The role of CT in prognosis prediction in COVID-19 patients

Tonje Gravdal; Kine Storås; Albertina Rusandu; Ragna Stalsberg

doi:10.7577/radopen.6092

Forfattere

Tonje Gravdal Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU) https://orcid.org/0009-0009-8592-6139
Kine Storås Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU) https://orcid.org/0009-0005-9322-1957
Albertina Rusandu Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU) https://orcid.org/0000-0001-9784-6157
Ragna Stalsberg Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU) https://orcid.org/0000-0002-9611-1615

DOI:

https://doi.org/10.7577/radopen.6092

Emneord (Nøkkelord):

COVID-19, Prognosis prediction, Computed Tomography, Literature review

Sammendrag

Introduction: The aim of the present study was to summarize and evaluate previously published scientific studies examining whether computed tomography (CT) of the thorax can predict the COVID-19 prognosis. The purpose is to clarify whether CT can predict the COVID-19 prognosis, and also if a CT examination can predict whether the patient will be admitted to the intensive care unit (ICU) or not.

Method: Traditional digital literature searches were performed in the Medline, Pubmed and Embase databases. Subsequent back- and forward citation-based searches were then conducted. A total of 17 studies were included according to preset inclusion and exclusion criteria.

Results: All 17 included articles were retrospective studies. The mean number of patients included was 219 (range: 28-901). Overall, the studies showed that CT-findings of abnormalities in the lung tissue may provide a possible COVID-19 prognosis determination. A total of 11 studies used a quantitative scoring system to evaluate the lung images. Based on the percentage of lung involvement, the ICU patients had a higher score compared with patients not admitted to the ICU. The pathology type with the highest predictive value was crazy paving pattern followed by vascular enlargement and air bronchogram. Pleural effusion and pleural thickening can help estimating the prognosis according to some of the studies.

Conclusion: The present study shows that CT can contribute to early diagnosis and predict the prognosis when using scoring systems or qualitative assessment of certain radiologic features which are more prevalent in critically ill COVID-19 patients.

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