tetano
Editor, Senior Moderator
Med Sci Monit
. 2021 Jun 3;27:e931285.
doi: 10.12659/MSM.931285.
Chest Computed Tomography (CT) as a Predictor of Clinical Course in Coronavirus Disease
Bartosz Mruk[SUP] 1 2 [/SUP], Jerzy Walecki[SUP] 1 2 [/SUP], Andrzej Górecki[SUP] 3 [/SUP], Agnieszka Kostkiewicz[SUP] 3 [/SUP], Katarzyna Sklinda[SUP] 1 2 [/SUP]
Affiliations
Abstract
BACKGROUND Chest imaging may be taken into consideration in detecting viral lung infections, especially if there are no tests available or there is a need for a prompt diagnosis. Imaging modalities enable evaluation of the character and extent of pulmonary lesions and monitoring of the disease course. The aim of this study was to verify the prognostic value of chest CT in COVID-19 patients. MATERIAL AND METHODS We conducted a retrospective review of clinical data and CT scans of 156 patients with SARS-CoV-2 infection confirmed by real-time reverse-transcription polymerase-chain-reaction (rRT-PCR) assay hospitalized in the Central Clinical Hospital of the Ministry of the Interior in Warsaw and in the Medical Centre in Łańcut, Poland. The total severity score (TSS) was used to quantify the extent of lung opacification in CT scans. RESULTS The dominant pattern in discharged patients was ground-glass opacities, whereas in the non-survivors, the dominant pulmonary changes were consolidations. The non-survivors were more likely to have pleural effusion, pleural thickening, lymphadenopathy, air bronchogram, and bronchiolectasis. There were no statistically significant differences among the 3 analyzed groups (non-survivors, discharged patients, and patients who underwent prolonged hospitalization) in the presence of fibrotic lesions, segmental or subsegmental pulmonary vessel enlargement, subpleural lines, air bubble sign, and halo sign. CONCLUSIONS Lung CT is a diagnostic tool with prognostic utility in COVID-19 patients. The correlation of the available clinical data with semi-quantitative radiological features enables evaluation of disease severity. The occurrence of specific radiomics shows a positive correlation with prognosis.
. 2021 Jun 3;27:e931285.
doi: 10.12659/MSM.931285.
Chest Computed Tomography (CT) as a Predictor of Clinical Course in Coronavirus Disease
Bartosz Mruk[SUP] 1 2 [/SUP], Jerzy Walecki[SUP] 1 2 [/SUP], Andrzej Górecki[SUP] 3 [/SUP], Agnieszka Kostkiewicz[SUP] 3 [/SUP], Katarzyna Sklinda[SUP] 1 2 [/SUP]
Affiliations
- PMID: 34149047
- DOI: 10.12659/MSM.931285
Abstract
BACKGROUND Chest imaging may be taken into consideration in detecting viral lung infections, especially if there are no tests available or there is a need for a prompt diagnosis. Imaging modalities enable evaluation of the character and extent of pulmonary lesions and monitoring of the disease course. The aim of this study was to verify the prognostic value of chest CT in COVID-19 patients. MATERIAL AND METHODS We conducted a retrospective review of clinical data and CT scans of 156 patients with SARS-CoV-2 infection confirmed by real-time reverse-transcription polymerase-chain-reaction (rRT-PCR) assay hospitalized in the Central Clinical Hospital of the Ministry of the Interior in Warsaw and in the Medical Centre in Łańcut, Poland. The total severity score (TSS) was used to quantify the extent of lung opacification in CT scans. RESULTS The dominant pattern in discharged patients was ground-glass opacities, whereas in the non-survivors, the dominant pulmonary changes were consolidations. The non-survivors were more likely to have pleural effusion, pleural thickening, lymphadenopathy, air bronchogram, and bronchiolectasis. There were no statistically significant differences among the 3 analyzed groups (non-survivors, discharged patients, and patients who underwent prolonged hospitalization) in the presence of fibrotic lesions, segmental or subsegmental pulmonary vessel enlargement, subpleural lines, air bubble sign, and halo sign. CONCLUSIONS Lung CT is a diagnostic tool with prognostic utility in COVID-19 patients. The correlation of the available clinical data with semi-quantitative radiological features enables evaluation of disease severity. The occurrence of specific radiomics shows a positive correlation with prognosis.