Non-invasive Device for the Screening and Diagnosis of Sleep Apnea Syndrome

University Hospital, Grenoble280 enrolled

Overview

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.

Study Type

INTERVENTIONAL

Allocation

Purpose

DIAGNOSTIC

Masking

NONE

Enrollment

280

Conditions

Obstructive Sleep Apnea

Interventions

3D acquisition of maxillofacial characteristicsDIAGNOSTIC_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

Eligibility

Sex: MALEMin age: 40 YearsMax age: 75 YearsHealthy volunteers:

Medical Language ↔ Plain English

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

Locations (1)

Grenoble Alpes University Hospital

Grenoble, France

Outcomes

Primary Outcomes

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...)

Time frame: 1 measure at inclusion

Secondary Outcomes

Sensitivity study from different stages of OSA severity

OSA severity stages will be apnea hypopnea index \<5, \<10, \<15

Time frame: 1 measure at inclusion

Compare diagnosis performances of predictive model and Berlin or NoSAS questionnaires

Correlation between the Berlin or NoSAS score and the predictive model results

Time frame: 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

Time frame: 1 measure at inclusion

Data from ClinicalTrials.gov

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