Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease

(1) Background: COVID-19 continues to represent a worrying pandemic. Despite the high percentage of non-severe illness, a wide clinical variability is often reported in real-world practice. Accurate predictors of disease aggressiveness, however, are still lacking. The purpose of our study was to eva...

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Autores principales:	Pierpaolo Palumbo, Maria Michela Palumbo, Federico Bruno, Giovanna Picchi, Antonio Iacopino, Chiara Acanfora, Ferruccio Sgalambro, Francesco Arrigoni, Arturo Ciccullo, Benedetta Cosimini, Alessandra Splendiani, Antonio Barile, Francesco Masedu, Alessandro Grimaldi, Ernesto Di Cesare, Carlo Masciocchi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	COVID-19 lung inflammation prognosis tomography computed scanners lung volume measurement Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/da4fcf94af0943caa330f6233a15e65c
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spelling	oai:doaj.org-article:da4fcf94af0943caa330f6233a15e65c2021-11-25T17:21:49ZAutomated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease10.3390/diagnostics111121252075-4418https://doaj.org/article/da4fcf94af0943caa330f6233a15e65c2021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/2125https://doaj.org/toc/2075-4418(1) Background: COVID-19 continues to represent a worrying pandemic. Despite the high percentage of non-severe illness, a wide clinical variability is often reported in real-world practice. Accurate predictors of disease aggressiveness, however, are still lacking. The purpose of our study was to evaluate the impact of quantitative analysis of lung computed tomography (CT) on non-intensive care unit (ICU) COVID-19 patients’ prognostication; (2) Methods: Our historical prospective study included fifty-five COVID-19 patients consecutively submitted to unenhanced lung CT. Primary outcomes were recorded during hospitalization, including composite ICU admission for the need of mechanical ventilation and/or death occurrence. CT examinations were retrospectively evaluated to automatically calculate differently aerated lung tissues (i.e., overinflated, well-aerated, poorly aerated, and non-aerated tissue). Scores based on the percentage of lung weight and volume were also calculated; (3) Results: Patients who reported disease progression showed lower total lung volume. Inflammatory indices correlated with indices of respiratory failure and high-density areas. Moreover, non-aerated and poorly aerated lung tissue resulted significantly higher in patients with disease progression. Notably, non-aerated lung tissue was independently associated with disease progression (HR: 1.02; <i>p</i>-value: 0.046). When different predictive models including clinical, laboratoristic, and CT findings were analyzed, the best predictive validity was reached by the model that included non-aerated tissue (C-index: 0.97; <i>p</i>-value: 0.0001); (4) Conclusions: Quantitative lung CT offers wide advantages in COVID-19 disease stratification. Non-aerated lung tissue is more likely to occur with severe inflammation status, turning out to be a strong predictor for disease aggressiveness; therefore, it should be included in the predictive model of COVID-19 patients.Pierpaolo PalumboMaria Michela PalumboFederico BrunoGiovanna PicchiAntonio IacopinoChiara AcanforaFerruccio SgalambroFrancesco ArrigoniArturo CicculloBenedetta CosiminiAlessandra SplendianiAntonio BarileFrancesco MaseduAlessandro GrimaldiErnesto Di CesareCarlo MasciocchiMDPI AGarticleCOVID-19lung inflammationprognosistomography computed scannerslung volume measurementMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 2125, p 2125 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 lung inflammation prognosis tomography computed scanners lung volume measurement Medicine (General) R5-920
spellingShingle	COVID-19 lung inflammation prognosis tomography computed scanners lung volume measurement Medicine (General) R5-920 Pierpaolo Palumbo Maria Michela Palumbo Federico Bruno Giovanna Picchi Antonio Iacopino Chiara Acanfora Ferruccio Sgalambro Francesco Arrigoni Arturo Ciccullo Benedetta Cosimini Alessandra Splendiani Antonio Barile Francesco Masedu Alessandro Grimaldi Ernesto Di Cesare Carlo Masciocchi Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease
description	(1) Background: COVID-19 continues to represent a worrying pandemic. Despite the high percentage of non-severe illness, a wide clinical variability is often reported in real-world practice. Accurate predictors of disease aggressiveness, however, are still lacking. The purpose of our study was to evaluate the impact of quantitative analysis of lung computed tomography (CT) on non-intensive care unit (ICU) COVID-19 patients’ prognostication; (2) Methods: Our historical prospective study included fifty-five COVID-19 patients consecutively submitted to unenhanced lung CT. Primary outcomes were recorded during hospitalization, including composite ICU admission for the need of mechanical ventilation and/or death occurrence. CT examinations were retrospectively evaluated to automatically calculate differently aerated lung tissues (i.e., overinflated, well-aerated, poorly aerated, and non-aerated tissue). Scores based on the percentage of lung weight and volume were also calculated; (3) Results: Patients who reported disease progression showed lower total lung volume. Inflammatory indices correlated with indices of respiratory failure and high-density areas. Moreover, non-aerated and poorly aerated lung tissue resulted significantly higher in patients with disease progression. Notably, non-aerated lung tissue was independently associated with disease progression (HR: 1.02; <i>p</i>-value: 0.046). When different predictive models including clinical, laboratoristic, and CT findings were analyzed, the best predictive validity was reached by the model that included non-aerated tissue (C-index: 0.97; <i>p</i>-value: 0.0001); (4) Conclusions: Quantitative lung CT offers wide advantages in COVID-19 disease stratification. Non-aerated lung tissue is more likely to occur with severe inflammation status, turning out to be a strong predictor for disease aggressiveness; therefore, it should be included in the predictive model of COVID-19 patients.
format	article
author	Pierpaolo Palumbo Maria Michela Palumbo Federico Bruno Giovanna Picchi Antonio Iacopino Chiara Acanfora Ferruccio Sgalambro Francesco Arrigoni Arturo Ciccullo Benedetta Cosimini Alessandra Splendiani Antonio Barile Francesco Masedu Alessandro Grimaldi Ernesto Di Cesare Carlo Masciocchi
author_facet	Pierpaolo Palumbo Maria Michela Palumbo Federico Bruno Giovanna Picchi Antonio Iacopino Chiara Acanfora Ferruccio Sgalambro Francesco Arrigoni Arturo Ciccullo Benedetta Cosimini Alessandra Splendiani Antonio Barile Francesco Masedu Alessandro Grimaldi Ernesto Di Cesare Carlo Masciocchi
author_sort	Pierpaolo Palumbo
title	Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease
title_short	Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease
title_full	Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease
title_fullStr	Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease
title_full_unstemmed	Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease
title_sort	automated quantitative lung ct improves prognostication in non-icu covid-19 patients beyond conventional biomarkers of disease
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/da4fcf94af0943caa330f6233a15e65c
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Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease

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