Innovative imaging techniques to predict treatment outcome in pediatric obstructive sleep apnea.

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 children with specific risk factors including obesity and Down syndrome. It results in neurocognitive impairment but can also augment for instance the obesity-related cardiovascular morbidity. Therefore, a correct treatment is mandatory. Adenotonsillectomy, the classical first line treatment, has a success percentage of only 50% or less. This implies that 50% of these 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 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 CT-scanning and functional imaging methods to describe more detailed physical characteristics of the airway. Second, we will identify markers that predict the success of treatment. Finally, we will introduce an individualized approach by selecting a treatment a priori based on the airway characteristics of a specific patient. We will also use virtual surgery to determine if a specific child will benefit from surgery

Keywords:IMAGING, SLEEP APNEA

Disciplines:Morphological sciences, Physiology, Respiratory medicine