BACKGROUND: Most individuals with OSA remain undiagnosed, mainly due to limited access to effective screening tools and diagnostic facilities. Therefore, the objective of this study was to identify craniofacial and anthropometric measurements that predict OSA in an Indian population. METHODS AND FINDINGS: Male subjects (n = 76) between 25 and 50 years of age were recruited for the study from the general population. The study measures consisted of home-based type IV polysomnography and a total of 40 anthropometric and craniofacial measurements. Key measures were identified, and a model was developed with these variables, which predicted the presence of OSA with a sensitivity, specificity and overall accuracy of 93.1, 20.0 and 74.4%, respectively. CONCLUSION: This preliminary study shows the utility of craniofacial and anthropometric variables in the identification of individuals at risk of OSA. These findings need to be further validated against the results of overnight polysomnography in a large independent population.
BACKGROUND: Most individuals with OSA remain undiagnosed, mainly due to limited access to effective screening tools and diagnostic facilities. Therefore, the objective of this study was to identify craniofacial and anthropometric measurements that predict OSA in an Indian population. METHODS AND FINDINGS: Male subjects (n = 76) between 25 and 50 years of age were recruited for the study from the general population. The study measures consisted of home-based type IV polysomnography and a total of 40 anthropometric and craniofacial measurements. Key measures were identified, and a model was developed with these variables, which predicted the presence of OSA with a sensitivity, specificity and overall accuracy of 93.1, 20.0 and 74.4%, respectively. CONCLUSION: This preliminary study shows the utility of craniofacial and anthropometric variables in the identification of individuals at risk of OSA. These findings need to be further validated against the results of overnight polysomnography in a large independent population.
Entities:
Keywords:
Berlins questionnaire; Mallampati score; Obstructive sleep apnea; anthropometry; craniofacial; logistic regression analysis; thyromental angle; type IV polysomnography
Authors: Nathalia Carolina Fernandes Fagundes; Terry Carlyle; Oyku Dalci; M Ali Darendeliler; Ida Kornerup; Paul W Major; Andrée Montpetit; Benjamin T Pliska; Stacey Quo; Giseon Heo; Carlos Flores Mir Journal: J Clin Sleep Med Date: 2022-01-01 Impact factor: 4.062