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Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia

The purpose of our work was to assess the independent and incremental value of AI-derived quantitative determination of lung lesions extent on initial CT scan for the prediction of clinical deterioration or death in patients hospitalized with COVID-19 pneumonia. 323 consecutive patients (mean age 65...

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Autores principales: Chabi, Marie Laure, Dana, Ophélie, Kennel, Titouan, Gence-Breney, Alexia, Salvator, Hélène, Ballester, Marie Christine, Vasse, Marc, Brun, Anne Laure, Mellot, François, Grenier, Philippe A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156322/
https://www.ncbi.nlm.nih.gov/pubmed/34069115
http://dx.doi.org/10.3390/diagnostics11050878
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author Chabi, Marie Laure
Dana, Ophélie
Kennel, Titouan
Gence-Breney, Alexia
Salvator, Hélène
Ballester, Marie Christine
Vasse, Marc
Brun, Anne Laure
Mellot, François
Grenier, Philippe A.
author_facet Chabi, Marie Laure
Dana, Ophélie
Kennel, Titouan
Gence-Breney, Alexia
Salvator, Hélène
Ballester, Marie Christine
Vasse, Marc
Brun, Anne Laure
Mellot, François
Grenier, Philippe A.
author_sort Chabi, Marie Laure
collection PubMed
description The purpose of our work was to assess the independent and incremental value of AI-derived quantitative determination of lung lesions extent on initial CT scan for the prediction of clinical deterioration or death in patients hospitalized with COVID-19 pneumonia. 323 consecutive patients (mean age 65 ± 15 years, 192 men), with laboratory-confirmed COVID-19 and an abnormal chest CT scan, were admitted to the hospital between March and December 2020. The extent of consolidation and all lung opacities were quantified on an initial CT scan using a 3D automatic AI-based software. The outcome was known for all these patients. 85 (26.3%) patients died or experienced clinical deterioration, defined as intensive care unit admission. In multivariate regression based on clinical, biological and CT parameters, the extent of all opacities, and extent of consolidation were independent predictors of adverse outcomes, as were diabetes, heart disease, C-reactive protein, and neutrophils/lymphocytes ratio. The association of CT-derived measures with clinical and biological parameters significantly improved the risk prediction (p = 0.049). Automated quantification of lung disease at CT in COVID-19 pneumonia is useful to predict clinical deterioration or in-hospital death. Its combination with clinical and biological data improves risk prediction.
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spelling pubmed-81563222021-05-28 Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia Chabi, Marie Laure Dana, Ophélie Kennel, Titouan Gence-Breney, Alexia Salvator, Hélène Ballester, Marie Christine Vasse, Marc Brun, Anne Laure Mellot, François Grenier, Philippe A. Diagnostics (Basel) Article The purpose of our work was to assess the independent and incremental value of AI-derived quantitative determination of lung lesions extent on initial CT scan for the prediction of clinical deterioration or death in patients hospitalized with COVID-19 pneumonia. 323 consecutive patients (mean age 65 ± 15 years, 192 men), with laboratory-confirmed COVID-19 and an abnormal chest CT scan, were admitted to the hospital between March and December 2020. The extent of consolidation and all lung opacities were quantified on an initial CT scan using a 3D automatic AI-based software. The outcome was known for all these patients. 85 (26.3%) patients died or experienced clinical deterioration, defined as intensive care unit admission. In multivariate regression based on clinical, biological and CT parameters, the extent of all opacities, and extent of consolidation were independent predictors of adverse outcomes, as were diabetes, heart disease, C-reactive protein, and neutrophils/lymphocytes ratio. The association of CT-derived measures with clinical and biological parameters significantly improved the risk prediction (p = 0.049). Automated quantification of lung disease at CT in COVID-19 pneumonia is useful to predict clinical deterioration or in-hospital death. Its combination with clinical and biological data improves risk prediction. MDPI 2021-05-14 /pmc/articles/PMC8156322/ /pubmed/34069115 http://dx.doi.org/10.3390/diagnostics11050878 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chabi, Marie Laure
Dana, Ophélie
Kennel, Titouan
Gence-Breney, Alexia
Salvator, Hélène
Ballester, Marie Christine
Vasse, Marc
Brun, Anne Laure
Mellot, François
Grenier, Philippe A.
Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia
title Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia
title_full Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia
title_fullStr Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia
title_full_unstemmed Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia
title_short Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia
title_sort automated ai-driven ct quantification of lung disease predicts adverse outcomes in patients hospitalized for covid-19 pneumonia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156322/
https://www.ncbi.nlm.nih.gov/pubmed/34069115
http://dx.doi.org/10.3390/diagnostics11050878
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