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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8156322 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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