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Comparisons of chest computed tomography patterns between survivors and non-survivors with Coronavirus Disease 2019 (COVID-19): a case-control study (preprint)
researchsquare; 2020.
Preprint
in English
| PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-27399.v1
ABSTRACT
Objectives To compare the chest computed tomography (CT) findings between survivors and non-survivors with Coronavirus Disease 2019 (COVID-19).Materials and Methods Between 12 January 2020 to 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.Results Elder age (median 69 vs. 43y, p<0.001), higher male ratio (31/41 vs. 32/83, p<0.001), and more comorbidities were observed in non-survivor group. On admission, significantly more bilateral (97.6% vs. 73.5%, p=0.005) and diffuse lesions (39.0% vs. 8.4%, p<0.001) with higher total CT score (median 10 vs. 4) 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=0.004). From the prediction of curve estimation, in survivor group total CT score increased in the first 20 days reaching the peak of 6 points and then gradual decreased for more than other 40 days (R2=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 (R2=0.711, p<0.001).Conclusions 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.
Full text:
Available
Collection:
Preprints
Database:
PREPRINT-RESEARCHSQUARE
Main subject:
COVID-19
Language:
English
Year:
2020
Document Type:
Preprint
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