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Volume 14 No. 11
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Scientific Investigations

Awake Multimodal Phenotyping for Prediction of Oral Appliance Treatment Outcome

Kate Sutherland, PhD1,2,3; Andrew S.L. Chan, MD, PhD1,2; Joachim Ngiam, BDS(Hons), MSD, MPhil1,2; Oyku Dalci, PhD4; M. Ali Darendeliler, PhD4; Peter A. Cistulli, MD, PhD1,2,3
1Centre for Sleep Health and Research, Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, Northern Sydney Local Health District, Sydney, Australia; 2Sydney Medical School, University of Sydney, Sydney, Australia; 3Charles Perkins Centre, University of Sydney, Sydney, Australia; 4Discipline of Orthodontics, Faculty of Dentistry, University of Sydney, Sydney Dental Hospital, Sydney Local Health District, Sydney, Australia

Study Objectives:

An oral appliance (OA) is a validated treatment for obstructive sleep apnea (OSA). However, therapeutic response is not certain in any individual and is a clinical barrier to implementing this form of therapy. Therefore, accurate and clinically applicable prediction methods are needed. The goal of this study was to derive prediction models based on multiple awake assessments capturing different aspects of the pharyngeal response to mandibular advancement. We hypothesized that a multimodal model would provide robust prediction.

Methods:

Patients with OSA (apnea-hypopnea index [AHI] > 10 events/h) were recruited for treatment with a customized OA (n = 142, 59% male). Participants underwent facial photography (craniofacial structure), spirometry (mid-inspiratory flow at 50% vital capacity [MIF50] and mid-expiratory flow at 50% vital capacity [MEF50] and the ratio MEF50/MIF50) and nasopharyngoscopy (velopharyngeal collapse with Mueller maneuver and mandibular advancement). Treatment response was defined by 3 criteria: (1) AHI < 5 events/h plus ≥ 50% reduction, (2) AHI < 10 events/h plus ≥ 50% reduction, (3) ≥ 50% AHI reduction. Multivariable regression models were used to assess predictive utility of phenotypic assessments compared to clinical characteristics alone (age, sex, obesity, baseline AHI).

Results:

Craniofacial structure and flow-volume loops predicted treatment response. Accuracy of the prediction models (area under the receiver operating characteristic curve) for each criterion were 0.90 (criterion 1), 0.79 (criterion 2), and 0.78 (criterion 3). However, these prediction models including phenotypic assessments did not provide a statistically significant improvement over clinical predictors only.

Conclusions:

Multimodal awake phenotyping does not enhance OA treatment outcome prediction. These office-based, awake assessments have limited utility for robust clinical prediction models. Future work should focus on sleep-related assessments.

Commentary:

A commentary on this article appears in this issue on page 1837.

Clinical Trial Registration:

Registry: Australian New Zealand Clinical Trials Registry, Title: Multimodal phenotyping for the prediction of oral appliance treatment outcome in obstructive sleep apnoea, Identifier: ACTRN12611000409976, URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=336663

Citation:

Sutherland K, Chan AS, Ngiam J, Dalci O, Darendeliler MA, Cistulli PA. Awake multimodal phenotyping for prediction of oral appliance treatment outcome. J Clin Sleep Med. 2018;14(11):1879–1887.




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