About: The performance of chest CT in evaluating the clinical severity of COVID-19 pneumonia: identifying critical cases based on CT characteristics

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: The performance of chest CT in evaluating the clinical severity of COVID-19 pneumonia: identifying critical cases based on CT characteristics Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	The performance of chest CT in evaluating the clinical severity of COVID-19 pneumonia: identifying critical cases based on CT characteristics
Creator	Shi, Lei Liu, Xing Zhang, Rui Gao, Jianbo Lyu, Peijie
Source	Medline; PMC
abstract	OBJECTIVES: To assess the clinical severity of COVID-19 pneumonia using qualitative and/or quantitative chest CT indicators and identify the CT characteristics of critical cases. MATERIALS AND METHODS: Fifty-one patients with COVID-19 pneumonia including ordinary cases (group A, n=12), severe cases(group B, n=15) and critical cases (group C, n=24) were retrospectively enrolled. The qualitative and quantitative indicators from chest CT were recorded and compared using Fisher's exact test, one-way ANOVA, Kruskal-Wallis H test and receiver operating characteristic analysis. RESULTS: Depending on the severity of the disease, the number of involved lung segments and lobes, the frequencies of consolidation, crazy-paving pattern and air bronchogram increased in more severe cases. Qualitative indicators including total severity score for the whole lung and total score for crazy-paving and consolidation could distinguish groups B and C from A(69% sensitivity, 83% specificity and 73% accuracy) but were similar between group B and group C. Combined qualitative and quantitative indicators could distinguish these three groups with high sensitivity(B+C vs. A, 90%; C vs. B, 92%), specificity(100%, 87%) and accuracy(92%, 90%). Critical cases had higher total severity score(>10) and higher total score for crazy-paving and consolidation(>4) than ordinary cases, and had higher mean lung density(>-779HU) and full width at half maximum(>128HU) but lower relative volume of normal lung density(≦50%) than ordinary/severe cases. In our critical cases, eight patients with relative volume of normal lung density smaller than 40% received mechanical ventilation for supportive treatment, and two of them had died. CONCLUSION: A rapid, accurate severity assessment of COVID-19 pneumonia based on chest CT would be feasible and could provide help for making management decisions, especially for the critical cases.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1097/rli.0000000000000689
bibo:pmid	32304402
has license	no-cc
sha1sum (hex)	d9684de2d9f394c567ef850f4af0996cca6c148e
schema:url	https://doi.org/10.1097/rli.0000000000000689
resource representing a document's title	The performance of chest CT in evaluating the clinical severity of COVID-19 pneumonia: identifying critical cases based on CT characteristics
has PubMed Central identifier	PMC7173027
has PubMed identifier	32304402
schema:publication	Invest Radiol
resource representing a document's body	covid:d9684de2d9f394c567ef850f4af0996cca6c148e#body_text
is schema:about of	named entity 'QUALITATIVE' named entity 'IDENTIFY' named entity 'CHARACTERISTICS' named entity 'CRITICAL' covid:arg/d9684de2d9f394c567ef850f4af0996cca6c148e named entity 'critical' named entity 'pneumonia' named entity 'qualitative' named entity 'cases' named entity 'chest CT' named entity 'COVID-19' named entity 'COVID-19' named entity 'iterative reconstruction' named entity 'GGO' named entity 'FWHM' named entity 'critical stage' named entity 'deep-learning' named entity 'FWHM' named entity 'Siemens Healthineers' named entity 'CT imaging' named entity 'medical equipment' named entity 'Chest CT' named entity 'DOI number' named entity 'clinical features' named entity 'pleural effusion' named entity 'DOI number' named entity 'exudation' named entity 'COVID-19' named entity 'COVID-19' named entity 'clinical characteristics' named entity 'sensitivity and specificity' named entity 'Wolters Kluwer Health' named entity 'lung parenchyma' named entity 'epidemic' named entity 'COVID-19' named entity 'alveoli' named entity 'exudate' named entity 'COVID-19' named entity 'DOI number' named entity 'Pulmonary fibrosis' named entity 'Wolters Kluwer Health' named entity 'voxel' named entity 'Wolters Kluwer Health' named entity 'Siemens Healthineers' named entity 'Wolters Kluwer Health' named entity 'pneumonia' named entity 'pneumonia' named entity 'pulmonary fibrosis' named entity 'DOI number' named entity 'COVID-19' named entity 'Middle East respiratory syndrome coronavirus' named entity 'lung volumes' named entity 'COVID-19' named entity 'Waukesha' named entity 'attenuation' named entity 'clinical characteristics' named entity 'FWHM' named entity 'China' named entity 'virus' named entity 'clinical symptoms' named entity 'respiratory epithelium' named entity 'evolution' named entity 'GGO' named entity 'COVID-19' named entity 'Wolters Kluwer Health' named entity 'exudate' named entity 'CT scans' named entity 'pneumonia' named entity '99-1' named entity 'prognosis' named entity 'pneumonia' named entity '8 days' named entity 'SARS-CoV-2' named entity 'COVID-19' named entity 'GGO' named entity 'supportive treatment' named entity 'sensitivity, specificity'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software