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

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.

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 (n_total=466).

CCCp was characterised by greater scoopiness (β=1.5±0.6 per 2sd, multivariable estimate±se) and skewness (β=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.

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.