Evaluation of An Optical Measurement Algorithm Combined With Patient and Provider Input to Reduce Mask Exchanges During Initial Positive Airway Pressure Therapy

Continuous positive airway pressure and non-invasive ventilation are common treatment modalities for obstructive sleep apnea, central sleep apnea, and chronic alveolar hypoventilation from a variety of causes. Use of positive airway pressure (PAP) requires use of an interface, commonly referred to as a "mask." There are a range of mask options available, differing in configuration and sizing, including masks that fit into the nostrils (nasal pillows, NP), cover the nose (nasal masks, NM), cover both the nose and the mouth (oronasal masks, ONM), and rarely those that fit into the mouth (oral masks, OM) or over the entire face. The variety of masks, sizes, and materials result from the wide variety of facial configurations and patient preferences along with requirements to provide a good seal for varying pressure requirements. Failure to find a good match for a given patient may result in significant side effects, such as eye irritation owing to leak into the eyes, skin pressure sores, noise generation, and inadequate therapy when air leaks are extreme. Pressure sores, mask dislodgement, claustrophobic complaints, air leaks, and sore eyes occur in 20-50% of patients with OSA receiving PAP, and these effects negatively correlate with PAP compliance. Furthermore, several trials point to differences in compliance related to which types of masks are utilized. In a randomized cross-over trial, compliance was 1 hour more per night in patients using NM compared to those using ONM.1 In another, NPs were associated with fewer adverse effects and better subjective sleep quality than NMs.2 Therefore, failure to find an acceptable mask results in lower or non-compliance, and therefore treatment failure. Currently, finding a right mask is performed either using crude templates, or via an iterative process, variably guided by experts in mask fitting. There are no standard certifications or algorithms to guide mask fitting. Given the above, it would be very desirable to find a reliable method to reduce the errors in mask fitting so that the costs, inconvenience, and suffering are all reduced.

Patients will be recruited from Mayo Clinic sleep medicine practices who have completed testing and are initiating PAP therapy for obstructive sleep apnea (OSA), obesity hypoventilation syndrome (OHS), or central sleep apnea syndrome (CSA). Testing will be performed for clinical reasons on patients referred for evaluation for sleep disordered breathing. Testing is at the clinician's discretion and can be either polysomnography or home sleep apnea tests. Patients meeting inclusion and not meeting exclusion criteria will be offered the opportunity to participate in this study. Participating patients will be randomized into one of two groups: the Active group or the Usual Care. A member of the study team register each patient to use the MaskFit AR™ mobile application to take facial measurements and generate mask recommendations. The patient will then be provided with a list recommendations and will be invited to use this list to select their mask from their durable medical equipment company. This is the differentiating part of the study, with the recommendation for a mask being decided by the patient's group assignment. The recommendation for masks may or may not be different from the one written on initial prescription. After the initial visit at the study center, all participants will also be contacted for a follow-up call and will also be asked to complete brief online surveys, one after 40 days and one after 90 days following the first visit.