Predicting sleep apnea responses to oral appliance therapy using polysomnographic airflow. Issue 7 (10th February 2020)

• Record Type: Journal Article
• Authors: Vena, Daniel; Azarbarzin, Ali; Marques, Melania; Op de Beeck, Sara; Vanderveken, Olivier M; Edwards, Bradley A; Calianese, Nicole; Hess, Lauren B; Radmand, Reza; Hamilton, Garun S; Joosten, Simon A; Taranto-Montemurro, Luigi; Kim, Sang-Wook; Verbraecken, Johan; Braem, Marc; White, David P; Sands, Scott A; Wellman, Andrew
• Is Part Of: Sleep. Volume 43:Issue 7(2020)
• Publication Date: 2020-02-10
• Subjects: OSA -- oral appliance therapy -- upper airway; 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/zsaa004
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Study Objectives: Oral appliance therapy is an increasingly common option for treating obstructive sleep apnea (OSA) in patients who are intolerant to continuous positive airway pressure (CPAP). Clinically applicable tools to identify patients who could respond to oral appliance therapy are limited. Methods: Data from three studies ( N = 81) were compiled, which included two sleep study nights, on and off oral appliance treatment. Along with clinical variables, airflow features were computed that included the average drop in airflow during respiratory events (event depth) and flow shape features, which, from previous work, indicates the mechanism of pharyngeal collapse. A model was developed to predict oral appliance treatment response (>50% reduction in apnea–hypopnea index [AHI] from baseline plus a treatment AHI <10 events/h). Model performance was quantified using (1) accuracy and (2) the difference in oral appliance treatment efficacy (percent reduction in AHI) and treatment AHI between predicted responders and nonresponders. Results: In addition to age and body mass index (BMI), event depth and expiratory "pinching" (validated to reflect palatal prolapse) were the airflow features selected by the model. Nonresponders had deeper events, "pinched" expiratory flow shape (i.e. associated with palatal collapse), were older, and had a higher BMI. Prediction accuracy was 74% and treatment AHI was lower in predicted responders compared to nonresponders by a clinically meaningful margin (8.0 [5.1 to 11.6] vs. 20.0 [12.2 to 29.5] events/h, p < 0.001). Conclusions: A model developed with airflow features calculated from routine polysomnography, combined with age and BMI, identified oral appliance treatment responders from nonresponders. This research represents an important application of phenotyping to identify alternative treatments for personalized OSA management.