Evidence map›Paper›PMID 30373687›Full record

Trial reportJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2018

Awake Multimodal Phenotyping for Prediction of Oral Appliance Treatment Outcome.

Kate Sutherland, Andrew S L Chan, Joachim Ngiam, Oyku Dalci, M Ali Darendeliler, Peter A Cistulli

Abstract readClinical Trial
In one paragraph

Trial report in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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  17. A consideration of factors affecting palliative oral appliance effectiveness for obstructive sleep apnea: a scoping review.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2021
    Article
  18. The relationship between specific nasopharyngoscopic features and treatment deterioration with mandibular advancement devices: a prospective study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2020
    Article
  19. Review
  20. Predicting a Successful Response to Oral Appliance Therapy: Advancing Knowledge One Model at a Time.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2018
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Kate SutherlandCentre for Sleep Health and Research, Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, Northern Sydney Local Health District, Sydney, Australia.
Andrew S L ChanCentre for Sleep Health and Research, Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, Northern Sydney Local Health District, Sydney, Australia.
Joachim NgiamCentre for Sleep Health and Research, Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, Northern Sydney Local Health District, Sydney, Australia.
Oyku DalciDiscipline of Orthodontics, Faculty of Dentistry, University of Sydney, Sydney Dental Hospital, Sydney Local Health District, Sydney, Australia.
M Ali DarendelilerDiscipline of Orthodontics, Faculty of Dentistry, University of Sydney, Sydney Dental Hospital, Sydney Local Health District, Sydney, Australia.
Peter A CistulliCentre for Sleep Health and Research, Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, Northern Sydney Local Health District, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

STUDY

objectivesAn 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.

methodsPatients 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 [MIF

resultsCraniofacial 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.

conclusionsMultimodal 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 registrationRegistry: 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.

Indexed as

Orthodontic Appliance DesignPhenotypeWakefulnessAdultAgedFemaleHumansMaleMandibular AdvancementMiddle AgedPolysomnographySleep Apnea, ObstructiveTreatment Outcomemandibular advancementobstructive sleep apneaoral appliance therapysextreatment outcome prediction

Identifiers

PMID30373687
PMCPMC6223562

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.