About: CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients

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About: CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients
Creator	Shi, Lei Zhang, Zhiyong Wang, Lin Zhang, Qi Lu, Hongzhou Shi, Jing Huang, Chao Shi, Nannan Shi, Yuxin Zhu, Tongyu Shan, Fei Song, Fengxiang Fang, Cong Ding, Zezhen Liu, Fengjun Mei, Xue Shi, Chunzi Su, Xiaoming Yang, Zhongcheng
Source	Medline; PMC
abstract	Rationale: Some patients with coronavirus disease 2019 (COVID-19) rapidly develop respiratory failure or even die, underscoring the need for early identification of patients at elevated risk of severe illness. This study aims to quantify pneumonia lesions by computed tomography (CT) in the early days to predict progression to severe illness in a cohort of COVID-19 patients. Methods: This retrospective cohort study included confirmed COVID-19 patients. Three quantitative CT features of pneumonia lesions were automatically calculated using artificial intelligence algorithms, representing the percentages of ground-glass opacity volume (PGV), semi-consolidation volume (PSV), and consolidation volume (PCV) in both lungs. CT features, acute physiology and chronic health evaluation II (APACHE-II) score, neutrophil-to-lymphocyte ratio (NLR), and d-dimer, on day 0 (hospital admission) and day 4, were collected to predict the occurrence of severe illness within a 28-day follow-up using both logistic regression and Cox proportional hazard models. Results: We included 134 patients, of whom 19 (14.2%) developed any severe illness. CT features on day 0 and day 4, as well as their changes from day 0 to day 4, showed predictive capability. Changes in CT features from day 0 to day 4 performed the best in the prediction (area under the receiver operating characteristic curve = 0.93, 95% confidence interval [CI] 0.87~0.99; C-index=0.88, 95% CI 0.81~0.95). The hazard ratios of PGV and PCV were 1.39 (95% CI 1.05~1.84, P=0.023) and 1.67 (95% CI 1.17~2.38, P=0.005), respectively. CT features, adjusted for age and gender, on day 4 and in terms of changes from day 0 to day 4 outperformed APACHE-II, NLR, and d-dimer. Conclusions: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.
has issue date	2020-04-27(xsd:dateTime)
bibo:doi	10.7150/thno.45985
bibo:pmid	32373235
has license	cc-by
sha1sum (hex)	5e27a621e9d938f46d71cabbb2878723dbe0fe95
schema:url	https://doi.org/10.7150/thno.45985
resource representing a document's title	CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients
has PubMed Central identifier	PMC7196293
has PubMed identifier	32373235
schema:publication	Theranostics
resource representing a document's body	covid:5e27a621e9d938f46d71cabbb2878723dbe0fe95#body_text
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