Upper Airway Areas, Volumes, and Linear Measurements Determined on Computed Tomography During Different Phases of Respiration Predict the Presence of Severe Obstructive Sleep Apnea
| Autor: | Navarat Apirakkittikul, Witaya Sungkarat, Thongchai Bhongmakapat, Khaisang Chousangsuntorn, Jiraporn Laothamatas, Nucharin Supakul |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Logistic regression 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests Respiratory disturbance index medicine Humans Prospective Studies Expiration 030223 otorhinolaryngology Aged Sleep Apnea Obstructive business.industry Sleep apnea Cone-Beam Computed Tomography Middle Aged Thailand medicine.disease Confidence interval Obstructive sleep apnea Otorhinolaryngology Predictive value of tests Breathing Pharynx Female Surgery Oral Surgery Nuclear medicine business 030217 neurology & neurosurgery |
| Zdroj: | Journal of Oral and Maxillofacial Surgery. 76:1524-1531 |
| ISSN: | 0278-2391 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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