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Obstructive sleep apnea (OSA) is characterized by intermittent collapse of the upper airway during sleep resulting in an abnormal sleep pattern and drops in oxygen concentration. It affects up to 50% of obese children and can be considered as one of obesity's most important complications. It results in neurocognitive impairment but can also augment the obesity-related cardiovascular morbidity. Therefore, a correct treatment is mandatory. Adenotonsillectomy, the classical first line treatment, has a success percentage of only 50%. This implies that 50% of obese children with OSA are at risk of being exposed to unnecessary surgery. The aim of this research project is to identify markers that could predict the outcome of this surgery in obese children with OSA. In a first study, we will identify markers that correlate with the severity of OSA in these children. More classical markers include for instance body mass index, neck circumference, tonsil size, etc. We will also use a more innovative approach with parameters obtained from ultra low dose CT-scanning and functional imaging methods (computational fluid dynamics) to describe more detailed physical characteristics of the airway (volume, cross sectional area, resistance). Second, we will identify markers that predict the success of treatment. Finally, we will apply virtual surgery on these images to determine if a specific child will benefit from surgery.
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