About: Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method

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

About: Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method 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
title	Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method
Creator	Alfieri, Giulia Cartocci, Gaia Catalano, Carlo Ciccarelli, Fabio Dolciami, Miriam Francone, Marco Iafrate, Franco Imperiale, Carmela Leonardi, Andrea Perotti, Stefano Petrillo, Roberta Ricci, Paolo Scala, Annarita Scipione, Roberto
source	Elsevier; Medline; PMC
abstract	BACKGROUND: So far, only a few studies evaluated the correlation between CT features and clinical outcome in patients with COVID-19 pneumonia. PURPOSE: To evaluate CT ability in differentiating critically ill patients requiring invasive ventilation from patients with less severe disease. METHODS: We retrospectively collected data from patients admitted to our institution for COVID-19 pneumonia between March 5th-24th. Patients were considered critically ill or non-critically ill, depending on the need for mechanical ventilation. CT images from both groups were analyzed for the assessment of qualitative features and disease extension, using a quantitative semiautomatic method. We evaluated the differences between the two groups for clinical, laboratory and CT data. Analyses were conducted on a per-protocol basis. RESULTS: 189 patients were analyzed. PaO(2)/FIO(2) ratio and oxygen saturation (SaO(2)) were decreased in critically ill patients. At CT, mixed pattern (ground glass opacities (GGO) and consolidation) and GGO alone were more frequent respectively in critically ill and in non-critically ill patients (p < 0.05). Lung volume involvement was significantly higher in critically ill patients (38.5 % vs. 5.8 %, p < 0.05). A cut-off of 23.0 % of lung involvement showed 96 % sensitivity and 96 % specificity in distinguishing critically ill patients from patients with less severe disease. The fraction of involved lung was related to lactate dehydrogenase (LDH) levels, PaO(2)/FIO(2) ratio and SaO(2) (p < 0.05). CONCLUSION: Lung disease extension, assessed using quantitative CT, has a significant relationship with clinical severity and may predict the need for invasive ventilation in patients with COVID-19.
has issue date	2020-07-29(xsd:dateTime)
bibo:doi	10.1016/j.ejrad.2020.109202
bibo:pmid	32745895
has license	no-cc
sha1sum (hex)	f28e3c0db6370aaf31b54870905a3c91d52bc6ba
schema:url	https://doi.org/10.1016/j.ejrad.2020.109202
resource representing a document's title	Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method
has PubMed Central identifier	PMC7388797
has PubMed identifier	32745895
schema:publication	Eur J Radiol
resource representing a document's body	covid:f28e3c0db6370aaf31b54870905a3c91d52bc6ba#body_text
is schema:about of	named entity 'sensitivity' named entity 'Results' named entity 'features' named entity 'involved' named entity 'patients' named entity 'pneumonia' named entity 'critically ill' named entity 'FIO' named entity 'semiautomatic' named entity 'ill' named entity 'ill' named entity 'evaluated' named entity 'quantitative' named entity 'ability' named entity 'pattern' named entity 'FIO' named entity 'laboratory' named entity 'patients' named entity 'data' named entity 'evaluate' named entity 'ventilation' named entity 'invasive ventilation' named entity 'lung' named entity 'lung' named entity 'pneumonia' named entity 'critically ill patients' named entity 'CT images' named entity 'Lung volume' named entity 'clinical outcome' named entity 'Lung disease' named entity 'pneumonia' named entity 'COVID' named entity 'COVID' named entity 'critically ill patients' named entity 'quantitative method' named entity 'retrospective study' named entity 'Lung disease' named entity 'chest imaging' named entity 'lung volume' named entity 'laboratory tests' named entity 'CPAP' named entity 'chronic obstructive pulmonary disease' named entity 'lung disease' named entity 'differential diagnosis' named entity '1.000' named entity 'GGO' named entity 'severe pneumonia' named entity 'COVID-19' named entity 'Pneumonia' named entity 'Germany' named entity 'oxygen' named entity 'pulmonary lobe' named entity 'lung volumes' named entity 'chest X-ray' named entity 'SARS-CoV-2' named entity 'critically ill patients' named entity 'asymptomatic' named entity 'SARS-Cov-2' named entity 'institutional review board' named entity 'lactate dehydrogenase' named entity 'infection' named entity 'throat swab' named entity 'evolution' named entity 'cut-off' named entity 'COVID-19' named entity 'pleural diseases' named entity '1, 2' named entity 'significant difference'

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 © 2009-2025 OpenLink Software