Publication: Upper Airway Areas, Volumes, and Linear Measurements Determined on Computed Tomography During Different Phases of Respiration Predict the Presence of Severe Obstructive Sleep Apnea
Submitted Date
Received Date
Accepted Date
Issued Date
2018-07-01
Copyright Date
Announcement No.
Application No.
Patent No.
Valid Date
Resource Type
Edition
Resource Version
Language
File Type
No. of Pages/File Size
ISBN
ISSN
15315053
02782391
02782391
eISSN
Scopus ID
WOS ID
Pubmed ID
arXiv ID
Call No.
Other identifier(s)
2-s2.0-85041340549
Journal Title
Volume
Issue
item.page.oaire.edition
Start Page
End Page
Access Rights
Access Status
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Physical Location
Bibliographic Citation
Journal of Oral and Maxillofacial Surgery. Vol.76, No.7 (2018), 1524-1531
Citation
Khaisang Chousangsuntorn, Thongchai Bhongmakapat, Navarat Apirakkittikul, Witaya Sungkarat, Nucharin Supakul, Jiraporn Laothamatas (2018). Upper Airway Areas, Volumes, and Linear Measurements Determined on Computed Tomography During Different Phases of Respiration Predict the Presence of Severe Obstructive Sleep Apnea. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/45705.
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Upper Airway Areas, Volumes, and Linear Measurements Determined on Computed Tomography During Different Phases of Respiration Predict the Presence of Severe Obstructive Sleep Apnea
Alternative Title(s)
Author's Affiliation
Author's E-mail
Editor(s)
Editor's Affiliation
Corresponding Author(s)
Creator(s)
Compiler
Advisor(s)
Illustrator(s)
Applicant(s)
Inventor(s)
Issuer
Assignee
Series
Has Part
Abstract
© 2018 Purpose: The objective of this study was to analyze the potential of using low-dose volumetric computed tomography (CT) during different phases of respiration for identifying patients likely to have severe obstructive sleep apnea (OSA), defined as a respiratory disturbance index (RDI) higher than 30. Patients and Methods: A prospective study was undertaken at the Ramathibodi Hospital (Bangkok, Thailand). Patients with diagnosed OSA (N = 82) were recruited and separated into group 1 (RDI, ≤30; n = 36) and group 2 (RDI, >30; n = 46). The 2 groups were scanned by low-dose volumetric CT while they were 1) breathing quietly, 2) at the end of inspiration, and 3) at the end of expiration. Values for CT variables were obtained from linear measurements on lateral scout images during quiet breathing and from the upper airway area and volume measurements were obtained on axial cross-sections during different phases of respiration. All CT variables were compared between study groups. A logistic regression model was constructed to calculate a patient's likelihood of having an RDI higher than 30 and the predictive value of each variable and of the final model. Results: The minimum cross-sectional area (MCA) measured at the end of inspiration (cutoff point, ≤0.33 cm2) was the most predictive variable for the identification of patients likely to have an RDI higher than 30 (adjusted odds ratio [OR] = 5.50; 95% confidence interval [CI], 1.76-17.20; sensitivity, 74%; specificity, 72%,), followed by the MCA measured at the end of expiration (cutoff point, ≤0.21 cm2; adjusted OR = 3.28; 95% CI, 1.05-10.24; sensitivity, 70%; specificity, 68%). Conclusion: CT scanning at the ends of inspiration and expiration helped identify patients with an RDI higher than 30 based on measurement of the MCA. Low-dose volumetric CT can be a useful tool to help the clinician rapidly identify patients with severe OSA and decide on the urgency to obtain a full-night polysomnographic study and to start treatment.