Polysomnographic airflow shapes and site of collapse during drug-induced sleep endoscopy.

ABSTRACT

BACKGROUND: Differences in the pharyngeal site of collapse influence efficacy of non-continuous positive airway pressure therapies for obstructive sleep apnoea (OSA). Notably, complete concentric collapse at the level of the palate (CCCp) during drug-induced sleep endoscopy (DISE) is associated with reduced efficacy of hypoglossal nerve stimulation, but CCCp is currently not recognisable using polysomnography. Here we develop a means to estimate DISE-based site of collapse using overnight polysomnography. METHODS: 182 OSA patients provided DISE and polysomnography data. Six polysomnographic flow shape characteristics (mean during hypopnoeas) were identified as candidate predictors of CCCp (primary outcome variable, n=44/182), including inspiratory skewness and inspiratory scoopiness. Multivariable logistic regression combined the six characteristics to predict clear presence (n=22) versus absence (n=128) of CCCp (partial collapse and concurrent tongue base collapse excluded). Odds ratios for actual CCCp between predicted subgroups were quantified after cross-validation. Secondary analyses examined complete lateral wall, tongue base or epiglottis collapse. External validation was performed on a separate dataset (ntotal=466). RESULTS: CCCp was characterised by greater scoopiness (ß=1.5±0.6 per 2sd, multivariable estimate±se) and skewness (ß=11.4±2.4) compared with non-CCCp. The odds ratio for CCCp in predicted positive versus negative subgroups was 5.0 (95% CI 1.9-13.1). The same characteristics provided significant cross-validated prediction of lateral wall (OR 6.3, 95% CI 2.4-16.5), tongue base (OR 3.2, 95% CI 1.4-7.3) and epiglottis (OR 4.4, 95% CI 1.5-12.4) collapse. CCCp and lateral wall collapse shared similar characteristics (skewed, scoopy), diametrically opposed to tongue base and epiglottis collapse characteristics. External validation confirmed model prediction. CONCLUSIONS: The current study provides a means to recognise patients with likely CCCp or other DISE-based site of collapse categories using routine polysomnography. Since site of collapse influences therapeutic responses, polysomnographic airflow shape analysis could facilitate precision site-specific OSA interventions.