CT imaging findings in lung transplant recipients with COVID-19

Bruno Hochhegger; Andres Pelaez; Tiago Machuca; Tan-Lucien Mohammed; Pratik Patel; Matheus Zanon; Felipe Torres; Stephan Altmayer; Douglas Zaione Nascimento

doi:10.1007/s00330-022-09148-w

CT imaging findings in lung transplant recipients with COVID-19

Eur Radiol. 2023 Mar;33(3):2089-2095. doi: 10.1007/s00330-022-09148-w. Epub 2022 Sep 24.

Authors

Bruno Hochhegger¹, Andres Pelaez², Tiago Machuca³, Tan-Lucien Mohammed⁴, Pratik Patel⁴, Matheus Zanon⁵, Felipe Torres⁶, Stephan Altmayer⁵, Douglas Zaione Nascimento⁷

Affiliations

¹ Department of Radiology, University of Florida, Gainesville, FL, USA. bhochhegger@ufl.edu.
² Department of Medicine, University of Florida, Gainesville, FL, USA.
³ Department of Surgery, University of Florida, Gainesville, FL, USA.
⁴ Department of Radiology, University of Florida, Gainesville, FL, USA.
⁵ Department of Radiology, Pontificia Universidade Catolica do Rio Grande do Sul, Porto Alegre, Brazil.
⁶ Department of Radiology, University of Toronto, Toronto, Canada.
⁷ Department of Lung Transplantation, Santa Casa de Misericordia de Porto Alegre, Porto Alegre, Brazil.

Abstract

Objectives: Our goal was to compare the chest computed tomography (CT) imaging findings of COVID-19 in lung transplant recipients (LTR) and a group of non-transplanted controls (NTC).

Methods: This retrospective study included 51 consecutive LTR hospitalized with COVID-19 from two centers. A total of 75 NTC were included for comparison. Images were classified regarding the standardized RSNA category, main pattern of lung attenuation, and longitudinal and axial distribution. Quantitative CT (QCT) analysis was performed to evaluate percentage of high attenuation areas (%HAA, threshold -250 to -700 HU). CT scoring was used to measure severity of parenchymal abnormalities.

Results: The imaging findings of COVID-19 in LTR were significantly different from controls regarding the RSNA classification and pattern of lung attenuation. LTR had a significantly higher proportion of patients with an indeterminate pattern on CT (0.31 vs. 0.11, p = 0.014). The most frequent pattern of attenuation in LTR was predominantly consolidation (0.39 vs. 0.22, p = 0.144) followed by a mixed pattern of ground-glass opacities (GGO) and consolidation (0.37 vs. 0.20, adjusted p = 0.102). On the other hand, the most common pattern in NTC was GGO predominant (0.58 vs. 0.24 of LTR, p = 0.001). LTR had significantly more severe parenchymal disease measured by CT score and %HAA by QCT (0.372 ± 0.08 vs. 0.148 ± 0.06, p < 0.001).

Conclusion: The most frequent finding of COVID-19 in LTR is a predominant pattern of consolidation. Compared to NTC, LTR more frequently demonstrated an indeterminate pattern according to the RSNA classification and more extensive lung abnormalities on QCT and semi-quantitative scoring.

Key points: • The most common CT finding of COVID-19 in LTR is a predominant pattern of consolidation followed by a mixed pattern of GGO and consolidation, while controls more often have a predominant pattern of GGO. • LTR more often presents with an indeterminate pattern of COVID-19 by RSNA classification than controls; therefore, molecular testing for COVID-19 is essential for LTR presenting with lower airway infection independently of imaging findings. • LTR had more extensive disease by semi-quantitative CT score and increased percentage areas of high attenuation on QCT.

Keywords: COVID-19; Computed tomography; Lung transplant; Quantitative CT.

MeSH terms

COVID-19 Testing
COVID-19*
Humans
Lung / diagnostic imaging
Retrospective Studies
SARS-CoV-2
Tomography, X-Ray Computed / methods
Transplant Recipients