Obstructive Sleep Apnea
Conditions
Keywords
Sleep apnea diagnosis, 3D acquisition
Brief summary
This prospective study aims to establish and evaluate a predictive model to diagnose OSA with maxillofacial characteristics 3D acquisition.
Detailed description
Polysomnography is the gold-standard for obstructive sleep apnea (OSA) diagnosis. However, OSA is still undiagnosed. Maxillofacial profile can influence OSA severity. Morphological characteristics can be identified but are not enough measurable and analysable by physicians. 3D acquisition of maxillofacial characteristics with a user-friendly tool, quick and low-priced could be used to obtain a predictive model as an OSA risk indicator. Thus, the aim of this study is to establish and evaluate a predictive model to diagnose OSA with maxillofacial characteristics 3D acquisition.
Interventions
DIAGNOSTIC_TEST3D acquisition of maxillofacial characteristics
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
Sponsors
SATT Linksium GRENOBLE
ARTEHIS
ARCTIC
University Hospital, Grenoble
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Intervention model description
Prospective
Eligibility
Sex/Gender
MALE
Age
40 Years to 75 Years
Healthy volunteers
Yes
Inclusion criteria
* 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
Exclusion criteria
* 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Establish and evaluate a predictive model for OSA diagnosis by 3D acquisition of characteristics maxillofacial | 1 measure at inclusion | 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...) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity study from different stages of OSA severity | 1 measure at inclusion | OSA severity stages will be apnea hypopnea index \<5, \<10, \<15 |
| Compare diagnosis performances of predictive model and Berlin or NoSAS questionnaires | 1 measure at inclusion | Correlation between the Berlin or NoSAS score and the predictive model results |
| Evaluate performances of the combination (Berlin questionnaire + predictive model) to estimate the OSA risk | 1 measure at inclusion | Calculate the sensitivity, specificity, predictive positive value and predictive negative value of the combination |
Countries
France
Outcome results
None listed