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

Christianne Veugen

    Research output: ThesisDoctoral thesis 1 (Research UU / Graduation UU)

    8 Downloads (Pure)

    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.
    Original languageEnglish
    Awarding Institution
    • University Medical Center (UMC) Utrecht
    Supervisors/Advisors
    • Stokroos, Robert, Primary supervisor
    • Copper, Marcel P., Co-supervisor
    Award date4 Apr 2024
    Place of PublicationUtrecht
    Publisher
    Print ISBNs978-94-6483-802-2
    DOIs
    Publication statusPublished - 4 Apr 2024

    Keywords

    • obstructive sleep apnea
    • drug-induced sleep endoscopy
    • continuous positive airway pressure
    • oral appliance treatment
    • upper airway stimulation

    Fingerprint

    Dive into the research topics of 'Identifying predictors to optimize treatment outcomes in patients with obstructive sleep apnea'. Together they form a unique fingerprint.

    Cite this