Multimodal phenotypic labelling using drug-induced sleep endoscopy, awake nasendoscopy and computational fluid dynamics for the prediction of mandibular advancement device treatment outcome: a prospective study

Mandibular advancement device (MAD) treatment outcome for obstructive sleep apnea (OSA) is variable and patient dependent. A global, clinically applicable predictive model is lacking. Our aim was to combine characteristics obtained during drug-induced sleep endoscopy (DISE), awake nasendoscopy, and computed tomography scan-based computational fluid dynamic (CFD) measurements in one multifactorial model, to explain MAD treatment outcome. A total of 100 patients with OSA were prospectively recruited and treated with a MAD at fixed 75% protrusion. In all, 72 underwent CFD analysis, DISE, and awake nasendoscopy at baseline in a blinded fashion and completed a 3-month follow-up polysomnography with a MAD. Treatment response was defined as a reduction in the apnea-hypopnea index (AHI) of >= 50% and deterioration as an increase of >= 10% during MAD treatment. To cope with missing data, multiple imputation with predictive mean matching was used. Multivariate logistic regression, adjusting for body mass index and baseline AHI, was used to combine all potential predictor variables. The strongest impact concerning odds ratios (ORs) was present for complete concentric palatal collapse (CCCp) during DISE on deterioration (OR 28.88, 95% confidence interval [CI] 1.18-704.35; p = 0.0391), followed by a C-shape versus an oval shape of the soft palate during wakefulness (OR 8.54, 95% CI 1.09-67.23; p = 0.0416) and tongue base collapse during DISE on response (OR 3.29, 95% CI 1.02-10.64; p = 0.0464). Both logistic regression models exhibited excellent and fair predictive accuracy. Our findings suggest DISE to be the most robust examination associated with MAD treatment outcome, with tongue base collapse as a predictor for successful MAD treatment and CCCp as an adverse DISE phenotype.