Association of High-Risk Obstructive Sleep Apnea with Artificial Intelligence-Guided, CT-Based Severity Scores in Patients with COVID-19 Pneumonia
(2024) In Journal of Clinical Medicine 13(21).- Abstract
Background: We have previously demonstrated that high-risk obstructive sleep apnea (HR-OSA), based on a modified Berlin Questionnaire (mBQ), is linked to worse clinical outcomes. Chest computed tomography (CT) imaging with the implementation of an artificial intelligence (AI) analysis program has been a valuable tool for the speedy assessment of huge numbers of patients during the COVID-19 epidemic. In the current study, we addressed how the severity of AI-guided, CT-based total opacity ratio (TOR) scores are associated with high-risk OSA and short-term outcomes in the same cohort. Methods: The ratio of the volume of high opacity areas to that of the total lung volume constituted the TOR. We arbitrarily applied thresholds of <5 (no... (More)
Background: We have previously demonstrated that high-risk obstructive sleep apnea (HR-OSA), based on a modified Berlin Questionnaire (mBQ), is linked to worse clinical outcomes. Chest computed tomography (CT) imaging with the implementation of an artificial intelligence (AI) analysis program has been a valuable tool for the speedy assessment of huge numbers of patients during the COVID-19 epidemic. In the current study, we addressed how the severity of AI-guided, CT-based total opacity ratio (TOR) scores are associated with high-risk OSA and short-term outcomes in the same cohort. Methods: The ratio of the volume of high opacity areas to that of the total lung volume constituted the TOR. We arbitrarily applied thresholds of <5 (no or mild TOR), ≥5 and <15 (moderate TOR), and ≥15 (severe TOR). Results: In total, 221 patients were included. HR-OSA was observed among 11.0% of the no or mild TOR group, 22.2% of the moderate TOR group, and 38.7% of the severe TOR group (p < 0.001). In a logistic regression analysis, HR-OSA was associated with a severe TOR with an adjusted odds ratio of 3.06 (95% confidence interval [CI] 1.27–7.44; p = 0.01). A moderate TOR predicted clinical worsening with an adjusted hazard ratio (HR) of 1.93 (95% CI 1.00–3.72; p = 0.05) and a severe TOR predicted worsening with an HR of 3.06 (95% CI 1.56–5.99; p = 0.001). Conclusions: Our results offer additional radiological proof of the relationship between HR-OSA and worse outcomes in patients with COVID-19 pneumonia. A TOR may also potentially indicate the individuals that are at higher risk of HR-OSA, enabling early intervention and management strategies. The clinical significance of TOR thresholds needs further evaluation in larger samples.
(Less)
- author
- Atceken, Zeynep ; Celik, Yeliz ; Atasoy, Cetin and Peker, Yüksel LU
- organization
- publishing date
- 2024-11
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, chest CT, COVID-19, OSAS
- in
- Journal of Clinical Medicine
- volume
- 13
- issue
- 21
- article number
- 6415
- publisher
- MDPI AG
- external identifiers
-
- pmid:39518554
- scopus:85208543008
- ISSN
- 2077-0383
- DOI
- 10.3390/jcm13216415
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2024 by the authors.
- id
- 47cd6778-84d1-4945-8b81-0e209e1622b2
- date added to LUP
- 2025-01-14 14:08:10
- date last changed
- 2025-07-02 04:19:54
@article{47cd6778-84d1-4945-8b81-0e209e1622b2,
abstract = {{<p>Background: We have previously demonstrated that high-risk obstructive sleep apnea (HR-OSA), based on a modified Berlin Questionnaire (mBQ), is linked to worse clinical outcomes. Chest computed tomography (CT) imaging with the implementation of an artificial intelligence (AI) analysis program has been a valuable tool for the speedy assessment of huge numbers of patients during the COVID-19 epidemic. In the current study, we addressed how the severity of AI-guided, CT-based total opacity ratio (TOR) scores are associated with high-risk OSA and short-term outcomes in the same cohort. Methods: The ratio of the volume of high opacity areas to that of the total lung volume constituted the TOR. We arbitrarily applied thresholds of <5 (no or mild TOR), ≥5 and <15 (moderate TOR), and ≥15 (severe TOR). Results: In total, 221 patients were included. HR-OSA was observed among 11.0% of the no or mild TOR group, 22.2% of the moderate TOR group, and 38.7% of the severe TOR group (p < 0.001). In a logistic regression analysis, HR-OSA was associated with a severe TOR with an adjusted odds ratio of 3.06 (95% confidence interval [CI] 1.27–7.44; p = 0.01). A moderate TOR predicted clinical worsening with an adjusted hazard ratio (HR) of 1.93 (95% CI 1.00–3.72; p = 0.05) and a severe TOR predicted worsening with an HR of 3.06 (95% CI 1.56–5.99; p = 0.001). Conclusions: Our results offer additional radiological proof of the relationship between HR-OSA and worse outcomes in patients with COVID-19 pneumonia. A TOR may also potentially indicate the individuals that are at higher risk of HR-OSA, enabling early intervention and management strategies. The clinical significance of TOR thresholds needs further evaluation in larger samples.</p>}},
author = {{Atceken, Zeynep and Celik, Yeliz and Atasoy, Cetin and Peker, Yüksel}},
issn = {{2077-0383}},
keywords = {{artificial intelligence; chest CT; COVID-19; OSAS}},
language = {{eng}},
number = {{21}},
publisher = {{MDPI AG}},
series = {{Journal of Clinical Medicine}},
title = {{Association of High-Risk Obstructive Sleep Apnea with Artificial Intelligence-Guided, CT-Based Severity Scores in Patients with COVID-19 Pneumonia}},
url = {{http://dx.doi.org/10.3390/jcm13216415}},
doi = {{10.3390/jcm13216415}},
volume = {{13}},
year = {{2024}},
}