Author | Carvalho, Alysson Roncally Silva | |
Author | Guimarães, Alan | |
Author | Garcia, Thiego de Souza Oliveira | |
Author | Werberich, Gabriel Madeira | |
Author | Ceotto, Victor Fraga | |
Author | Bozza, Fernando Augusto | |
Author | Rodrigues, Rosana Souza | |
Author | Pinto, Joana Sofia F. | |
Author | Schmitt, Willian Rebouças | |
Author | Zin, Walter Araujo | |
Author | França, Manuela | |
Access date | 2021-12-28T13:04:27Z | |
Available date | 2021-12-28T13:04:27Z | |
Document date | 2021 | |
Citation | CARVALHO, Alysson Roncally Silva et al. Estimating COVID-19 Pneumonia Extent and Severity From Chest Computed Tomography. Frontiers in physiology, v. 12, p. 1-12, 2021 | pt_BR |
ISSN | 1664-042X | pt_BR |
URI | https://www.arca.fiocruz.br/handle/icict/50534 | |
Language | eng | pt_BR |
Publisher | Frontiers Media | pt_BR |
Rights | open access | pt_BR |
Title | Estimating COVID-19 Pneumonia Extent and Severity From Chest Computed Tomography | pt_BR |
Type | Article | pt_BR |
DOI | 10.3389/fphys.2021.617657 | |
Abstract | Background: COVID-19 pneumonia extension is assessed by computed tomography (CT) with the ratio between the volume of abnormal pulmonary opacities (PO) and CT-estimated lung volume (CTLV). CT-estimated lung weight (CTLW) also correlates with pneumonia severity. However, both CTLV and CTLW depend on demographic and anthropometric variables.
Purposes: To estimate the extent and severity of COVID-19 pneumonia adjusting the volume and weight of abnormal PO to the predicted CTLV (pCTLV) and CTLW (pCTLW), respectively, and to evaluate their possible association with clinical and radiological outcomes.
Methods: Chest CT from 103 COVID-19 and 86 healthy subjects were examined retrospectively. In controls, predictive equations for estimating pCTLV and pCTLW were assessed. COVID-19 pneumonia extent and severity were then defined as the ratio between the volume and the weight of abnormal PO expressed as a percentage of the pCTLV and pCTLW, respectively. A ROC analysis was used to test differential diagnosis ability of the proposed method in COVID-19 and controls. The degree of pneumonia extent and severity was assessed with Z-scores relative to the average volume and weight of PO in controls. Accordingly, COVID-19 patients were classified as with limited, moderate and diffuse pneumonia extent and as with mild, moderate and severe pneumonia severity.
Results: In controls, CTLV could be predicted by sex and height (adjusted R 2 = 0.57; P < 0.001) while CTLW by age, sex, and height (adjusted R 2 = 0.6; P < 0.001). The cutoff of 20% (AUC = 0.91, 95%CI 0.88-0.93) for pneumonia extent and of 50% (AUC = 0.91, 95%CI 0.89-0.92) for pneumonia severity were obtained. Pneumonia extent were better correlated when expressed as a percentage of the pCTLV and pCTLW (r = 0.85, P < 0.001), respectively. COVID-19 patients with diffuse and severe pneumonia at admission presented significantly higher CRP concentration, intra-hospital mortality, ICU stay and ventilatory support necessity, than those with moderate and limited/mild pneumonia. Moreover, pneumonia severity, but not extent, was positively and moderately correlated with age (r = 0.46) and CRP concentration (r = 0.44).
Conclusion: The proposed estimation of COVID-19 pneumonia extent and severity might be useful for clinical and radiological patient stratification. | pt_BR |
Affilliation | University of Porto. Faculty of Medicine. Department of Surgery and Physiology. Cardiovascular R&D Centre. Porto, Portugal / Universidade Federal do Rio de Janeiro. Alberto Luiz Coimbra Institute of Post-Graduation and Research in Engineering. Biomedical Engineering Program. Laboratory of Pulmonary Engineering. Rio de Janeiro, RJ, Brazil / Universidade Federal do Rio de Janeiro. Carlos Chagas Filho Institute of Biophysics. Laboratory of Respiration Physiology. Rio de Janeiro, RJ, Brazil. | pt_BR |
Affilliation | Universidade Federal do Rio de Janeiro. Alberto Luiz Coimbra Institute of Post-Graduation and Research in Engineering. Biomedical Engineering Program. Laboratory of Pulmonary Engineering. Rio de Janeiro, RJ, Brazil. | pt_BR |
Affilliation | Universidade Estácio de Sá. Medical Faculty. Rio de Janeiro, RJ, Brazil. | pt_BR |
Affilliation | Universidade Federal do Rio de Janeiro. Department of Radiology. Rio de Janeiro, RJ, Brazil. | pt_BR |
Affilliation | Hospital Niteroi D'Or. Niterói, RJ, Brazil. | pt_BR |
Affilliation | Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Instituto D'Or de Pesquisa e Ensino, Rio de Janeiro, RJ, Brasil. | pt_BR |
Affilliation | Universidade Federal do Rio de Janeiro. Department of Radiology. Rio de Janeiro, RJ, Brazil / Instituto D'Or de Pesquisa e Ensino, Rio de Janeiro, RJ, Brasil. | pt_BR |
Affilliation | Complexo Hospitalar Universitário do Porto. Radiology Department. Porto, Portugal. | pt_BR |
Affilliation | Complexo Hospitalar Universitário do Porto. Radiology Department. Porto, Portugal. | pt_BR |
Affilliation | Universidade Federal do Rio de Janeiro. Carlos Chagas Filho Institute of Biophysics. Laboratory of Respiration Physiology. Rio de Janeiro, RJ, Brazil. | pt_BR |
Affilliation | Complexo Hospitalar Universitário do Porto. Radiology Department. Porto, Portugal / Porto University. Instituto de Ciências Biomeìdicas Abel Salazar. Porto, Portugal. | pt_BR |
Subject | COVID-19 | pt_BR |
Subject | CT-estimated lung volume | pt_BR |
Subject | CT-estimated lung weight | pt_BR |
Subject | Computed tomography | pt_BR |
Subject | Deep learning | pt_BR |