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Sci Rep . Different Computed Tomography Patterns of Coronavirus Disease 2019 (COVID-19) Between Survivors and Non-Survivors

tetano

Editor, Senior Moderator
Sci Rep


. 2020 Jul 9;10(1):11336.
doi: 10.1038/s41598-020-68057-4.
Different Computed Tomography Patterns of Coronavirus Disease 2019 (COVID-19) Between Survivors and Non-Survivors


Feng Pan[SUP] 1 2 [/SUP], Chuansheng Zheng[SUP] 1 2 [/SUP], Tianhe Ye[SUP] 1 2 [/SUP], Lingli Li[SUP] 1 2 [/SUP], Dehan Liu[SUP] 1 2 [/SUP], Lin Li[SUP] 1 2 [/SUP], Richard L Hesketh[SUP] 3 [/SUP], Lian Yang[SUP] 4 5 [/SUP]



Affiliations

Abstract

This study aimed to compare the chest computed tomography (CT) findings between survivors and non-survivors with Coronavirus Disease 2019 (COVID-19). Between 12 January 2020 and 20 February 2020, the records of 124 consecutive patients diagnosed with COVID-19 were retrospectively reviewed and divided into survivor (83/124) and non-survivor (41/124) groups. Chest CT findings were qualitatively compared on admission and serial chest CT scans were semi-quantitively evaluated between two groups using curve estimations. On admission, significantly more bilateral (97.6% vs. 73.5%, p = 0.001) and diffuse lesions (39.0% vs. 8.4%, p < 0.001) with higher total CT score (median 10 vs. 4, p < 0.001) were observed in non-survivor group compared with survivor group. Besides, crazy-paving pattern was more predominant in non-survivor group than survivor group (39.0% vs. 12.0%, p < 0.001). From the prediction of curve estimation, in survivor group total CT score increased in the first 20 days reaching a peak of 6 points and then gradually decreased for more than other 40 days (R[SUP]2[/SUP] = 0.545, p < 0.001). In non-survivor group, total CT score rapidly increased over 10 points in the first 10 days and gradually increased afterwards until ARDS occurred with following death events (R[SUP]2[/SUP] = 0.711, p < 0.001). In conclusion, persistent progression with predominant crazy-paving pattern was the major manifestation of COVID-19 in non-survivors. Understanding this CT feature could help the clinical physician to predict the prognosis of the patients.
 
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