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Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome

To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years;...

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Autores principales: Homayounieh, Fatemeh, Bezerra Cavalcanti Rockenbach, Marcio Aloisio, Ebrahimian, Shadi, Doda Khera, Ruhani, Bizzo, Bernardo C., Buch, Varun, Babaei, Rosa, Karimi Mobin, Hadi, Mohseni, Iman, Mitschke, Matthias, Zimmermann, Mathis, Durlak, Felix, Rauch, Franziska, Digumarthy, Subba R, Kalra, Mannudeep K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906242/
https://www.ncbi.nlm.nih.gov/pubmed/33634416
http://dx.doi.org/10.1007/s10278-021-00430-9
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author Homayounieh, Fatemeh
Bezerra Cavalcanti Rockenbach, Marcio Aloisio
Ebrahimian, Shadi
Doda Khera, Ruhani
Bizzo, Bernardo C.
Buch, Varun
Babaei, Rosa
Karimi Mobin, Hadi
Mohseni, Iman
Mitschke, Matthias
Zimmermann, Mathis
Durlak, Felix
Rauch, Franziska
Digumarthy, Subba R
Kalra, Mannudeep K.
author_facet Homayounieh, Fatemeh
Bezerra Cavalcanti Rockenbach, Marcio Aloisio
Ebrahimian, Shadi
Doda Khera, Ruhani
Bizzo, Bernardo C.
Buch, Varun
Babaei, Rosa
Karimi Mobin, Hadi
Mohseni, Iman
Mitschke, Matthias
Zimmermann, Mathis
Durlak, Felix
Rauch, Franziska
Digumarthy, Subba R
Kalra, Mannudeep K.
author_sort Homayounieh, Fatemeh
collection PubMed
description To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ −200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94–0.97) as well as for predicting patient outcome (AUC 0.7–0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76–0.82, 95% confidence interval (CI) 0.73–0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69–0.77, 95% CI 0.69–0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.
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spelling pubmed-79062422021-02-26 Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome Homayounieh, Fatemeh Bezerra Cavalcanti Rockenbach, Marcio Aloisio Ebrahimian, Shadi Doda Khera, Ruhani Bizzo, Bernardo C. Buch, Varun Babaei, Rosa Karimi Mobin, Hadi Mohseni, Iman Mitschke, Matthias Zimmermann, Mathis Durlak, Felix Rauch, Franziska Digumarthy, Subba R Kalra, Mannudeep K. J Digit Imaging Article To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ −200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94–0.97) as well as for predicting patient outcome (AUC 0.7–0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76–0.82, 95% confidence interval (CI) 0.73–0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69–0.77, 95% CI 0.69–0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes. Springer International Publishing 2021-02-25 2021-04 /pmc/articles/PMC7906242/ /pubmed/33634416 http://dx.doi.org/10.1007/s10278-021-00430-9 Text en © Society for Imaging Informatics in Medicine 2021
spellingShingle Article
Homayounieh, Fatemeh
Bezerra Cavalcanti Rockenbach, Marcio Aloisio
Ebrahimian, Shadi
Doda Khera, Ruhani
Bizzo, Bernardo C.
Buch, Varun
Babaei, Rosa
Karimi Mobin, Hadi
Mohseni, Iman
Mitschke, Matthias
Zimmermann, Mathis
Durlak, Felix
Rauch, Franziska
Digumarthy, Subba R
Kalra, Mannudeep K.
Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
title Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
title_full Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
title_fullStr Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
title_full_unstemmed Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
title_short Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
title_sort multicenter assessment of ct pneumonia analysis prototype for predicting disease severity and patient outcome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906242/
https://www.ncbi.nlm.nih.gov/pubmed/33634416
http://dx.doi.org/10.1007/s10278-021-00430-9
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