Evaluation of COVID-19 Findings on Thoracic CT: Is There any Correlation with Age and Comorbidity?

Yıl 2025, Cilt: 7 Sayı: 1, 251 - 257, 15.01.2025

Nevin Köremezli Keskin , Merve Başdemirci , Onur Başdemirci , Aziz Ahmad Hamidi

https://doi.org/10.37990/medr.1554665

Öz

Aim: To share insights gained from low-dose thoracic computed tomography (CT) findings of patients diagnosed with COVID-19. Additionally, we aimed to evaluate the correlation between the observed CT findings, CT severity index, patient age, and the presence of comorbid conditions.
Material and Method: One hundred patients having a COVID-19 diagnosis were included in the study's sample. We meticulously reviewed the thoracic CT image characteristics, the lung severity index, and various clinical data related to the patients. The relationships between these factors were then analyzed to draw meaningful conclusions.
Results: The study included 100 patients, consisting of 67 men and 33 women. Among these patients, 30 had at least one underlying comorbid condition, with hypertension being the most prevalent. CT scans were positive in 65 patients. Within this group, 60 patients (93.3%) exhibited ground-glass opacities (GGO), 27 patients (41.5%) showed signs of consolidation, 22 patients (33.8%) had both GGO and consolidation, and 8 patients (12.3%) displayed the crazy paving pattern. The total lung severity score (TLSS) ranged from 0 to 19, with mean score of 3.11±3.71. Each lung lobe was systematically evaluated for the extent of involvement. We found a statistically significant relationship indicating that increasing age correlated with higher grades of lung involvement. Furthermore, significant association was noted between presence of comorbidities and the lung involvement grades. Our analysis revealed a moderate positive correlation between CT lung involvement grade and patient age, alongside a weaker positive correlation between the lung involvement grade and comorbid conditions.
Conclusion: CT imaging has proven to be important tool in managing patients suspected of or confirmed to have COVID-19. Notably, the CT lung severity grade was significantly elevated in patients over 65 years and those with comorbidities. These findings underscore the critical role that CT plays in evaluating and managing the severity of COVID-19 pneumonia.

Anahtar Kelimeler

COVID-19, comorbidity, computed tomography, computed tomography lung severity grade

Kaynakça

• Schoeman D, Fielding BC. Coronavirus envelope protein: current knowledge. Virol J. 2019;16:69.
• Garcia M, Lipskiy N, Tyson J, et al. Centers for Disease Control and Prevention 2019 novel coronavirus disease (COVID-19) information management: addressing national health-care and public health needs for standardized data definitions and codified vocabulary for data exchange. J Am Med Inform Assoc. 2020;27:1476-87.
• Wang W, Tang J, Wei F. Updated understanding of the outbreak of 2019 novel coronavirus (2019‐nCoV) in Wuhan, China. J Med Virol. 2020;92:441-7.
• Pan F, Ye T, Sun P, et al. Time course of lung changes at chest CT during recovery from Coronavirus Disease 2019 (COVID-19). Radiology. 2020;295:715-21.
• Bernheim A, Mei X, Huang M, et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): relationship to duration of infection. Radiology. 2020;295:200463.
• Ng MY, Lee EYP, Yang J, et al. Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol Cardiothorac Imaging. 2020;2:e200034.
• Bai HX, Hsieh B, Xiong Z, et al. Performance of radiologists in differentiating COVID-19 from Non-COVID-19 viral pneumonia at chest CT. Radiology. 2020;296:E46-54.
• Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet. 2020;395:507-13.
• Cao M, Zhang D, Wang Y, et al. Clinical features of patients infected with the 2019 novel Coronavirus (COVID-19) in Shanghai, China. 2020 March 6. doi: 10.1101/2020.03.04.20030395. [Epub ahead of print].
• Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, China, of Novel Coronavirus–infected pneumonia. N Engl J Med. 2020;382:1199-207.
• Guan W jie, Ni Z yi, Hu Y, et al. Clinical characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382:1708-20.
• Caruso D, Zerunian M, Polici M, et al. Chest CT features of COVID-19 in Rome, Italy. Radiology. 2020;296:E79-85.
• Pijls BG, Jolani S, Atherley A, et al. Demographic risk factors for COVID-19 infection, severity, ICU admission and death: a meta-analysis of 59 studies. BMJ Open. 2021;11:e044640.
• Singh MK, Mobeen A, Chandra A, et al. A meta-analysis of comorbidities in COVID-19: which diseases increase the susceptibility of SARS-CoV-2 infection?. Comput Biol Med. 2021;130:104219.
• Wang D, Hu B, Hu C, et al. Clinical Characteristics of 138 hospitalized patients with 2019 novel Coronavirus–infected pneumonia in Wuhan, China. JAMA. 2020;323:1061. Erratum in: JAMA. 2021;325:1113.
• Wang L, Li X, Chen H, et al. Coronavirus Disease 19 infection does not result in acute kidney injury: an analysis of 116 hospitalized patients from Wuhan, China. Am J Nephrol. 2020;51:343-8.
• Liang W, Guan W, Chen R, et al. Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. Lancet Oncol. 2020;21:335-7.
• Guan W jie, Liang W hua, Zhao Y, et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J. 2020;55:2000547.
• 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;215:87-93.
• Chung M, Bernheim A, Mei X, et al. CT imaging features of 2019 novel Coronavirus (2019-nCoV). Radiology. 2020;295:202-7.
• Simpson S, Kay FU, Abbara S, 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. Radiology: Cardiothoracic Imaging. 2020;2:e200152.
• Zhou X, Pu Y, Zhang D, et al. CT findings and dynamic imaging changes of COVID-19 in 2908 patients: a systematic review and meta-analysis. Acta Radiol. 2022;63:291-310.
• Flook M, Jackson C, Vasileiou E, et al. Informing the public health response to COVID-19: a systematic review of risk factors for disease, severity, and mortality. BMC Infect Dis. 2021;21:342.
• Davies NG, Klepac P, Liu Y, et al. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nat Med. 2020;26:1205-11.
• Ayoub HH, Chemaitelly H, Mumtaz GR, et al. Characterizing key attributes of COVID-19 transmission dynamics in China’s original outbreak: model-based estimations. Glob Epidemiol. 2020;2:100042.
• Zhang J jin, Dong X, Liu G hui, Gao Y dong. Risk and protective factors for COVID-19 morbidity, severity, and mortality. Clinic Rev Allerg Immunol. 2022;64:90-107.
• Shaw AC, Joshi S, Greenwood H, et al. Aging of the innate immune system. Curr Opin Immunol. 2010;22:507-13.
• Gao Y, Ding M, Dong X, et al. Risk factors for severe and critically ill COVID‐19 patients: a review. Allergy. 2021;76:428-55.
• Wang B, Li R, Lu Z, Huang Y. Does comorbidity increase the risk of patients with COVID-19: evidence from meta-analysis. Aging. 2020;12:6049-57.
• Bhandari S, Rankawat G, Bagarhatta M, et al. Clinico-radiological evaluation and correlation of CT chest images with progress of disease in COVID-19 patients. J Assoc Physicians India. 2020;68:34-42.