Objective Pharyngeal Phenotyping in Obstructive Sleep Apnea With High‐Resolution Manometry

Abstract

AbstractObjectiveDrug‐induced sleep endoscopy (DISE) is a commonly used diagnostic tool for surgical procedural selection in obstructive sleep apnea (OSA), but it is expensive, subjective, and requires sedation. Here we present an initial investigation of high‐resolution pharyngeal manometry (HRM) for upper airway phenotyping in OSA, developing a software system that reliably predicts pharyngeal sites of collapse based solely on manometric recordings.Study DesignProspective cross‐sectional study.SettingAn academic sleep medicine and surgery practice.MethodsForty participants underwent simultaneous HRM and DISE. A machine learning algorithm was constructed to estimate pharyngeal level‐specific severity of collapse, as determined by an expert DISE reviewer. The primary outcome metrics for each level were model accuracy and F1‐score, which balances model precision against recall.ResultsDuring model training, the average F1‐score across all categories was 0.86, with an average weighted accuracy of 0.91. Using a holdout test set of 9 participants, a K‐nearest neighbor model trained on 31 participants attained an average F1‐score of 0.96 and an average accuracy of 0.97. The F1‐score for prediction of complete concentric palatal collapse was 0.86.ConclusionOur findings suggest that HRM may enable objective and dynamic mapping of the pharynx, opening new pathways toward reliable and reproducible assessment of this complex anatomy in sleep.