The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission

Abstract Triage is crucial for patient’s management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient’s a...

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Autores principales:	Maurizio Bartolucci, Matteo Benelli, Margherita Betti, Sara Bicchi, Luca Fedeli, Federico Giannelli, Donatella Aquilini, Alessio Baldini, Guglielmo Consales, Massimo Edoardo Di Natale, Pamela Lotti, Letizia Vannucchi, Michele Trezzi, Lorenzo Nicola Mazzoni, Sandro Santini, Roberto Carpi, Daniela Matarrese, Luca Bernardi, Mario Mascalchi, the COVID Working Group
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9e781ab8aa3947e99cd855a85eb8952a
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spelling	oai:doaj.org-article:9e781ab8aa3947e99cd855a85eb8952a2021-12-02T18:49:21ZThe incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission10.1038/s41598-021-95114-32045-2322https://doaj.org/article/9e781ab8aa3947e99cd855a85eb8952a2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95114-3https://doaj.org/toc/2045-2322Abstract Triage is crucial for patient’s management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient’s admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73–0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56–0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp . The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.Maurizio BartolucciMatteo BenelliMargherita BettiSara BicchiLuca FedeliFederico GiannelliDonatella AquiliniAlessio BaldiniGuglielmo ConsalesMassimo Edoardo Di NatalePamela LottiLetizia VannucchiMichele TrezziLorenzo Nicola MazzoniSandro SantiniRoberto CarpiDaniela MatarreseLuca BernardiMario Mascalchithe COVID Working GroupNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maurizio Bartolucci Matteo Benelli Margherita Betti Sara Bicchi Luca Fedeli Federico Giannelli Donatella Aquilini Alessio Baldini Guglielmo Consales Massimo Edoardo Di Natale Pamela Lotti Letizia Vannucchi Michele Trezzi Lorenzo Nicola Mazzoni Sandro Santini Roberto Carpi Daniela Matarrese Luca Bernardi Mario Mascalchi the COVID Working Group The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission
description	Abstract Triage is crucial for patient’s management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient’s admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73–0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56–0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp . The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.
format	article
author	Maurizio Bartolucci Matteo Benelli Margherita Betti Sara Bicchi Luca Fedeli Federico Giannelli Donatella Aquilini Alessio Baldini Guglielmo Consales Massimo Edoardo Di Natale Pamela Lotti Letizia Vannucchi Michele Trezzi Lorenzo Nicola Mazzoni Sandro Santini Roberto Carpi Daniela Matarrese Luca Bernardi Mario Mascalchi the COVID Working Group
author_facet	Maurizio Bartolucci Matteo Benelli Margherita Betti Sara Bicchi Luca Fedeli Federico Giannelli Donatella Aquilini Alessio Baldini Guglielmo Consales Massimo Edoardo Di Natale Pamela Lotti Letizia Vannucchi Michele Trezzi Lorenzo Nicola Mazzoni Sandro Santini Roberto Carpi Daniela Matarrese Luca Bernardi Mario Mascalchi the COVID Working Group
author_sort	Maurizio Bartolucci
title	The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission
title_short	The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission
title_full	The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission
title_fullStr	The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission
title_full_unstemmed	The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission
title_sort	incremental value of computed tomography of covid-19 pneumonia in predicting icu admission
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9e781ab8aa3947e99cd855a85eb8952a
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The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission

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