Computational Modeling of Airway Obstruction in Sleep Apnea in Down Syndrome

Abstract

Current treatment options are successful in 40% to 60% of children with persistent obstructive sleep apnea after adenotonsillectomy. Residual obstruction assessments are largely subjective and do not clearly define multilevel obstruction. We endeavor to use computational fluid dynamics to perform virtual surgery and assess airflow changes in patients with Down syndrome and persistent obstructive sleep apnea. Three-dimensional airway models were reconstructed from respiratory-gated computed tomography and magnetic resonance imaging. Virtual surgeries were performed on 10 patients, mirroring actual surgeries. They demonstrated how surgical changes affect airflow resistance. Airflow and upper airway resistance was calculated from computational fluid dynamics. Virtual and actual surgery outcomes were compared with obstructive apnea-hypopnea index values. Actual surgery successfully treated 6 of 10 patients (postoperative obstructive apnea-hypopnea index <5). In 8 of 10 subjects, both apnea-hypopnea index and the calculated upper airway resistance after virtual surgery decreased as compared with baseline values. This is a feasibility and proof-of-concept study. Further studies are needed before using these techniques in surgical planning.