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Published on: November 11, 2020
Age and gender based variations in upper pharyngeal airway space: a retrospective study on the Upper Egypt population
Dina Gamil Anis1, Rania Yousif ElMasry1, Maha Eshaq Amer1
1Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Minia University, Minia 61111, Egypt.
Objectives:
Upper airway assessment is crucial due to its impact on craniofacial growth and airway-related disorders. This retrospective observational cross-sectional study aimed to evaluate the upper pharyngeal airway (UPA) space volume and minimum cross-sectional area (MCSA) in a normal sample from Upper Egypt population, analysing age and gender-related variations using cone-beam computed tomography.
Methods:
Ninety cone beam computed tomography scans of patients aged 18-60 years were evaluated. The sample was divided into 3 groups (Group I: ≤20 years, Group II: 21-40 years, and Group III: >40 years). Each group was further divided into 2 subgroups according to gender. Nasopharyngeal, oropharyngeal, hypopharyngeal, and total airway volumes, as well as the MCSA, were calculated using Dolphin 3D software. Two-way ANOVA with Bonferroni post hoc test was used for statistical analysis.
Results:
The findings revealed significant gender differences. Males exhibited larger airway dimensions across all age groups except for nasopharyngeal volumes in adults over 20 years. A decline in airway volumes and MCSA in individuals over 40 years. The most constricted airway area predominantly occurred in the oropharynx. A strong positive correlation exists between MCSA and airway volumes. Very good inter- and intra-observer agreement.
Conclusions:
Upper pharyngeal airway dimensions in a sample of the Upper Egyptian population are influenced by age and gender, highlighting the need for age and gender specific considerations in the diagnosis and treatment.
Advances In Knowledge:
This study is the first to evaluate age and gender-related variations in UPA volume within the Upper Egypt population. Also, it introduces a discriminant model that provides a probabilistic classification of the site of MCSA with 62.2% accuracy.
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