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Clin Microbiol Infect . CT lung lesions as predictors of early death or ICU admission in COVID-19 patients

tetano

Editor, Senior Moderator
Clin Microbiol Infect


. 2020 Jul 24;S1198-743X(20)30438-9.
doi: 10.1016/j.cmi.2020.07.030. Online ahead of print.
CT lung lesions as predictors of early death or ICU admission in COVID-19 patients


Yvon Ruch[SUP] 1 [/SUP], Charlotte Kaeuffer[SUP] 2 [/SUP], Mickael Ohana[SUP] 3 [/SUP], Aissam Labani[SUP] 3 [/SUP], Thibaut Fabacher[SUP] 4 [/SUP], Pascal Bilbault[SUP] 5 [/SUP], Sabrina Kepka[SUP] 5 [/SUP], Morgane Solis[SUP] 6 [/SUP], Valentin Greigert[SUP] 2 [/SUP], Nicolas Lefebvre[SUP] 2 [/SUP], Yves Hansmann[SUP] 2 [/SUP], Fran?ois Danion[SUP] 2 [/SUP]



Affiliations

Abstract

Objective: The main objective of this study was to investigate the prognostic value of early systematic chest computed tomography (CT) with quantification of lung lesions in coronavirus disease 2019 (COVID-19) patients.
Methods: We studied 572 patients diagnosed with COVID-19, confirmed using polymerase chain reaction for whom a chest CT was performed at hospital admission. Visual quantification was used to classify patients as per the percentage of lung parenchyma affected by COVID-19 lesions: normal CT, 0%-10%, 11%-25%, 26%-50%, 51%-75%, and >75%. The primary endpoint was severe disease, defined by death or intensive care unit admission in the 7 days following admission.
Results: The mean patient age was 66.0 ? 16.0 years, and 343/572 (60.0%) were men. The primary endpoint occurred in 206/572 (36.0%) patients. The extent of lesions on initial CT was independently associated with prognosis (odds ratio = 2.35, 95% confidence interval 1.24-4.46; p <0.01). Most patients with lung involvement >50% developed severe disease (66/95, 69.5%), compared to patients with lung involvement of 26%-50% (70/171, 40.9%) and ≤25% (70/306, 22.9%) (p <0.01 and p <0.01, respectively). None (0/14) of the patients with normal CT had severe disease.
Conclusion: Chest CT findings at admission are associated with bad outcome in COVID-19 patients.

Keywords: COVID-19; Computed tomography; coronavirus; ground-glass opacities; visual quantification.
 
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