AUTHOR=Yuyun Xu , Lexi Yu , Haochu Wang , Zhenyu Shu , Xiangyang Gong
TITLE=Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments
JOURNAL=Frontiers in Public Health
VOLUME=9
YEAR=2021
URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2021.596938
DOI=10.3389/fpubh.2021.596938
ISSN=2296-2565
ABSTRACT=Background: The coronavirus disease 2019 (COVID-19) outbreak is spreading rapidly around the world.
Purpose: We aimed to explore early warning information for patients with severe/critical COVID-19 based on quantitative analysis of chest CT images at the lung segment level.
Materials and Methods: A dataset of 81 patients with coronavirus disease 2019 (COVID-19) treated at Wuhan Wuchang hospital in Wuhan city from 21 January 2020 to 14 February 2020 was retrospectively analyzed, including ordinary and severe/critical cases. The time course of all subjects was divided into four stages. The differences in each lobe and lung segment between the two groups at each stage were quantitatively analyzed using the percentage of lung involvement (PLI) in order to investigate the most important segment of lung involvement in the severe/critical group and its corresponding time point.
Results: Lung involvement in the ordinary and severe/critical groups reached a peak on the 18th and 14th day, respectively. In the first stage, PLIs in the right middle lobe and the left superior lobe between the two groups were significantly different. In the second stage and the fourth stage, there were statistically significant differences between the two groups in the whole lung, right superior lobe, right inferior lobe and left superior lobe. The rapid progress of the lateral segment of the right middle lobe on the second day and the anterior segment of the right upper lobe on the 13th day may be a warning sign for severe/critical patients. Age was the most important demographic characteristic of the severe/critical group.
Conclusion: Quantitative assessment based on the lung segments of chest CT images provides early warning information for potentially severe/critical patients.