Physiological Traits to Predict Osa and Response to Non-pap Therapy

961 A Physiological Model of OSA Pathogenesis—Owens et al. INTRODUCTION In addition to compromised upper airway anatomy, other physiological traits are increasingly recognized for their contributions to the development of obstructive sleep apnea (OSA). These nonanatomical traits include: (1) poor pharyngeal muscle responsiveness during sleep, (2) an overly sensitive ventilatory control system (i.e., high loop gain [LG]), and (3) a low respiratory arousal threshold that leads to arousal rather than stable ventilation.1–4 Given the variable adherence to continuous positive airway pressure (CPAP),5,6 we and others have suggested that an alternative approach for nonadherent patients would be targeted manipulation of one or more of the underlying traits that contribute to OSA pathogenesis. However, no such method or model is yet validated to predict how many patients might benefit from non-PAP therapy, or how to guide therapy in an individual patient. There are several prerequisites before such an approach to OSA management could be attempted. First, the traits would need to be measured in an easy and relatively noninvasive Study Objectives: Both anatomical and nonanatomical traits are important in obstructive sleep apnea (OSA) pathogenesis. We have previously described a model combining these traits, but have not determined its diagnostic accuracy to predict OSA. A valid model, and knowledge of the published effect sizes of trait manipulation, would also allow us to predict the number of patients with OSA who might be effectively treated without using positive airway pressure (PAP). Design, Participants and Intervention: Fifty-seven subjects with and without OSA underwent standard clinical and research sleep studies to measure OSA severity and the physiological traits important for OSA pathogenesis, respectively. The traits were incorporated into a physiological model to predict OSA. The model validity was determined by comparing the model prediction of OSA to the clinical diagnosis of OSA. The effect of various trait manipulations was then simulated to predict the proportion of patients treated by each intervention. Measurements and Results: The model had good sensitivity (80%) and specificity (100%) for predicting OSA. A single intervention on one trait would be predicted to treat OSA in approximately one quarter of all patients. Combination therapy with two interventions was predicted to treat OSA in ~50% of patients. Conclusions: An integrative model of physiological traits can be used to predict population-wide and individual responses to non-PAP therapy. Many patients with OSA would be expected to be treated based on known trait manipulations, making a strong case for the importance of nonanatomical traits in OSA pathogenesis and the effectiveness of non-PAP therapies.