Non-invasive Device for the Screening and Diagnosis of Sleep Apnea Syndrome
Episas
1 other identifier
interventional
280
1 country
1
Brief Summary
This prospective study aims to establish and evaluate a predictive model to diagnose OSA with maxillofacial characteristics 3D acquisition.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Jul 2018
Typical duration for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
July 27, 2018
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 27, 2018
CompletedFirst Submitted
Initial submission to the registry
August 13, 2018
CompletedFirst Posted
Study publicly available on registry
August 15, 2018
CompletedStudy Completion
Last participant's last visit for all outcomes
September 8, 2020
CompletedFebruary 10, 2021
February 1, 2021
Same day
August 13, 2018
February 9, 2021
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Establish and evaluate a predictive model for OSA diagnosis by 3D acquisition of characteristics maxillofacial
apnea hypopnea index will be measured by polysomnography for each patient and compared to a predictive model establish from body mass index and 3D acquisition (cricomental distance...)
1 measure at inclusion
Secondary Outcomes (3)
Sensitivity study from different stages of OSA severity
1 measure at inclusion
Compare diagnosis performances of predictive model and Berlin or NoSAS questionnaires
1 measure at inclusion
Evaluate performances of the combination (Berlin questionnaire + predictive model) to estimate the OSA risk
1 measure at inclusion
Study Arms (1)
OSA diagnosis with 3D acquisition
OTHEROSA diagnosis with 3D acquisition
Interventions
A 3D acquisition of maxillofacial characteristics will be performed for each patient in order to validate a predictive model comparable to data obtained by polysomnography
Eligibility Criteria
You may qualify if:
- BMI \< 35 kg/m²
- caucasian men
- patients from the sleep laboratory (CHU Grenoble Alpes) admitted for a polysomnography
- Patient who has given free and informed consent in writing
You may not qualify if:
- history of maxillofacial surgery
- dental malocclusion
- patient involved in another clinical research study
- patient not affiliated with social security
- patient deprived of liberty or hospitalized without consent
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- University Hospital, Grenoblelead
- SATT Linksium GRENOBLEcollaborator
- ARTEHIScollaborator
- ARCTICcollaborator
Study Sites (1)
Grenoble Alpes University Hospital
Grenoble, France
Related Publications (1)
Monna F, Ben Messaoud R, Navarro N, Baillieul S, Sanchez L, Loiodice C, Tamisier R, Joyeux-Faure M, Pepin JL. Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans. Sleep Med. 2022 Jul;95:76-83. doi: 10.1016/j.sleep.2022.04.019. Epub 2022 Apr 29.
PMID: 35567881DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Jean-Louis PEPIN
CHU Grenoble Alpes
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- NA
- Masking
- NONE
- Purpose
- DIAGNOSTIC
- Intervention Model
- SINGLE GROUP
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
August 13, 2018
First Posted
August 15, 2018
Study Start
July 27, 2018
Primary Completion
July 27, 2018
Study Completion
September 8, 2020
Last Updated
February 10, 2021
Record last verified: 2021-02
Data Sharing
- IPD Sharing
- Will not share