Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Lanza, Ezio, Muglia, Riccardo, Bolengo, Isabella, Santonocito, Orazio Giuseppe, Lisi, Costanza, Angelotti, Giovanni, Morandini, Pierandrea, Savevski, Victor, Politi, Letterio Salvatore, Balzarini, Luca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
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
_version_ 1783550730205921280
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
work_keys_str_mv AT lanzaezio quantitativechestctanalysisincovid19topredicttheneedforoxygenationsupportandintubation
AT mugliariccardo quantitativechestctanalysisincovid19topredicttheneedforoxygenationsupportandintubation
AT bolengoisabella quantitativechestctanalysisincovid19topredicttheneedforoxygenationsupportandintubation
AT santonocitooraziogiuseppe quantitativechestctanalysisincovid19topredicttheneedforoxygenationsupportandintubation
AT lisicostanza quantitativechestctanalysisincovid19topredicttheneedforoxygenationsupportandintubation
AT angelottigiovanni quantitativechestctanalysisincovid19topredicttheneedforoxygenationsupportandintubation
AT morandinipierandrea quantitativechestctanalysisincovid19topredicttheneedforoxygenationsupportandintubation
AT savevskivictor quantitativechestctanalysisincovid19topredicttheneedforoxygenationsupportandintubation
AT politiletteriosalvatore quantitativechestctanalysisincovid19topredicttheneedforoxygenationsupportandintubation
AT balzariniluca quantitativechestctanalysisincovid19topredicttheneedforoxygenationsupportandintubation