Overview
The study is designed to develop models to predict site of collapse information using routine polysomnography. In an observational study, the investigators will study 1000 patients with obstructive sleep apnea (OSA) who have had a recent polysomnographic study and will undergo drug-induced sleep endoscopy (DISE) as part of their standard clinical care. Flow shape information from the polysomnographic study will be associated with the DISE results, and a set of prediction models will be developed and validated to detect the site, pattern and degree of upper airway collapse as seen during DISE.
Description
The site, pattern and degree of upper airway collapse is associated with the outcome of different OSA therapies. In current clinical practice, this information is assessed during DISE, in which the upper airway is evaluated during a light sedation, mimicking natural sleep. Information on the site of collapse is not currently available from routine clinical sleep studies.
Recent research has shown that the site of upper airway collapse seen during endoscopy can be recognized from the distinct airflow shape patterns. For example, greater "negative effort dependence" (NED) or inspiratory scooping is associated with non-tongue-base sites of collapse. In preliminary analysis using >150 patients, the investigators recently developed a logistic regression model predicting complete concentric collapse at the level of the palate (CCCp). The model included 6 meaningful airflow features (scoopiness NED, inspiratory skewness, peak flow in early inspiration, inspiratory volume in the first 3rd of inspiration, inspiratory rise time, peak volume). Each feature is calculated as the mean value during identified hypopneas. Other sites of collapse were also assessed using the same six features in separate models (lateral walls, tongue-base, epiglottis).
The study will proceed in two phases of development. In the first phase, the investigators will prospectively validate the preliminary model for predictive CCCP in 300 patients (primary outcome test for phase I). Odds for true CCCP in the predicted CCCP subgroup will be compared against the odds for CCCP in the predicted non-CCCP subgroup (Fisher exact test). Secondary analyses will validate the other three model (lateral walls, tongue-base, epiglottis).
Prior to the second phase, the investigators will develop a refined model to predict CCCP using all available pooled data (N>450). The investigators will seek to include new parameters or incorporate additional interactions between predictors, where appropriate, Refined models for other sites will also be developed. Sensitivity analyses will examine different patterns of collapse (e.g. anterior-posterior), different severities (partial vs complete), how to optimally account for multi-site obstruction, and whether predicted sites are correlated.
In the second phase, the investigators will prospectively apply the refined model to N=700 patients, following the same analytic approach as for phase 1.
Interim analysis will be performed but the study will not be terminated for early success).
Patients will undergo therapies per clinical indication and will be documented; In the entire dataset, the investigators will explore whether the presence of CCCP (per model prediction, and per DISE results) is associated with reduced efficacy of oral appliances, hypoglossal nerve stimulation, positional therapy, greater efficacy of upper airway surgery, greater CPAP pressure requirement, reduced CPAP efficacy, and reduced CPAP adherence. Associations wlil also be performed using other predicted and actual site of collapse information.
Eligibility
Inclusion Criteria:
- 18 years or older.
- Diagnosis with OSA (AHI ≥ 5).
- Eligible for drug-induced sleep endoscopy as the next step in the clinical path for OSA.
- Capable of giving informed consent.
Exclusion Criteria:
- Diagnostic polysomnography data not available at the Antwerp University Hospital.
- Factors indicative of unstable clinical status or would preclude DISE investigation:
- Medication use related to sleeping disorders.
- Central Sleep Apnea Syndrome.
- Medical history of known causes of tiredness by day or severe sleep disruption (insomnia, PLMS, Narcolepsy).
- Seizure disorders.
- Known medical history of intellectual disability, memory disorders or current psychiatric disorders (psychotic illness, major depression, or acute anxiety attacks as mentioned by the patient).
- Pregnancy or willing to become pregnant