About: Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation

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About: Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation 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	Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation
Creator	Balzarini, Luca Angelotti, Giovanni Bolengo, Isabella Lanza, Ezio Lisi, Costanza Morandini, Pierandrea Muglia, Riccardo Santonocito, Orazio Savevski, Victor Letterio, & Politi, Salvatore
source	Medline; PMC
abstract	OBJECTIVE: Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19. METHODS: We performed a single-centre retrospective study on COVID-19 patients hospitalised from January 25, 2020, to April 28, 2020, who received CT at admission prompted by respiratory symptoms such as dyspnea or desaturation. QCT was performed using a semi-automated method (3D Slicer). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (− 500, 100 HU). We collected patient’s clinical data including oxygenation support throughout hospitalisation. RESULTS: Two hundred twenty-two patients (163 males, median age 66, IQR 54–6) were included; 75% received oxygenation support (20% intubation rate). Compromised lung volume was the most accurate outcome predictor (logistic regression, p < 0.001). %CL values in the 6–23% range increased risk of oxygenation support; values above 23% were at risk for intubation. %CL showed a negative correlation with PaO(2)/FiO(2) ratio (p < 0.001) and was a risk factor for in-hospital mortality (p < 0.001). CONCLUSIONS: QCT provides new metrics of COVID-19. The compromised lung volume is accurate in predicting the need for oxygenation support and intubation and is a significant risk factor for in-hospital death. QCT may serve as a tool for the triaging process of COVID-19. KEY POINTS: • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the − 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6–23% range prompt oxygenation therapy; values above 23% increase the need for intubation.
has issue date	2020-06-26(xsd:dateTime)
bibo:doi	10.1007/s00330-020-07013-2
bibo:pmid	32591888
has license	no-cc
sha1sum (hex)	c9cb44e690ea5d280530e85f705dbbd8fd2ab13c
schema:url	https://doi.org/10.1007/s00330-020-07013-2
resource representing a document's title	Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation
has PubMed Central identifier	PMC7317888
has PubMed identifier	32591888
schema:publication	Eur Radiol
resource representing a document's body	covid:c9cb44e690ea5d280530e85f705dbbd8fd2ab13c#body_text
is schema:about of	named entity 'performed' named entity 'Objective' named entity 'admission' named entity 'devices' named entity 'values' named entity 'collected' named entity 'patient' named entity 'volume' named entity 'performed' named entity 'Two' named entity 'COVID-19' named entity 'Quantitative' named entity 'PATIENTS' named entity 'TWENTY-TWO' named entity 'COVID-19 PANDEMIC' named entity 'INTERVALS' named entity 'SUFFERED' named entity 'data' named entity 'quantitative' named entity 'Given' named entity 'support' named entity 'oxygenation' named entity 'COVID-19' named entity 'risk' named entity 'chest' named entity 'suffered' named entity 'Hounsfield unit' named entity 'ICU beds' named entity 'lung volume' named entity 'ARDS' named entity 'Italy' named entity 'intubation' named entity 'COVID-19' named entity 'retrospective study' named entity 'March 2020' named entity 'lung volume' named entity 'machine learning' named entity 'Italy' named entity 'emergency department' named entity 'COVID' named entity 'Italy' named entity '1.06' named entity '3D Slicer' named entity 'Quantitative Computed Tomography' named entity 'ICU' named entity 'body mass index' named entity 'BMI' named entity 'covariate' named entity 'Spain' named entity 'CT scanner' named entity 'face mask' named entity 'ICU' named entity 'lung volume' named entity 'COVID' named entity 'nasal cannula' named entity 'intubation' named entity 'COVID-19' named entity 'adipose tissue' named entity '0.01' named entity 'Lung volumes' named entity 'mechanical ventilation' named entity 'early diagnosis' named entity 'Milan' named entity 'medical image computing' named entity 'lung volumes' named entity 'COVID' named entity '1.02' named entity 'learning curve' named entity 'ground-glass opacities' named entity 'ground-glass opacities' named entity 'Milan'

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