The Diagnostic Utility of Artificial Intelligence-Guided Computed Tomography-Based Severity Scores for Predicting Short-Term Clinical Outcomes in Adults with COVID-19 Pneumonia
(2023) In Journal of Clinical Medicine 12(22).- Abstract
Chest computed tomography (CT) imaging with the use of an artificial intelligence (AI) analysis program has been helpful for the rapid evaluation of large numbers of patients during the COVID-19 pandemic. We have previously demonstrated that adults with COVID-19 infection with high-risk obstructive sleep apnea (OSA) have poorer clinical outcomes than COVID-19 patients with low-risk OSA. In the current secondary analysis, we evaluated the association of AI-guided CT-based severity scores (SSs) with short-term outcomes in the same cohort. In total, 221 patients (mean age of 52.6 ± 15.6 years, 59% men) with eligible chest CT images from March to May 2020 were included. The AI program scanned the CT images in 3D, and the algorithm measured... (More)
Chest computed tomography (CT) imaging with the use of an artificial intelligence (AI) analysis program has been helpful for the rapid evaluation of large numbers of patients during the COVID-19 pandemic. We have previously demonstrated that adults with COVID-19 infection with high-risk obstructive sleep apnea (OSA) have poorer clinical outcomes than COVID-19 patients with low-risk OSA. In the current secondary analysis, we evaluated the association of AI-guided CT-based severity scores (SSs) with short-term outcomes in the same cohort. In total, 221 patients (mean age of 52.6 ± 15.6 years, 59% men) with eligible chest CT images from March to May 2020 were included. The AI program scanned the CT images in 3D, and the algorithm measured volumes of lobes and lungs as well as high-opacity areas, including ground glass and consolidation. An SS was defined as the ratio of the volume of high-opacity areas to that of the total lung volume. The primary outcome was the need for supplemental oxygen and hospitalization over 28 days. A receiver operating characteristic (ROC) curve analysis of the association between an SS and the need for supplemental oxygen revealed a cut-off score of 2.65 on the CT images, with a sensitivity of 81% and a specificity of 56%. In a multivariate logistic regression model, an SS > 2.65 predicted the need for supplemental oxygen, with an odds ratio (OR) of 3.98 (95% confidence interval (CI) 1.80–8.79; p < 0.001), and hospitalization, with an OR of 2.40 (95% CI 1.23–4.71; p = 0.011), adjusted for age, sex, body mass index, diabetes, hypertension, and coronary artery disease. We conclude that AI-guided CT-based SSs can be used for predicting the need for supplemental oxygen and hospitalization in patients with COVID-19 pneumonia.
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- author
- Atceken, Zeynep ; Celik, Yeliz ; Atasoy, Cetin and Peker, Yüksel LU
- organization
- publishing date
- 2023-11
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, chest CT, clinical outcomes, COVID-19, supplemental oxygen
- in
- Journal of Clinical Medicine
- volume
- 12
- issue
- 22
- article number
- 7039
- publisher
- MDPI AG
- external identifiers
-
- pmid:38002652
- scopus:85178387827
- ISSN
- 2077-0383
- DOI
- 10.3390/jcm12227039
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: The authors thank the Koc University Research Center for Translational Medicine, funded by the Presidency of Turkey, Presidency of Strategy and Budget, for the use of its services and facilities. Publisher Copyright: © 2023 by the authors.
- id
- fa714db4-850b-488f-863f-b06c3ca84480
- date added to LUP
- 2024-01-03 15:31:48
- date last changed
- 2024-09-06 02:15:58
@article{fa714db4-850b-488f-863f-b06c3ca84480, abstract = {{<p>Chest computed tomography (CT) imaging with the use of an artificial intelligence (AI) analysis program has been helpful for the rapid evaluation of large numbers of patients during the COVID-19 pandemic. We have previously demonstrated that adults with COVID-19 infection with high-risk obstructive sleep apnea (OSA) have poorer clinical outcomes than COVID-19 patients with low-risk OSA. In the current secondary analysis, we evaluated the association of AI-guided CT-based severity scores (SSs) with short-term outcomes in the same cohort. In total, 221 patients (mean age of 52.6 ± 15.6 years, 59% men) with eligible chest CT images from March to May 2020 were included. The AI program scanned the CT images in 3D, and the algorithm measured volumes of lobes and lungs as well as high-opacity areas, including ground glass and consolidation. An SS was defined as the ratio of the volume of high-opacity areas to that of the total lung volume. The primary outcome was the need for supplemental oxygen and hospitalization over 28 days. A receiver operating characteristic (ROC) curve analysis of the association between an SS and the need for supplemental oxygen revealed a cut-off score of 2.65 on the CT images, with a sensitivity of 81% and a specificity of 56%. In a multivariate logistic regression model, an SS > 2.65 predicted the need for supplemental oxygen, with an odds ratio (OR) of 3.98 (95% confidence interval (CI) 1.80–8.79; p < 0.001), and hospitalization, with an OR of 2.40 (95% CI 1.23–4.71; p = 0.011), adjusted for age, sex, body mass index, diabetes, hypertension, and coronary artery disease. We conclude that AI-guided CT-based SSs can be used for predicting the need for supplemental oxygen and hospitalization in patients with COVID-19 pneumonia.</p>}}, author = {{Atceken, Zeynep and Celik, Yeliz and Atasoy, Cetin and Peker, Yüksel}}, issn = {{2077-0383}}, keywords = {{artificial intelligence; chest CT; clinical outcomes; COVID-19; supplemental oxygen}}, language = {{eng}}, number = {{22}}, publisher = {{MDPI AG}}, series = {{Journal of Clinical Medicine}}, title = {{The Diagnostic Utility of Artificial Intelligence-Guided Computed Tomography-Based Severity Scores for Predicting Short-Term Clinical Outcomes in Adults with COVID-19 Pneumonia}}, url = {{http://dx.doi.org/10.3390/jcm12227039}}, doi = {{10.3390/jcm12227039}}, volume = {{12}}, year = {{2023}}, }