Predicting sleep apnea responses to oral appliance therapy using polysomnographic airflow

Abstract

Study objectives: Oral appliance therapy is an increasingly common option for treating obstructive sleep apnea (OSA) in patients who are intolerant to continuous positive airway pressure (CPAP). Clinically applicable tools to identify patients who could respond to oral appliance therapy are limited.

Methods: Data from three studies (N = 81) were compiled, which included two sleep study nights, on and off oral appliance treatment. Along with clinical variables, airflow features were computed that included the average drop in airflow during respiratory events (event depth) and flow shape features, which, from previous work, indicates the mechanism of pharyngeal collapse. A model was developed to predict oral appliance treatment response (>50% reduction in apnea-hypopnea index [AHI] from baseline plus a treatment AHI <10 events/h). Model performance was quantified using (1) accuracy and (2) the difference in oral appliance treatment efficacy (percent reduction in AHI) and treatment AHI between predicted responders and nonresponders.

Results: In addition to age and body mass index (BMI), event depth and expiratory "pinching" (validated to reflect palatal prolapse) were the airflow features selected by the model. Nonresponders had deeper events, "pinched" expiratory flow shape (i.e. associated with palatal collapse), were older, and had a higher BMI. Prediction accuracy was 74% and treatment AHI was lower in predicted responders compared to nonresponders by a clinically meaningful margin (8.0 [5.1 to 11.6] vs. 20.0 [12.2 to 29.5] events/h, p < 0.001).

Conclusions: A model developed with airflow features calculated from routine polysomnography, combined with age and BMI, identified oral appliance treatment responders from nonresponders. This research represents an important application of phenotyping to identify alternative treatments for personalized OSA management.

Keywords: OSA; oral appliance therapy; upper airway.

Figure 1.

Representative data from two subjects, one with deep events (A) and another with shallow events (B), to facilitate the explanation of calculating event depth from airflow. The calculation was performed by (i) identifying all scored respiratory events (apneas and hypopneas), (ii) calculating a “ventilation profile” for each event as the uncalibrated volume × respiratory rate, expressed as a percentage of eupneic [mean] ventilation, (iii) aligning the ventilation profiles for each event at event termination (dashed vertical line) and then ensemble averaging. Panel (iv) of A and B illustrates the ensemble-averaged ventilation profile (thick black line) for each subject, superimposed over the ventilation profiles for all respiratory events (thin gray lines). Event depth is calculated as the mean reduction (from eupnea) in ensemble-averaged ventilation during the event.

Figure 2.

The predictive model illustrated in two dimensions (*adjusted for age and BMI) accurately separated treatment responders from nonresponders. Patients with more pinched expirations and deeper events (bottom right region, shaded red) were less likely to respond to oral appliances (red circles). Shallow events without expiratory pinching (top left region, shaded green) were more likely to respond (green circles).

Figure 3.

Flow trace from two representative patients (A) with expiratory pinching and (B) without expiratory pinching, which look like a normal expiration. Above the flow trace in A is a profile view of the pharynx illustrating a normal inspiration and an expiration with palatal prolapse whereby the palate flips up to block the nasopharynx causing air to be shunted out of the mouth. This appears in the flow shape as a sudden drop in expiratory flow. On the right are enlarged breaths from A and B with annotations of time durations at or above 90% of peak expiratory flow (t₉₀). Flatness is calculated as the ratio of t₉₀ to total expiratory time (t_exp).

Figure 4.

The model developed in the present study (red circle) outperforms previously published models that used predictors acquired from clinical data and PSG (gray circles), and drug-induced sleep endoscopy (DISE, gray triangles); and performed comparably to past models that used predictors acquired from awake endoscopy (AE, gray triangles).