OBJECTIVE: We aim to investigate if anatomical and functional properties of the upper airway using computerized 3D models derived from computed tomography (CT) scans better predict obstructive sleep apnea (OSA) severity than standard clinical markers. METHODS: Consecutive children with suspected OSA underwent polysomnography, clinical assessment of upper airway patency, and a CT scan while awake. A three-dimensional (3D) reconstruction of the pharyngeal airway was built from these images, and computational fluid dynamics modeling of low inspiratory flow was performed using open-source software. RESULTS: Thirty-three children were included (23 boys; mean age, was 6.0±3.2y). OSA was diagnosed in 23 patients. Children with OSA had a significantly lower volume of the overlap region between tonsils and the adenoids (median volume, 1408 mm compared to 2173 mm; p=0.04), a lower mean cross-sectional area at this location (median volume, 69.3mm(2) compared to 114.3mm2; p=0.04), and a lower minimal cross-sectional area (median volume, 17.9 mm2 compared to 25.9 mm2; p=0.05). Various significant correlations were found between several imaging parameters and the severity of OSA, most pronounced for upper airway conductance (r=-0.46) (p<0.01) for correlation between upper airway conductance and the apnea-hypopnea index. No differences or significant correlations were observed with clinical parameters of upper airway patency. Preliminary data after treatment showed that none of the patients with residual OSA had their smallest cross-sectional area located in segment 3, and this frequency was significantly lower than in their peers whose sleep study normalized (64%; p=0.05). CONCLUSION: Functional imaging parameters are highly correlated with OSA severity and are a more powerful correlate than clinical scores of upper airway patency. Preliminary data also showed that we could identify differences in the upper airway of those subjects who did not benefit from a local upper airway treatment.
OBJECTIVE: We aim to investigate if anatomical and functional properties of the upper airway using computerized 3D models derived from computed tomography (CT) scans better predict obstructive sleep apnea (OSA) severity than standard clinical markers. METHODS: Consecutive children with suspected OSA underwent polysomnography, clinical assessment of upper airway patency, and a CT scan while awake. A three-dimensional (3D) reconstruction of the pharyngeal airway was built from these images, and computational fluid dynamics modeling of low inspiratory flow was performed using open-source software. RESULTS: Thirty-three children were included (23 boys; mean age, was 6.0±3.2y). OSA was diagnosed in 23 patients. Children with OSA had a significantly lower volume of the overlap region between tonsils and the adenoids (median volume, 1408 mm compared to 2173 mm; p=0.04), a lower mean cross-sectional area at this location (median volume, 69.3mm(2) compared to 114.3mm2; p=0.04), and a lower minimal cross-sectional area (median volume, 17.9 mm2 compared to 25.9 mm2; p=0.05). Various significant correlations were found between several imaging parameters and the severity of OSA, most pronounced for upper airway conductance (r=-0.46) (p<0.01) for correlation between upper airway conductance and the apnea-hypopnea index. No differences or significant correlations were observed with clinical parameters of upper airway patency. Preliminary data after treatment showed that none of the patients with residual OSA had their smallest cross-sectional area located in segment 3, and this frequency was significantly lower than in their peers whose sleep study normalized (64%; p=0.05). CONCLUSION: Functional imaging parameters are highly correlated with OSA severity and are a more powerful correlate than clinical scores of upper airway patency. Preliminary data also showed that we could identify differences in the upper airway of those subjects who did not benefit from a local upper airway treatment.
Authors: Haiyan Luo; Sanghun Sin; Joseph M McDonough; Carmen R Isasi; Raanan Arens; David M Wootton Journal: J Biomech Date: 2014-03-24 Impact factor: 2.712
Authors: Monique A L J Slaats; Dieter Loterman; Cedric van Holsbeke; Wim Vos; Kim Van Hoorenbeeck; Jan de Backer; Wilfried de Backer; Marek Wojciechowski; An Boudewyns; Stijn Verhulst Journal: J Clin Sleep Med Date: 2018-04-15 Impact factor: 4.062
Authors: W M Faizal; N N N Ghazali; C Y Khor; Irfan Anjum Badruddin; M Z Zainon; Aznijar Ahmad Yazid; Norliza Binti Ibrahim; Roziana Mohd Razi Journal: Comput Methods Programs Biomed Date: 2020-06-26 Impact factor: 5.428