[Analysis of CBCT-based soft tissue predictors for obstructive sleep apnea in adults]

Objective:To investigate the relationship between soft tissue measurements from cone beam computed tomography（CBCT） imaging obtained for dental treatment purposes and the severity of obstructive sleep apnea（OSA） in adults, and to evaluate the value of these soft tissue variables as alternative predictors for OSA screening. Methods:A total of 140 subjects with snoring were enrolled retrospectively based on polysomnography and CBCT examinations. Participants were categorized into a primary snoring group and an OSA group based on the apnea-hypopnea index. Upper airway soft tissue parameters were measured from the CBCT images. To analyze the associations between soft tissue variables and OSA severity, a hierarchical regression model was applied. Additionally, binary logistic regression was performed to identify independent risk factors for OSA, and receiver operating characteristic curve analysis was conducted to evaluate the predictive performance of these parameters. Results:Significant differences were observed in multiple upper airway soft-tissue parameters measured by CBCT between patients with OSA and those with primary snoring. These parameters included oropharyngeal volume, minimal cross-sectional area（mCSA）, palatal length and thickness, as well as tongue length and width. In a multivariable hierarchical regression model adjusted for demographic characteristics, only palatal length and mCSA remained statistically significant（P<0.05）. Binary logistic regression analysis further confirmed that both palatal length and mCSA were significant risk factors for OSA. Conclusion:Among the CBCT-derived soft-tissue measurements of the upper airway, palatal length and the mCSA can serve as reliable predictors of the AHI for OSA screening.