Belun Ring Gen3 Deep Learning Algorithms With Subxiphoid Body Sensor: Exploring Its Diagnostic Capabilities for Sleep Disordered Breathing With Analysis of Biomarker Dynamics
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 79
- 试验地点
- 4
- 主要终点
- Performance of diagnosing sleep disordered breathing by BR's Gen3 DL algorithms
研究概览
简要总结
Hypothesis: BR's Gen3 DL algorithms, combined with its subxiphoid body sensor, can accurately diagnose OSA, categorize its severity, identify REM OSA and supine OSA, and detect central sleep apnea (CSA).
Primary Objective:
To rigorously evaluate the overall performance of the BR with Gen3 DL Algorithms and Subxiphoid Body Sensor in assessing SDB in individuals referred to the sleep labs with clinical suspicion of sleep apnea and a STOP-Bang score > 3, by comparing to the attended in-lab PSG, the gold standard.
Secondary Objectives:
To determine the accuracy of BR sleep stage parameters using the Gen3 DL algorithms by comparing to the in-lab PSG;
To assess the accuracy of the BR arrhythmia detection algorithm;
To assess the impact of CPAP on HRV (both time- and frequency-domain), delta HR, hypoxic burden, and PWADI during split night studies;
To assess if any of the baseline HRV parameters (both time- and frequency-domain), delta heart rate (referred to as Delta HR), hypoxic burden, and pulse wave amplitude drop index (PWADI) or the change of these parameters may predict CPAP compliance;
To evaluate the minimum duration of quality data necessary for BR to achieve OSA diagnosis;
To examine the performance of OSA screening tools using OSA predictive AI models formulated by National Taiwan University Hospital (NTUH) and Northeast Ohio Medical University (NEOMED).
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
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Provision of signed informed consent form.
- •Clinically assessed and suspicious for OSA with a STOP-Bang score ≥ 3.
排除标准
- •Full night PAP titration study.
- •On home O2, noninvasive ventilator, diaphragmatic pacing, or any form of a nerve stimulator.
- •Having atrial fibrillation-flutter, pacemaker/defibrillator, left ventricular assist device (LVAD), or status post cardiac transplantation.
- •Recent hospitalization or recent surgery in the past 30 days.
- •Unstable cardiopulmonary status on the night of the study judged to be unsafe for sleep study by the sleep tech and/or the on-call sleep physician.
- •If a participant did not sleep for at least 4 hours of technically valid sleep based on the Belun Ring method for diagnostic assessments, or a minimum of 3 hours of technically valid sleep during the diagnostic phase of a split-night study, the patient will be excluded from statistical analysis.
结局指标
主要结局
Performance of diagnosing sleep disordered breathing by BR's Gen3 DL algorithms
时间窗: 2 years
To establish mean, standard deviation, and target clinical agreement limit values for the differences in sleep-disordered breathing parameters (including AHI3%, AHI4%, REM AHI 3%, REM AHI4%, Supine AHI3%, Supine AHI4%, and CAI) from the BR against PSG. To calculate the sensitivity and specificity values, along with 95% confidence intervals (CIs), for sleep-disordered breathing parameters from the BR against PSG at PSG cutoffs of 5, 15, and 30 events/h. Pearson correlation and regression analysis will be employed to assess the association between sleep-disordered breathing parameters obtained from the BR and PSG.
次要结局
- Accuracy of the NTUH and NEOMED models(3 years)
- Performance of sleep stage classification by BR's Gen3 DL algorithms(2 years)
- To evaluate the minimum duration of quality data necessary for OSA diagnosis(2 years)
- Accuracy of the BR arrhythmia-detecting algorithm(3 years)
- Biomarker relationship analysis(3 years)