Obstructive sleep apnea (OSA) is usually diagnosed from a single night of home sleep apnea testing using the apnea-hypopnea index (AHI). However, the AHI varies substantially from night to night, undermining diagnostic accuracy, and shows only modest correlation with symptoms. This variability further limits its usefulness for predicting cardiovascular and other complications. Besides the traditional AHI, more robust physiological markers are needed. Several emerging physiological metrics - hypoxic burden, ventilatory burden, heart rate variability, autonomic arousals, and the pulse wave amplitude drop index - capture the physiological impact of OSA more comprehensively and demonstrate stronger associations with cardiovascular risk. Despite this promise, their night-to-night variability has not been studied. A systematic evaluation of both established and novel OSA metrics across nights is essential to identify reliable, stable parameters suitable for clinical routine. This improves diagnostic precision beyond what traditional metrics can provide, enhances patient selection, reduces costs and patient harm, and may improve treatment outcomes.
Study Type
OBSERVATIONAL
Enrollment
192
Inselspital University Hospital and University Bern
Bern, Switzerland
RECRUITINGNight-to-night variability of apnea-hypopnea index (events per hour of sleep) over 4 nights
The variability of the apnea-hypopnea index (events per hour of sleep) over 4 nights will be quantified using linear mixed-effects models, accounting for confounding variables.
Time frame: 4 nights of respiratory polygraphy
Night-to-night variability of oxygen desaturation index (events per hour of sleep) over 10 nights
The variability will be quantified using linear mixed-effects models, accounting for confounding variables.
Time frame: 4 nights of respiratory polygraphy and 10 nights of oxymetry
Night-to-night variability of hypoxic burden (minute x percent per hour of sleep) over 10 nights
The variability will be quantified using linear mixed-effects models, accounting for confounding variables.
Time frame: 4 nights of respiratory polygraphy and 10 nights of oxymetry
Night-to-night variability of ventilatory burden over 4 nights
The variability will be quantified using linear mixed-effects models, accounting for confounding variables. Ventilatory burden will be calculated according to Parekh et al.
Time frame: 4 nights of respiratory polygraphy
Night-to-night variability of heart rate variability over 10 nights
The variability will be quantified using linear mixed-effects models, accounting for confounding variables.
Time frame: 4 nights of respiratory polygraphy and 10 nights of oxymetry
Night-to-night variability of pulse wave amplitude drops (events per hour) over 10 nights
The variability will be quantified using linear mixed-effects models, accounting for confounding variables.
Time frame: 4 nights of respiratory polygraphy and 10 nights of oxymetry
Identification of factors contributing to and explaining variability
Each influencing factor will be evaluated on its potential to explain the observed variability in the objective physiological parameters listed above. Each factor will be included individually as a fixed effect in the mixed-effects model and tested for significance. The following factors will be analyzed: * Age (years) * Body mass index (kg(m2) * Gender * Upper airway anatomy (tonsil size) * Mean OSA severity over all nights (of the respective parameter) * Sleep position (supine time as a percent of sleep time) * Alcohol consumption (number of drinks) * Caffeine intake (number of cups) * Nicotine use (number of packages) * Sleep Medication (free text) * Subjective sleep quality (visual analog scale, VAS, 0 to 10 points) * Snoring intensity (VAS, 0 to 10 points) * Daytime sleepiness on a VAS of the subsequent day (0 to 10 points) * Daytime sleepiness on the Epworth Sleepiness Scale of the subsequent day (0 to 24 points) * Respiratory symptoms such as infection (VAS) * Allergy symptoms
Time frame: 4 nights of respiratory polygraphy and 10 nights of oxymetry
Correlation between physiological parameters from sleep testing and patient-reported outcome measures (PROMs)
Associations between physiological metrics and PROMs will be assessed using mixed-effects models, analogously to the analysis of influencing factors above and correlation analysis (Spearman's rank coefficient). Patient-reported symptoms for correlation analyses: * Sleep quality on a VAS (0 to 10 points) * Snoring intensity on a VAS (0 to 10 points) * Daytime sleepiness on a VAS (0 to 10 points) * Epworth Sleepiness Scale (0 to 24 points)
Time frame: 4 nights of respiratory polygraphy and 10 nights of oxymetry
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