Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study

Abstract To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial...

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Autores principales:	Shadi Ebrahimian, Fatemeh Homayounieh, Marcio A. B. C. Rockenbach, Preetham Putha, Tarun Raj, Ittai Dayan, Bernardo C. Bizzo, Varun Buch, Dufan Wu, Kyungsang Kim, Quanzheng Li, Subba R. Digumarthy, Mannudeep K. Kalra
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f8c2b5d978b64d608349f7877ae3ab70
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spelling	oai:doaj.org-article:f8c2b5d978b64d608349f7877ae3ab702021-12-02T14:12:42ZArtificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study10.1038/s41598-020-79470-02045-2322https://doaj.org/article/f8c2b5d978b64d608349f7877ae3ab702021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79470-0https://doaj.org/toc/2045-2322Abstract To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r2 = 0.79–0.86; p < 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p < 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p < 0.001). The addition of patients’ age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90–0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87–0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.Shadi EbrahimianFatemeh HomayouniehMarcio A. B. C. RockenbachPreetham PuthaTarun RajIttai DayanBernardo C. BizzoVarun BuchDufan WuKyungsang KimQuanzheng LiSubba R. DigumarthyMannudeep K. KalraNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shadi Ebrahimian Fatemeh Homayounieh Marcio A. B. C. Rockenbach Preetham Putha Tarun Raj Ittai Dayan Bernardo C. Bizzo Varun Buch Dufan Wu Kyungsang Kim Quanzheng Li Subba R. Digumarthy Mannudeep K. Kalra Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
description	Abstract To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r2 = 0.79–0.86; p < 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p < 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p < 0.001). The addition of patients’ age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90–0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87–0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.
format	article
author	Shadi Ebrahimian Fatemeh Homayounieh Marcio A. B. C. Rockenbach Preetham Putha Tarun Raj Ittai Dayan Bernardo C. Bizzo Varun Buch Dufan Wu Kyungsang Kim Quanzheng Li Subba R. Digumarthy Mannudeep K. Kalra
author_facet	Shadi Ebrahimian Fatemeh Homayounieh Marcio A. B. C. Rockenbach Preetham Putha Tarun Raj Ittai Dayan Bernardo C. Bizzo Varun Buch Dufan Wu Kyungsang Kim Quanzheng Li Subba R. Digumarthy Mannudeep K. Kalra
author_sort	Shadi Ebrahimian
title	Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
title_short	Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
title_full	Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
title_fullStr	Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
title_full_unstemmed	Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
title_sort	artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f8c2b5d978b64d608349f7877ae3ab70
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Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study

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