Identifying predictors to optimize treatment outcomes in patients with obstructive sleep apnea

Abstract

This dissertation explored ways to predict which patients are at high risk for obstructive sleep apnea (OSA) and how different treatments like Continuous Positive Airway Pressure (CPAP), oral appliance treatment (OAT), and upper airway stimulation (UAS) may work for individual patients. Identifying risk factors for OSA and predictors of treatment success can aid in improving tailored therapies and reducing unnecessary procedures.

The research examined various screening tools to identify high-risk patients, such as the NoSAS score and the STOP-Bang questionnaire. Additionally, it investigated factors influencing the effectiveness of treatments like CPAP, OAT, and UAS, including anatomical airway characteristics and responses to specific maneuvers during diagnostic sleep studies.

The dissertation underscores the importance of personalized treatments for OSA to enhance patient adherence and satisfaction. Future research should focus on validating and optimizing predictive factors and exploring new methods, such as biomarkers, for assessing OSA and monitoring treatment response.