Clinical trial NCT06255613: Validation of Belun Ring Gen3 Deep Learning Algorithms With Subxiphoid Body Sensor
Official title: Belun Ring Gen3 Deep Learning Algorithms With Subxiphoid Body Sensor: Exploring Its Diagnostic Capabilities for Sleep Disordered Breathing With Analysis of Biomarker Dynamics
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Sleep-Disordered Breathing
Sleep Architecture
Arrhythmia
Keywords: Obstructive Sleep Apnea
Wearable
Home Sleep Test
Photoplethysmography
Deep Learning
Predictive AI models
Lead sponsor: Belun Technology Company Limited
Sponsor class: OTHER
Collaborators: name: University Hospitals Cleveland Medical Center
First posted: 2024-02-13
Enrollment: 79
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief summary: 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).


Eligibility criteria: Inclusion Criteria:

* Provision of signed informed consent form.
* Clinically assessed and suspicious for OSA with a STOP-Bang score ≥ 3.

Exclusion Criteria:

* 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.

Primary outcomes:
measure: Performance of diagnosing sleep disordered breathing by BR's Gen3 DL algorithms
timeFrame: 2 years
Secondary outcomes:
measure: Performance of sleep stage classification by BR's Gen3 DL algorithms
timeFrame: 2 years
measure: To evaluate the minimum duration of quality data necessary for OSA diagnosis
timeFrame: 2 years
measure: Accuracy of the BR arrhythmia-detecting algorithm
timeFrame: 3 years
measure: Biomarker relationship analysis
timeFrame: 3 years
measure: Accuracy of the NTUH and NEOMED models
timeFrame: 3 years
Other outcomes:
Locations:
city: Chardon
country: United States
facility: UH Geauga Health Center Services
state: Ohio
city: Cleveland
country: United States
facility: University Hospitals Cleveland Medical Center
state: Ohio
Study officials:
affiliation: University Hospitals Cleveland Medical Center
name: Ambrose A. Chiang, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: University Hospitals Cleveland Medical Center
name: Susheel P. Patil, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: University Hospitals Cleveland Medical Center
name: Kingman P. Strohl, MD
role: PRINCIPAL_INVESTIGATOR
References: