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Objective/background: Surgical interventions for obstructive sleep apnea (OSA) are less effective in obese than in normal-weight children. However, the mechanisms that underpin this relationship are not fully understood. Therefore, this study aimed to explore how body weight influences upper airway collapse and treatment outcome in children with OSA., Methods: We conducted a retrospective analysis of prospectively collected data on polysomnography, drug-induced sleep endoscopy (DISE), and treatment outcome in otherwise healthy children with OSA. Associations between body mass index (BMI) z-score and upper airway collapse during DISE were assessed using logistic regression modelling. Treatment success was defined as obstructive apnea-hypopnea index (oAHI) < 5 events/hour and cure as oAHI < 2 events/hour with obstructive apnea index < 1 event/hour., Results: A total of 139 children were included [median (Q1─Q3); age 4.5 (3.1─8.4) years; BMI z-score 0.3 (-0.8 to 1.4); oAHI 10.8 (6.8─18.0) events/hour]. Twenty-five of them were overweight and 21 were obese. After adjusting for age and history of upper airway surgery, BMI z-score was significantly correlated with circumferential upper airway collapse during DISE (odds ratio 1.67; 95% confidence interval 1.12─2.65; P = 0.011). Outcome of DISE-directed treatment was similar in normal-weight (success: 91.4%; cure: 78.5%), overweight (success: 88.0%; cure: 80.0%), and obese (success: 90.5%; cure: 76.5%) children. Children with circumferential collapse responded better to continuous positive airway pressure than to (adeno)tonsillectomy., Conclusion: Increasing body weight is associated with circumferential upper airway collapse during DISE and, accordingly, may require treatment strategies other than (adeno)tonsillectomy., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Oral appliance therapy with a mandibular advancement device (OAm) can yield to complete therapeutic response (apnea-hypopnea index [AHI]<5 events/h), though some patients show little or no improvement in daytime sleepiness. The prevalence of residual excessive sleepiness (RES) despite effective treatment with OAm therapy is unknown. We aimed to determine the prevalence of RES in patients treated with a titratable custom-made duobloc OAm., Methods: A prevalence study was performed, collecting data from 185 patients with an established diagnosis of sleep-disordered breathing (SDB) under OAm therapy with a titratable custom-made duobloc device (baseline data were male:female ratio, 129:56; age, 48±9 years; body mass index [BMI], 27±4 kg/m2; Epworth Sleepiness Scale [ESS] score, 10±5; and AHI, 19±12 events/h). A full-night polysomnography was performed at baseline and after 3 months of OAm therapy. Daytime sleepiness was assessed using the ESS with RES defined as an ESS score of 11 or higher out of 24, despite complete therapeutic response., Results: Out of 185 patients, 84 patients (45%) showed a complete therapeutic response with an AHI of <5 events per hour after 3 months of OAm therapy. Despite this normalization of AHI, 27 out of these 84 patients (32%) showed RES and had a significantly higher baseline ESS (15±4 vs. 9±4; P<.001) and were younger (43±9 vs. 47±9; P=.028) compared to patients without RES., Conclusion: RES under OAm therapy showed a prevalence of up to 32% in SDB patients effectively treated with respect to AHI. Patients with RES were younger and had higher baseline daytime sleepiness., (Copyright © 2013 Elsevier B.V. All rights reserved.)

The obstructive sleep apnea-hypopnea syndrome (OSAHS) is a sleep related breathing disorder. A popular treatment is the use of a mandibular repositioning appliance (MRA) which advances the mandibula during the sleep and decreases the collapsibility of the upper airway. The success rate of such a device is, however, limited and very variable within a population of patients. Previous studies using computational fluid dynamics have shown that there is a decrease in upper airway resistance in patients who improve clinically due to an MRA. In this article, correlations between patient-specific anatomical and functional parameters are studied to examine how MRA induced biomechanical changes will have an impact on the upper airway resistance. Low-dose computed tomography (CT) scans are made from 143 patients suffering from OSAHS. A baseline scan and a scan after mandibular repositioning (MR) are performed in order to study variations in parameters. It is found that MR using a simulation bite is able to induce resistance changes by changing the pharyngeal lumen. The change in minimal cross-sectional area is the best parameter to predict the change in upper airway resistance. Looking at baseline values, the ideal patients for MR induced resistance decrease seem to be women with short airways, high initial resistance and no baseline occlusion., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Determination of the apnea hypopnea index (AHI) as a measure of the severity of obstructive sleep apnea/hypopnea syndrome (OSAHS) is a widely accepted methodology. However, the outcome of such a determination depends on the method used, is time consuming and insufficient for prediction of the effect of all treatment modalities. For these reasons more methods for evaluating the severity of OSAHS, based on different imaging modalities, have been looked into and recent studies have shown that anatomical properties determined from three-dimensional (3D) computed tomography (CT) images are good markers for the severity of the OSAHS. Therefore, we correlated anatomical measurements of a 3D reconstruction of the upper airway together with flow simulation results with the severity of OSAHS in order to find a combination of variables to indicate the severity of OSAHS in patients. The AHI of 20 non-selected, consecutive patients has been determined during a polysomnography. All patients also underwent a CT scan from which a 3D model of the upper airway geometry was reconstructed. This 3D model was used to evaluate the anatomical properties of the upper airway in OSAHS patients as well as to perform computational fluid dynamics (CFD) computations to evaluate the airflow and resistance of this upper airway. It has been shown that a combination of the smallest cross-sectional area and the resistance together with the body mass index (BMI) form a set of markers that predict very well the severity of OSAHS in patients within this study. We believe that these markers can be used to evaluate the outcome of an OSAHS treatment.

Mandibular advancement devices (MADs) have emerged as a popular alternative for the treatment of sleep-disordered breathing. These devices bring the mandibula forward in order to increase upper airway (UA) volume and prevent total UA collapse during sleep. However, the precise mechanism of action appears to be quite complex and is not yet completely understood; this might explain interindividual variation in treatment success. We examined whether an UA model, that combines imaging techniques and computational fluid dynamics (CFD), allows for a prediction of the treatment outcome with MADs. Ten patients that were treated with a custom-made mandibular advancement device (MAD), underwent split-night polysomnography. The morning after the sleep study, a low radiation dose CT scan was scheduled with and without the MAD. The CT examinations allowed for a comparison between the change in UA volume and the anatomical characteristics through the conversion to three-dimensional computer models. Furthermore, the change in UA resistance could be calculated through flow simulations with CFD. Boundary conditions for the model such as mass flow rate and pressure distributions were obtained during the split-night polysomnography. Therefore, the flow modeling was based on a patient specific geometry and patient specific boundary conditions. The results indicated that a decrease in UA resistance and an increase in UA volume correlate with both a clinical and an objective improvement. The results of this pilot study suggest that the outcome of MAD treatment can be predicted using the described UA model.