Non-invasive Device for the Screening and Diagnosis of Sleep Apnea Syndrome
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 280
- 试验地点
- 1
- 主要终点
- Establish and evaluate a predictive model for OSA diagnosis by 3D acquisition of characteristics maxillofacial
研究概览
简要总结
This prospective study aims to establish and evaluate a predictive model to diagnose OSA with maxillofacial characteristics 3D acquisition.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 40 Years 至 75 Years（Adult, Older Adult）
- 性别
- Male
- 接受健康志愿者
- 是
入选标准
- •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
排除标准
- •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
结局指标
主要结局
Establish and evaluate a predictive model for OSA diagnosis by 3D acquisition of characteristics maxillofacial
时间窗: 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...)
次要结局
- 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)