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Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation
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 qua...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317888/ https://www.ncbi.nlm.nih.gov/pubmed/32591888 http://dx.doi.org/10.1007/s00330-020-07013-2 |
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author | Lanza, Ezio Muglia, Riccardo Bolengo, Isabella Santonocito, Orazio Giuseppe Lisi, Costanza Angelotti, Giovanni Morandini, Pierandrea Savevski, Victor Politi, Letterio Salvatore Balzarini, Luca |
author_facet | Lanza, Ezio Muglia, Riccardo Bolengo, Isabella Santonocito, Orazio Giuseppe Lisi, Costanza Angelotti, Giovanni Morandini, Pierandrea Savevski, Victor Politi, Letterio Salvatore Balzarini, Luca |
author_sort | Lanza, Ezio |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7317888 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-73178882020-06-26 Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation Lanza, Ezio Muglia, Riccardo Bolengo, Isabella Santonocito, Orazio Giuseppe Lisi, Costanza Angelotti, Giovanni Morandini, Pierandrea Savevski, Victor Politi, Letterio Salvatore Balzarini, Luca Eur Radiol Chest 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. Springer Berlin Heidelberg 2020-06-26 2020 /pmc/articles/PMC7317888/ /pubmed/32591888 http://dx.doi.org/10.1007/s00330-020-07013-2 Text en © European Society of Radiology 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Chest Lanza, Ezio Muglia, Riccardo Bolengo, Isabella Santonocito, Orazio Giuseppe Lisi, Costanza Angelotti, Giovanni Morandini, Pierandrea Savevski, Victor Politi, Letterio Salvatore Balzarini, Luca Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation |
title | Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation |
title_full | Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation |
title_fullStr | Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation |
title_full_unstemmed | Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation |
title_short | Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation |
title_sort | quantitative chest ct analysis in covid-19 to predict the need for oxygenation support and intubation |
topic | Chest |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317888/ https://www.ncbi.nlm.nih.gov/pubmed/32591888 http://dx.doi.org/10.1007/s00330-020-07013-2 |
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