About: Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia
Creator	Colombi, Davide Bodini, C Sverzellati, Nicola Flavio, ; Maffi, Gabriele Michieletti, Emanuele Milanese, Gianluca Morelli, Nicola Petrini, Marcello Silva, Mario
source	Medline; PMC
abstract	BACKGROUND: Computed tomography (CT) of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease depicts the extent of lung involvement in COVID-19 pneumonia. PURPOSE: The aim of the study was to determine the value of quantification of the well-aerated lung obtained at baseline chest CT for determining prognosis in patients with COVID-19 pneumonia. MATERIALS AND METHODS: Patients who underwent chest CT suspected for COVID-19 pneumonia at the emergency department admission between February 17 to March 10, 2020 were retrospectively analyzed. Patients with negative reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2 in nasal-pharyngeal swabs, negative chest CT, and incomplete clinical data were excluded. CT was analyzed for quantification of well aerated lung visually (%V-WAL) and by open-source software (%S-WAL and absolute volume, VOL-WAL). Clinical parameters included demographics, comorbidities, symptoms and symptom duration, oxygen saturation and laboratory values. Logistic regression was used to evaluate relationship between clinical parameters and CT metrics versus patient outcome (ICU admission/death vs. no ICU admission/ death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. RESULTS: The study included 236 patients (females 59/123, 25%; median age, 68 years). A %V-WAL<73% (OR, 5.4; 95% CI, 2.7-10.8; P<0.001), %S-WAL<71% (OR, 3.8; 95% CI, 1.9-7.5; P<0.001), and VOL-WAL<2.9 L (OR, 2.6; 95% CI, 1.2-5.8; P<0.01) were predictors of ICU admission/death. In comparison with clinical model containing only clinical parameters (AUC, 0.83), all three quantitative models showed higher diagnostic performance (AUC 0.86 for all models). The models containing %V-WAL<73% and VOL-WAL<2.9L were superior in terms of performance as compared to the models containing only clinical parameters (P=0.04 for both models). CONCLUSION: In patients with confirmed COVID-19 pneumonia, visual or software quantification the extent of CT lung abnormality were predictors of ICU admission or death.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1148/radiol.2020201433
bibo:pmid	32301647
has license	no-cc
sha1sum (hex)	cd1bcf920c2168fadcf9c036827d4ba6a001db1a
schema:url	https://doi.org/10.1148/radiol.2020201433
resource representing a document's title	Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia
has PubMed Central identifier	PMC7233411
has PubMed identifier	32301647
schema:publication	Radiology
resource representing a document's body	covid:cd1bcf920c2168fadcf9c036827d4ba6a001db1a#body_text
is schema:about of	named entity 'This' named entity 'Lung' named entity 'Pneumonia' named entity 'COVID-19' named entity 'Lung' named entity 'pneumonia' named entity 'RT-PCR' named entity 'emphysema' named entity 'chest CT' named entity '5.2' named entity 'pulmonary abnormalities' named entity 'carina' named entity 'chest CT' named entity '95% CI' named entity 'mg/dl' named entity '95% CI' named entity 'ROC' named entity 'COVID' named entity 'emergency department' named entity 'ICU' named entity 'tidal volume' named entity 'COVID' named entity 'emergency department' named entity 'institutional review board' named entity 'emergency setting' named entity 'ARDS' named entity '95% CI' named entity 'ICU' named entity 'ROC' named entity 'pharyngeal' named entity '3.5' named entity 'COVID' named entity '95% CI' named entity 'prognostic significance' named entity 'lung parenchyma' named entity 'ICU' named entity 'R-squared' named entity 'Emotion' named entity 'lung parenchyma' named entity 'ICU' named entity 'influenza infection' named entity 'soft-tissue' named entity 'Cardiovascular diseases' named entity 'Siemens AG' named entity 'ICU' named entity 'emphysema' named entity 'SARS-CoV-2' named entity 'emergency department' named entity 'emphysema' named entity '95% confidence interval' named entity 'lung parenchyma' named entity 'COVID-19' named entity '10.8' named entity 'mediastinal' named entity 'pre-test probability' named entity 'Inter-rater agreement' named entity '95% CI' named entity '95% CI' named entity 'coronavirus' named entity 'GGO' named entity 'Dyspnea' named entity 'total lung volume' named entity 'SARS-CoV-2' named entity '95% CI' named entity 'radiography' named entity 'pulmonary fibrosis' named entity 'Statistical analysis' named entity 'radiographic' named entity 'LDH' named entity 'adipose tissue' named entity 'regression analysis' named entity 'ROC'

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