0440 A Model to Evaluate the Contribution of Pathophysiological Phenotypes to OSA Severity and Develop Simplified Approaches to Estimate the Key Phenotypic Traits that Contribute to OSA. (12th April 2019)

• Record Type: Journal Article
• Authors: Dutta, Ritaban; Delaney, Gary; Jordan, Amy; White, David; Wellman, Andrew; Eckert, Danny J
• Is Part Of: Sleep. Volume 42(2019)Supplement 1
• Page Start: A177
• Page End: A178
• Publication Date: 2019-04-12
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsz067.439
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: This study aimed to determine if a predictive model could be used to accurately classify obstructive sleep apnea (OSA) severity level (i.e. mild=AHI<15, moderate=AHI 15-30 and severe=AHI>30 events/h) using gold standard physiological measurements of the 4 pathophysiological phenotypes that contribute to OSA (Aim 1). We also investigated whether the 4 pathophysiological phenotypes can be estimated from standard anthropometric, demographic and polysomnographic variables (Aim 2). Methods: Anthropometric, demographic and polysomnography parameters from a standard diagnostic study (off CPAP) were collected in 52 CPAP-treated OSA patients. The 4 key OSA phenotypes: 1) upper-airway collapsibility (Pcrit), 2) arousal threshold, 3) loop gain and 4) pharyngeal muscle responsiveness were also quantified in these individuals using gold-standard upper airway physiology methodology on a separate night. Principal component analyses and machine learning techniques were used to address the study objectives. Results: Principal component analyses using the 4 OSA phenotypes showed linear separability with the first two components. This enabled development of a model to classify different levels of OSA severity defined according to total AHI with up to 93% accuracy (95% sensitivity, 90% specificity) from the phenotypes. In addition, using polysomnographically-derived parameters plus anthropometric and demographic variables, the model was able to predict physiologically-determined Pcrit with 80%, arousal threshold with 83%, loop gain with 57% and muscle responsiveness with 67% accuracy respectively. Conclusion: These findings indicate that OSA phenotypes are important contributors to polysomnographic defined OSA severity levels. In addition, these findings highlight the potential for routine sleep study and clinical data to estimate OSA phenotypes, which may be helpful as part of a clinical decision support system to inform targeted treatment for OSA. Support (If Any): This study was supported by a Cooperative Research Centre Project Grant, a joint Australian Government, Academia and Industry collaboration (Industry partner: Oventus Medical). Commercial in confidence until published in the Sleep 2019 abstract supplement.