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| Name | Class |
|---|---|
| SATT Linksium GRENOBLE | UNKNOWN |
| ARTEHIS | UNKNOWN |
| ARCTIC | UNKNOWN |
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This prospective study aims to establish and evaluate a predictive model to diagnose OSA with maxillofacial characteristics 3D acquisition.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| OSA diagnosis with 3D acquisition | Other | OSA diagnosis with 3D acquisition |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 3D acquisition of maxillofacial characteristics | Diagnostic Test | 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity study from different stages of OSA severity | OSA severity stages will be apnea hypopnea index <5, <10, <15 | 1 measure at inclusion |
| Compare diagnosis performances of predictive model and Berlin or NoSAS questionnaires |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Jean-Louis PEPIN | CHU Grenoble Alpes | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Grenoble Alpes University Hospital | Grenoble | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35567881 | Derived | 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. |
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| ID | Term |
|---|---|
| D020181 | Sleep Apnea, Obstructive |
| ID | Term |
|---|---|
| D012891 | Sleep Apnea Syndromes |
| D001049 | Apnea |
| D012120 | Respiration Disorders |
| D012140 | Respiratory Tract Diseases |
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Prospective
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Correlation between the Berlin or NoSAS score and the predictive model results
| 1 measure at inclusion |
| Evaluate performances of the combination (Berlin questionnaire + predictive model) to estimate the OSA risk | Calculate the sensitivity, specificity, predictive positive value and predictive negative value of the combination | 1 measure at inclusion |
| D020919 |
| Sleep Disorders, Intrinsic |
| D020920 | Dyssomnias |
| D012893 | Sleep Wake Disorders |
| D009422 | Nervous System Diseases |