 2020. 160(2): p. 585-592. e2.
• 27. Guan, C.S., et al., Imaging features of coronavirus disease 2019 (COVID-19): evaluation on thin-section CT. Academic radiology, 2020. 27(5): p. 609-613.
• 28. Xu, X., et al., Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2. European journal of nuclear medicine and molecular imaging, 2020. 47(5): p. 1275-1280.
• 29. Shi, H., et al., Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. The Lancet infectious diseases, 2020. 20(4): p. 425-434.
• 30. Ding, X., et al., Chest CT findings of COVID-19 pneumonia by duration of symptoms. European journal of radiology, 2020. 127: p. 109009.
• 31. Li, X., et al., CT imaging changes of corona virus disease 2019 (COVID-19): a multi-center study in Southwest China. Journal of translational medicine, 2020. 18: p. 1-8.
• 32. Wang, X., et al., Clinical characteristics of non-critically ill patients with novel coronavirus infection (COVID-19) in a Fangcang Hospital. Clinical Microbiology and Infection, 2020. 26(8): p. 1063-1068.
• 33. Zhou, Z., et al., Coronavirus disease 2019: initial chest CT findings. European radiology, 2020: p. 1-9.
• 34. Nie, W., et al., First CT characteristic appearance of patients with coronavirus disease 2019. Zhong nan da xue xue bao. Yi xue ban= Journal of Central South University. Medical sciences, 2020. 45(3).
• 35. Liu, Z., et al., Association between initial chest CT or clinical features and clinical course in patients with coronavirus disease 2019 pneumonia. Korean journal of radiology, 2020. 21(6): p. 736.