Phenotyping from Polysomnography to Predict Sleep Apnea Response to Oral Appliance Therapy

Project: Research


  • Daniel Vena (PI)


Obstructive sleep apnea (OSA) is a common disorder associated with serious cardio-metabolic outcomes that is undertreated due to few therapeutic options beyond continuous positive airway pressure (CPAP). Oral appliance therapy is an alternative for treating OSA and improving cardiovascular outcomes but achieves a complete response in only half of unselected OSA patients. Clinicians often rely on demographics and OSA severity to select patients for oral appliances, which have little predictive value. We have shown that pathophysiological (endotypic) traits causing OSA hold the key to determining response to oral appliance therapy. Most recently, we identified that a simple model including measures of site (palate-based obstruction) and pharyngeal collapsibility accurately predicts responders to oral appliances (80% accurate, n=81). While highly promising, important next steps are to: 1) improve model performance by using a larger dataset; and 2) rigorous prospective validation. Therefore, the aim of this study is to develop and validate a model that uses PSG-derived endotypic traits to predict oral appliance treatment efficacy. We hypothesize that traits included in the preliminary model (palate-based obstruction and collapsibility), and other traits known to affect oral appliance treatment (ventilatory control instability and tongue-base obstruction) can predict oral appliance treatment efficacy. The model will be developed using a large sample (n=200) of retrospective data to predict oral appliance treatment response (>50% reduction in AHI from baseline plus a treatment AHI
Award amount$131,356.00
Award date01/01/2020
Program typePostdoctoral Fellowship
Award ID20POST35210530
Effective start/end date01/01/202012/31/2021